stevenbucaille/image-classification-models-dataset

model_id stringlengths 7 105	model_card stringlengths 1 130k	model_labels listlengths 2 80k
jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6206 - Accuracy: 0.825 - Brier Loss: 0.2570 - Nll: 0.9939 - F1 Micro: 0.825 - F1 Macro: 0.8166 - Ece: 0.1370 - Aurc: 0.0444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.0511 \| 0.22 \| 0.9320 \| 7.5162 \| 0.22 \| 0.0792 \| 0.3104 \| 0.7615 \| \| No log \| 2.0 \| 14 \| 3.4353 \| 0.35 \| 0.8214 \| 5.1797 \| 0.35 \| 0.2316 \| 0.2589 \| 0.6234 \| \| No log \| 3.0 \| 21 \| 2.6406 \| 0.47 \| 0.6828 \| 2.9202 \| 0.47 \| 0.3725 \| 0.2781 \| 0.3277 \| \| No log \| 4.0 \| 28 \| 2.0027 \| 0.57 \| 0.5596 \| 1.8624 \| 0.57 \| 0.4971 \| 0.2526 \| 0.2124 \| \| No log \| 5.0 \| 35 \| 1.5018 \| 0.65 \| 0.4518 \| 1.7094 \| 0.65 \| 0.6128 \| 0.2242 \| 0.1396 \| \| No log \| 6.0 \| 42 \| 1.3904 \| 0.71 \| 0.4105 \| 1.8765 \| 0.7100 \| 0.7058 \| 0.2202 \| 0.1126 \| \| No log \| 7.0 \| 49 \| 1.1226 \| 0.76 \| 0.3558 \| 1.8024 \| 0.76 \| 0.7029 \| 0.1815 \| 0.0841 \| \| No log \| 8.0 \| 56 \| 1.1810 \| 0.73 \| 0.3716 \| 1.5642 \| 0.7300 \| 0.7027 \| 0.1956 \| 0.0834 \| \| No log \| 9.0 \| 63 \| 1.2131 \| 0.73 \| 0.3811 \| 1.7544 \| 0.7300 \| 0.6774 \| 0.2070 \| 0.0872 \| \| No log \| 10.0 \| 70 \| 1.3986 \| 0.72 \| 0.4043 \| 2.0161 \| 0.72 \| 0.7259 \| 0.2021 \| 0.1098 \| \| No log \| 11.0 \| 77 \| 1.1001 \| 0.765 \| 0.3202 \| 1.9113 \| 0.765 \| 0.7578 \| 0.1859 \| 0.0678 \| \| No log \| 12.0 \| 84 \| 1.0429 \| 0.77 \| 0.3487 \| 1.2955 \| 0.7700 \| 0.7663 \| 0.1910 \| 0.0827 \| \| No log \| 13.0 \| 91 \| 0.9864 \| 0.77 \| 0.3227 \| 1.3721 \| 0.7700 \| 0.7734 \| 0.1692 \| 0.0710 \| \| No log \| 14.0 \| 98 \| 1.0068 \| 0.74 \| 0.3581 \| 1.3362 \| 0.74 \| 0.7271 \| 0.1848 \| 0.0804 \| \| No log \| 15.0 \| 105 \| 0.8635 \| 0.795 \| 0.3009 \| 1.4785 \| 0.795 \| 0.7810 \| 0.1646 \| 0.0538 \| \| No log \| 16.0 \| 112 \| 0.8157 \| 0.81 \| 0.2845 \| 1.2525 \| 0.81 \| 0.7931 \| 0.1545 \| 0.0519 \| \| No log \| 17.0 \| 119 \| 0.8616 \| 0.78 \| 0.3186 \| 1.4230 \| 0.78 \| 0.7705 \| 0.1610 \| 0.0647 \| \| No log \| 18.0 \| 126 \| 0.8034 \| 0.8 \| 0.2784 \| 1.4410 \| 0.8000 \| 0.7811 \| 0.1576 \| 0.0489 \| \| No log \| 19.0 \| 133 \| 0.7601 \| 0.805 \| 0.2697 \| 1.2885 \| 0.805 \| 0.7823 \| 0.1499 \| 0.0494 \| \| No log \| 20.0 \| 140 \| 0.7598 \| 0.82 \| 0.2709 \| 1.3643 \| 0.82 \| 0.8090 \| 0.1542 \| 0.0516 \| \| No log \| 21.0 \| 147 \| 0.8221 \| 0.79 \| 0.2905 \| 1.4031 \| 0.79 \| 0.7640 \| 0.1612 \| 0.0585 \| \| No log \| 22.0 \| 154 \| 0.7271 \| 0.825 \| 0.2599 \| 1.0950 \| 0.825 \| 0.8147 \| 0.1381 \| 0.0454 \| \| No log \| 23.0 \| 161 \| 0.7556 \| 0.795 \| 0.2891 \| 1.1111 \| 0.795 \| 0.7822 \| 0.1413 \| 0.0558 \| \| No log \| 24.0 \| 168 \| 0.7197 \| 0.81 \| 0.2759 \| 1.1361 \| 0.81 \| 0.7905 \| 0.1617 \| 0.0500 \| \| No log \| 25.0 \| 175 \| 0.7192 \| 0.83 \| 0.2620 \| 1.3395 \| 0.83 \| 0.8155 \| 0.1459 \| 0.0433 \| \| No log \| 26.0 \| 182 \| 0.7347 \| 0.805 \| 0.2821 \| 1.1396 \| 0.805 \| 0.7868 \| 0.1512 \| 0.0541 \| \| No log \| 27.0 \| 189 \| 0.7402 \| 0.815 \| 0.2805 \| 1.3562 \| 0.815 \| 0.7928 \| 0.1489 \| 0.0519 \| \| No log \| 28.0 \| 196 \| 0.6986 \| 0.815 \| 0.2562 \| 1.1454 \| 0.815 \| 0.7944 \| 0.1467 \| 0.0443 \| \| No log \| 29.0 \| 203 \| 0.7148 \| 0.81 \| 0.2718 \| 1.1404 \| 0.81 \| 0.7944 \| 0.1440 \| 0.0513 \| \| No log \| 30.0 \| 210 \| 0.7041 \| 0.81 \| 0.2796 \| 1.3773 \| 0.81 \| 0.7998 \| 0.1484 \| 0.0494 \| \| No log \| 31.0 \| 217 \| 0.7428 \| 0.815 \| 0.2823 \| 1.1146 \| 0.815 \| 0.7967 \| 0.1626 \| 0.0542 \| \| No log \| 32.0 \| 224 \| 0.6941 \| 0.82 \| 0.2682 \| 1.1921 \| 0.82 \| 0.8098 \| 0.1427 \| 0.0478 \| \| No log \| 33.0 \| 231 \| 0.7170 \| 0.81 \| 0.2794 \| 1.2244 \| 0.81 \| 0.7875 \| 0.1407 \| 0.0511 \| \| No log \| 34.0 \| 238 \| 0.7024 \| 0.815 \| 0.2805 \| 1.0423 \| 0.815 \| 0.8043 \| 0.1560 \| 0.0512 \| \| No log \| 35.0 \| 245 \| 0.7299 \| 0.81 \| 0.2710 \| 1.1835 \| 0.81 \| 0.7964 \| 0.1475 \| 0.0530 \| \| No log \| 36.0 \| 252 \| 0.6488 \| 0.83 \| 0.2500 \| 1.1662 \| 0.83 \| 0.8117 \| 0.1315 \| 0.0431 \| \| No log \| 37.0 \| 259 \| 0.6877 \| 0.815 \| 0.2751 \| 1.0878 \| 0.815 \| 0.7973 \| 0.1381 \| 0.0489 \| \| No log \| 38.0 \| 266 \| 0.7019 \| 0.84 \| 0.2620 \| 1.2709 \| 0.8400 \| 0.8282 \| 0.1607 \| 0.0498 \| \| No log \| 39.0 \| 273 \| 0.6687 \| 0.81 \| 0.2680 \| 1.3004 \| 0.81 \| 0.7959 \| 0.1346 \| 0.0465 \| \| No log \| 40.0 \| 280 \| 0.6813 \| 0.81 \| 0.2809 \| 1.0539 \| 0.81 \| 0.7929 \| 0.1628 \| 0.0500 \| \| No log \| 41.0 \| 287 \| 0.6525 \| 0.83 \| 0.2493 \| 1.1496 \| 0.83 \| 0.8176 \| 0.1413 \| 0.0437 \| \| No log \| 42.0 \| 294 \| 0.6526 \| 0.835 \| 0.2547 \| 1.2429 \| 0.835 \| 0.8253 \| 0.1420 \| 0.0450 \| \| No log \| 43.0 \| 301 \| 0.6696 \| 0.82 \| 0.2717 \| 1.0446 \| 0.82 \| 0.8118 \| 0.1486 \| 0.0501 \| \| No log \| 44.0 \| 308 \| 0.6555 \| 0.83 \| 0.2626 \| 0.9948 \| 0.83 \| 0.8214 \| 0.1366 \| 0.0461 \| \| No log \| 45.0 \| 315 \| 0.6380 \| 0.82 \| 0.2600 \| 1.2151 \| 0.82 \| 0.8026 \| 0.1263 \| 0.0428 \| \| No log \| 46.0 \| 322 \| 0.6356 \| 0.82 \| 0.2571 \| 1.0923 \| 0.82 \| 0.8114 \| 0.1443 \| 0.0449 \| \| No log \| 47.0 \| 329 \| 0.6444 \| 0.815 \| 0.2638 \| 1.0657 \| 0.815 \| 0.7980 \| 0.1503 \| 0.0476 \| \| No log \| 48.0 \| 336 \| 0.6337 \| 0.82 \| 0.2676 \| 1.0650 \| 0.82 \| 0.8077 \| 0.1370 \| 0.0442 \| \| No log \| 49.0 \| 343 \| 0.6271 \| 0.84 \| 0.2541 \| 1.1500 \| 0.8400 \| 0.8230 \| 0.1365 \| 0.0422 \| \| No log \| 50.0 \| 350 \| 0.6284 \| 0.81 \| 0.2588 \| 1.2703 \| 0.81 \| 0.7964 \| 0.1411 \| 0.0425 \| \| No log \| 51.0 \| 357 \| 0.6507 \| 0.82 \| 0.2612 \| 1.1306 \| 0.82 \| 0.7996 \| 0.1558 \| 0.0460 \| \| No log \| 52.0 \| 364 \| 0.6329 \| 0.825 \| 0.2602 \| 1.2060 \| 0.825 \| 0.8146 \| 0.1296 \| 0.0439 \| \| No log \| 53.0 \| 371 \| 0.6342 \| 0.825 \| 0.2574 \| 1.0132 \| 0.825 \| 0.8158 \| 0.1467 \| 0.0434 \| \| No log \| 54.0 \| 378 \| 0.6486 \| 0.82 \| 0.2633 \| 1.1662 \| 0.82 \| 0.8060 \| 0.1445 \| 0.0466 \| \| No log \| 55.0 \| 385 \| 0.6245 \| 0.825 \| 0.2588 \| 1.1358 \| 0.825 \| 0.8088 \| 0.1428 \| 0.0429 \| \| No log \| 56.0 \| 392 \| 0.6303 \| 0.815 \| 0.2616 \| 0.9843 \| 0.815 \| 0.8013 \| 0.1447 \| 0.0458 \| \| No log \| 57.0 \| 399 \| 0.6196 \| 0.82 \| 0.2545 \| 1.1936 \| 0.82 \| 0.8076 \| 0.1516 \| 0.0438 \| \| No log \| 58.0 \| 406 \| 0.6241 \| 0.82 \| 0.2620 \| 1.0557 \| 0.82 \| 0.8100 \| 0.1423 \| 0.0450 \| \| No log \| 59.0 \| 413 \| 0.6278 \| 0.82 \| 0.2579 \| 1.0777 \| 0.82 \| 0.8076 \| 0.1382 \| 0.0451 \| \| No log \| 60.0 \| 420 \| 0.6385 \| 0.81 \| 0.2651 \| 0.9962 \| 0.81 \| 0.7910 \| 0.1565 \| 0.0467 \| \| No log \| 61.0 \| 427 \| 0.6328 \| 0.82 \| 0.2619 \| 0.9968 \| 0.82 \| 0.8103 \| 0.1299 \| 0.0469 \| \| No log \| 62.0 \| 434 \| 0.6195 \| 0.82 \| 0.2571 \| 0.9997 \| 0.82 \| 0.8062 \| 0.1471 \| 0.0438 \| \| No log \| 63.0 \| 441 \| 0.6150 \| 0.825 \| 0.2560 \| 1.0061 \| 0.825 \| 0.8166 \| 0.1498 \| 0.0430 \| \| No log \| 64.0 \| 448 \| 0.6201 \| 0.825 \| 0.2574 \| 1.0592 \| 0.825 \| 0.8166 \| 0.1369 \| 0.0442 \| \| No log \| 65.0 \| 455 \| 0.6281 \| 0.815 \| 0.2601 \| 0.9990 \| 0.815 \| 0.8013 \| 0.1449 \| 0.0459 \| \| No log \| 66.0 \| 462 \| 0.6232 \| 0.825 \| 0.2538 \| 1.0657 \| 0.825 \| 0.8166 \| 0.1341 \| 0.0442 \| \| No log \| 67.0 \| 469 \| 0.6242 \| 0.82 \| 0.2567 \| 1.0622 \| 0.82 \| 0.8100 \| 0.1432 \| 0.0445 \| \| No log \| 68.0 \| 476 \| 0.6213 \| 0.82 \| 0.2598 \| 1.0666 \| 0.82 \| 0.8100 \| 0.1517 \| 0.0447 \| \| No log \| 69.0 \| 483 \| 0.6268 \| 0.82 \| 0.2577 \| 1.0106 \| 0.82 \| 0.8100 \| 0.1365 \| 0.0455 \| \| No log \| 70.0 \| 490 \| 0.6252 \| 0.82 \| 0.2579 \| 0.9979 \| 0.82 \| 0.8100 \| 0.1395 \| 0.0451 \| \| No log \| 71.0 \| 497 \| 0.6251 \| 0.82 \| 0.2589 \| 1.0606 \| 0.82 \| 0.8100 \| 0.1485 \| 0.0448 \| \| 0.3286 \| 72.0 \| 504 \| 0.6212 \| 0.825 \| 0.2571 \| 1.0034 \| 0.825 \| 0.8166 \| 0.1448 \| 0.0443 \| \| 0.3286 \| 73.0 \| 511 \| 0.6212 \| 0.82 \| 0.2584 \| 0.9940 \| 0.82 \| 0.8100 \| 0.1499 \| 0.0444 \| \| 0.3286 \| 74.0 \| 518 \| 0.6214 \| 0.82 \| 0.2576 \| 0.9914 \| 0.82 \| 0.8100 \| 0.1411 \| 0.0448 \| \| 0.3286 \| 75.0 \| 525 \| 0.6233 \| 0.82 \| 0.2580 \| 0.9966 \| 0.82 \| 0.8100 \| 0.1592 \| 0.0450 \| \| 0.3286 \| 76.0 \| 532 \| 0.6214 \| 0.82 \| 0.2568 \| 0.9952 \| 0.82 \| 0.8100 \| 0.1404 \| 0.0448 \| \| 0.3286 \| 77.0 \| 539 \| 0.6217 \| 0.825 \| 0.2575 \| 0.9951 \| 0.825 \| 0.8166 \| 0.1361 \| 0.0445 \| \| 0.3286 \| 78.0 \| 546 \| 0.6220 \| 0.82 \| 0.2569 \| 0.9964 \| 0.82 \| 0.8100 \| 0.1385 \| 0.0450 \| \| 0.3286 \| 79.0 \| 553 \| 0.6225 \| 0.82 \| 0.2581 \| 0.9950 \| 0.82 \| 0.8100 \| 0.1485 \| 0.0450 \| \| 0.3286 \| 80.0 \| 560 \| 0.6213 \| 0.82 \| 0.2578 \| 0.9912 \| 0.82 \| 0.8100 \| 0.1381 \| 0.0446 \| \| 0.3286 \| 81.0 \| 567 \| 0.6209 \| 0.82 \| 0.2572 \| 0.9948 \| 0.82 \| 0.8100 \| 0.1415 \| 0.0447 \| \| 0.3286 \| 82.0 \| 574 \| 0.6213 \| 0.82 \| 0.2578 \| 0.9958 \| 0.82 \| 0.8100 \| 0.1422 \| 0.0449 \| \| 0.3286 \| 83.0 \| 581 \| 0.6220 \| 0.82 \| 0.2579 \| 0.9947 \| 0.82 \| 0.8100 \| 0.1553 \| 0.0448 \| \| 0.3286 \| 84.0 \| 588 \| 0.6212 \| 0.82 \| 0.2574 \| 0.9915 \| 0.82 \| 0.8100 \| 0.1418 \| 0.0447 \| \| 0.3286 \| 85.0 \| 595 \| 0.6220 \| 0.82 \| 0.2579 \| 0.9937 \| 0.82 \| 0.8100 \| 0.1628 \| 0.0450 \| \| 0.3286 \| 86.0 \| 602 \| 0.6207 \| 0.82 \| 0.2572 \| 0.9945 \| 0.82 \| 0.8100 \| 0.1412 \| 0.0447 \| \| 0.3286 \| 87.0 \| 609 \| 0.6212 \| 0.82 \| 0.2573 \| 0.9940 \| 0.82 \| 0.8100 \| 0.1414 \| 0.0447 \| \| 0.3286 \| 88.0 \| 616 \| 0.6201 \| 0.825 \| 0.2570 \| 0.9943 \| 0.825 \| 0.8166 \| 0.1366 \| 0.0443 \| \| 0.3286 \| 89.0 \| 623 \| 0.6210 \| 0.82 \| 0.2573 \| 0.9944 \| 0.82 \| 0.8100 \| 0.1414 \| 0.0448 \| \| 0.3286 \| 90.0 \| 630 \| 0.6207 \| 0.82 \| 0.2572 \| 0.9942 \| 0.82 \| 0.8100 \| 0.1414 \| 0.0447 \| \| 0.3286 \| 91.0 \| 637 \| 0.6210 \| 0.82 \| 0.2572 \| 0.9952 \| 0.82 \| 0.8100 \| 0.1415 \| 0.0447 \| \| 0.3286 \| 92.0 \| 644 \| 0.6205 \| 0.82 \| 0.2572 \| 0.9939 \| 0.82 \| 0.8100 \| 0.1414 \| 0.0447 \| \| 0.3286 \| 93.0 \| 651 \| 0.6207 \| 0.825 \| 0.2570 \| 0.9938 \| 0.825 \| 0.8166 \| 0.1373 \| 0.0445 \| \| 0.3286 \| 94.0 \| 658 \| 0.6206 \| 0.82 \| 0.2572 \| 0.9945 \| 0.82 \| 0.8100 \| 0.1414 \| 0.0447 \| \| 0.3286 \| 95.0 \| 665 \| 0.6203 \| 0.825 \| 0.2568 \| 0.9951 \| 0.825 \| 0.8166 \| 0.1370 \| 0.0444 \| \| 0.3286 \| 96.0 \| 672 \| 0.6205 \| 0.82 \| 0.2571 \| 0.9942 \| 0.82 \| 0.8100 \| 0.1413 \| 0.0448 \| \| 0.3286 \| 97.0 \| 679 \| 0.6206 \| 0.825 \| 0.2570 \| 0.9943 \| 0.825 \| 0.8166 \| 0.1370 \| 0.0445 \| \| 0.3286 \| 98.0 \| 686 \| 0.6206 \| 0.825 \| 0.2570 \| 0.9942 \| 0.825 \| 0.8166 \| 0.1370 \| 0.0445 \| \| 0.3286 \| 99.0 \| 693 \| 0.6206 \| 0.825 \| 0.2570 \| 0.9940 \| 0.825 \| 0.8166 \| 0.1370 \| 0.0445 \| \| 0.3286 \| 100.0 \| 700 \| 0.6206 \| 0.825 \| 0.2570 \| 0.9939 \| 0.825 \| 0.8166 \| 0.1370 \| 0.0444 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_MSE	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0108 - Accuracy: 0.815 - Brier Loss: 0.2593 - Nll: 1.1011 - F1 Micro: 0.815 - F1 Macro: 0.8014 - Ece: 0.1462 - Aurc: 0.0442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 7.1562 \| 0.195 \| 0.9158 \| 7.6908 \| 0.195 \| 0.1043 \| 0.2991 \| 0.7927 \| \| No log \| 2.0 \| 14 \| 6.3485 \| 0.245 \| 0.8600 \| 4.2261 \| 0.245 \| 0.1794 \| 0.2960 \| 0.7445 \| \| No log \| 3.0 \| 21 \| 5.2488 \| 0.41 \| 0.7090 \| 3.6293 \| 0.41 \| 0.3494 \| 0.2826 \| 0.3786 \| \| No log \| 4.0 \| 28 \| 4.0484 \| 0.565 \| 0.5698 \| 1.7703 \| 0.565 \| 0.5209 \| 0.2622 \| 0.2372 \| \| No log \| 5.0 \| 35 \| 3.0368 \| 0.655 \| 0.4710 \| 1.5921 \| 0.655 \| 0.6368 \| 0.2222 \| 0.1598 \| \| No log \| 6.0 \| 42 \| 2.6191 \| 0.695 \| 0.4219 \| 1.6919 \| 0.695 \| 0.6535 \| 0.2041 \| 0.1200 \| \| No log \| 7.0 \| 49 \| 2.0941 \| 0.725 \| 0.3913 \| 1.3852 \| 0.7250 \| 0.6844 \| 0.2046 \| 0.0966 \| \| No log \| 8.0 \| 56 \| 2.0668 \| 0.725 \| 0.4119 \| 1.3829 \| 0.7250 \| 0.6811 \| 0.1890 \| 0.1045 \| \| No log \| 9.0 \| 63 \| 1.7456 \| 0.79 \| 0.3138 \| 1.5258 \| 0.79 \| 0.7539 \| 0.1521 \| 0.0651 \| \| No log \| 10.0 \| 70 \| 1.5815 \| 0.77 \| 0.3391 \| 1.2461 \| 0.7700 \| 0.7323 \| 0.1593 \| 0.0725 \| \| No log \| 11.0 \| 77 \| 1.5720 \| 0.785 \| 0.2895 \| 1.3282 \| 0.785 \| 0.7659 \| 0.1408 \| 0.0522 \| \| No log \| 12.0 \| 84 \| 1.8886 \| 0.78 \| 0.3692 \| 1.6238 \| 0.78 \| 0.7717 \| 0.2015 \| 0.0917 \| \| No log \| 13.0 \| 91 \| 1.6164 \| 0.785 \| 0.2918 \| 1.6303 \| 0.785 \| 0.7925 \| 0.1545 \| 0.0564 \| \| No log \| 14.0 \| 98 \| 1.4318 \| 0.785 \| 0.3220 \| 1.3070 \| 0.785 \| 0.7606 \| 0.1430 \| 0.0639 \| \| No log \| 15.0 \| 105 \| 1.2774 \| 0.81 \| 0.2807 \| 1.2877 \| 0.81 \| 0.7939 \| 0.1532 \| 0.0595 \| \| No log \| 16.0 \| 112 \| 1.3797 \| 0.8 \| 0.2993 \| 1.2409 \| 0.8000 \| 0.7759 \| 0.1565 \| 0.0700 \| \| No log \| 17.0 \| 119 \| 1.3629 \| 0.795 \| 0.3091 \| 1.1781 \| 0.795 \| 0.7670 \| 0.1712 \| 0.0567 \| \| No log \| 18.0 \| 126 \| 1.5101 \| 0.8 \| 0.3192 \| 1.3586 \| 0.8000 \| 0.7878 \| 0.1919 \| 0.0665 \| \| No log \| 19.0 \| 133 \| 1.3897 \| 0.805 \| 0.2857 \| 1.4983 \| 0.805 \| 0.7851 \| 0.1356 \| 0.0516 \| \| No log \| 20.0 \| 140 \| 1.3821 \| 0.795 \| 0.3204 \| 1.0916 \| 0.795 \| 0.7745 \| 0.1678 \| 0.0651 \| \| No log \| 21.0 \| 147 \| 1.2852 \| 0.83 \| 0.2621 \| 1.5182 \| 0.83 \| 0.8246 \| 0.1483 \| 0.0486 \| \| No log \| 22.0 \| 154 \| 1.2080 \| 0.815 \| 0.2744 \| 1.1921 \| 0.815 \| 0.7957 \| 0.1319 \| 0.0500 \| \| No log \| 23.0 \| 161 \| 1.4016 \| 0.805 \| 0.3165 \| 1.3364 \| 0.805 \| 0.7844 \| 0.1534 \| 0.0624 \| \| No log \| 24.0 \| 168 \| 1.2883 \| 0.825 \| 0.2592 \| 1.4946 \| 0.825 \| 0.8119 \| 0.1549 \| 0.0481 \| \| No log \| 25.0 \| 175 \| 1.1715 \| 0.815 \| 0.2676 \| 1.3363 \| 0.815 \| 0.8054 \| 0.1464 \| 0.0494 \| \| No log \| 26.0 \| 182 \| 1.1844 \| 0.825 \| 0.2585 \| 1.4938 \| 0.825 \| 0.8045 \| 0.1572 \| 0.0469 \| \| No log \| 27.0 \| 189 \| 1.1739 \| 0.81 \| 0.2959 \| 1.0692 \| 0.81 \| 0.7909 \| 0.1625 \| 0.0550 \| \| No log \| 28.0 \| 196 \| 1.1944 \| 0.815 \| 0.2891 \| 1.1811 \| 0.815 \| 0.7971 \| 0.1430 \| 0.0572 \| \| No log \| 29.0 \| 203 \| 1.2115 \| 0.83 \| 0.2597 \| 1.4809 \| 0.83 \| 0.8101 \| 0.1289 \| 0.0469 \| \| No log \| 30.0 \| 210 \| 1.1622 \| 0.81 \| 0.2825 \| 1.1104 \| 0.81 \| 0.7931 \| 0.1463 \| 0.0511 \| \| No log \| 31.0 \| 217 \| 1.2591 \| 0.8 \| 0.3096 \| 1.2310 \| 0.8000 \| 0.7789 \| 0.1719 \| 0.0591 \| \| No log \| 32.0 \| 224 \| 1.1752 \| 0.82 \| 0.2687 \| 1.4091 \| 0.82 \| 0.7959 \| 0.1581 \| 0.0504 \| \| No log \| 33.0 \| 231 \| 1.1114 \| 0.815 \| 0.2719 \| 1.0945 \| 0.815 \| 0.7885 \| 0.1492 \| 0.0485 \| \| No log \| 34.0 \| 238 \| 1.1105 \| 0.815 \| 0.2727 \| 1.1239 \| 0.815 \| 0.7962 \| 0.1300 \| 0.0479 \| \| No log \| 35.0 \| 245 \| 1.1662 \| 0.825 \| 0.2748 \| 1.3396 \| 0.825 \| 0.8100 \| 0.1571 \| 0.0554 \| \| No log \| 36.0 \| 252 \| 1.1023 \| 0.815 \| 0.2757 \| 1.1805 \| 0.815 \| 0.8031 \| 0.1428 \| 0.0504 \| \| No log \| 37.0 \| 259 \| 1.1060 \| 0.84 \| 0.2604 \| 1.3305 \| 0.8400 \| 0.8319 \| 0.1596 \| 0.0487 \| \| No log \| 38.0 \| 266 \| 1.1123 \| 0.81 \| 0.2682 \| 1.1122 \| 0.81 \| 0.7922 \| 0.1310 \| 0.0482 \| \| No log \| 39.0 \| 273 \| 1.0820 \| 0.815 \| 0.2669 \| 1.1629 \| 0.815 \| 0.7955 \| 0.1479 \| 0.0490 \| \| No log \| 40.0 \| 280 \| 1.0972 \| 0.805 \| 0.2784 \| 1.2442 \| 0.805 \| 0.7858 \| 0.1576 \| 0.0483 \| \| No log \| 41.0 \| 287 \| 1.0845 \| 0.83 \| 0.2705 \| 1.1180 \| 0.83 \| 0.8221 \| 0.1504 \| 0.0468 \| \| No log \| 42.0 \| 294 \| 1.0769 \| 0.82 \| 0.2602 \| 1.1173 \| 0.82 \| 0.8066 \| 0.1458 \| 0.0451 \| \| No log \| 43.0 \| 301 \| 1.1366 \| 0.81 \| 0.2939 \| 1.0722 \| 0.81 \| 0.7958 \| 0.1532 \| 0.0526 \| \| No log \| 44.0 \| 308 \| 1.0716 \| 0.82 \| 0.2635 \| 1.1839 \| 0.82 \| 0.8043 \| 0.1403 \| 0.0451 \| \| No log \| 45.0 \| 315 \| 1.0865 \| 0.81 \| 0.2770 \| 1.3595 \| 0.81 \| 0.7929 \| 0.1501 \| 0.0528 \| \| No log \| 46.0 \| 322 \| 1.0768 \| 0.82 \| 0.2638 \| 1.1161 \| 0.82 \| 0.8067 \| 0.1462 \| 0.0457 \| \| No log \| 47.0 \| 329 \| 1.0644 \| 0.825 \| 0.2552 \| 1.2086 \| 0.825 \| 0.8098 \| 0.1579 \| 0.0439 \| \| No log \| 48.0 \| 336 \| 1.0511 \| 0.815 \| 0.2656 \| 1.1019 \| 0.815 \| 0.8014 \| 0.1518 \| 0.0471 \| \| No log \| 49.0 \| 343 \| 1.0517 \| 0.82 \| 0.2717 \| 1.0881 \| 0.82 \| 0.8044 \| 0.1559 \| 0.0473 \| \| No log \| 50.0 \| 350 \| 1.0824 \| 0.81 \| 0.2813 \| 1.1022 \| 0.81 \| 0.7968 \| 0.1538 \| 0.0505 \| \| No log \| 51.0 \| 357 \| 1.1439 \| 0.835 \| 0.2634 \| 1.3483 \| 0.835 \| 0.8206 \| 0.1471 \| 0.0496 \| \| No log \| 52.0 \| 364 \| 1.0444 \| 0.83 \| 0.2500 \| 1.0999 \| 0.83 \| 0.8156 \| 0.1310 \| 0.0423 \| \| No log \| 53.0 \| 371 \| 1.0426 \| 0.825 \| 0.2644 \| 1.1112 \| 0.825 \| 0.8053 \| 0.1295 \| 0.0474 \| \| No log \| 54.0 \| 378 \| 1.0341 \| 0.825 \| 0.2635 \| 1.1053 \| 0.825 \| 0.8092 \| 0.1467 \| 0.0465 \| \| No log \| 55.0 \| 385 \| 1.0900 \| 0.815 \| 0.2762 \| 1.1021 \| 0.815 \| 0.7990 \| 0.1439 \| 0.0480 \| \| No log \| 56.0 \| 392 \| 1.0423 \| 0.845 \| 0.2517 \| 1.2594 \| 0.845 \| 0.8444 \| 0.1497 \| 0.0428 \| \| No log \| 57.0 \| 399 \| 1.0246 \| 0.825 \| 0.2634 \| 1.0927 \| 0.825 \| 0.8130 \| 0.1260 \| 0.0454 \| \| No log \| 58.0 \| 406 \| 1.0365 \| 0.835 \| 0.2649 \| 1.0825 \| 0.835 \| 0.8232 \| 0.1291 \| 0.0448 \| \| No log \| 59.0 \| 413 \| 1.0394 \| 0.82 \| 0.2668 \| 1.0968 \| 0.82 \| 0.8045 \| 0.1458 \| 0.0460 \| \| No log \| 60.0 \| 420 \| 1.0261 \| 0.815 \| 0.2720 \| 1.0883 \| 0.815 \| 0.8011 \| 0.1409 \| 0.0472 \| \| No log \| 61.0 \| 427 \| 1.0503 \| 0.83 \| 0.2543 \| 1.3230 \| 0.83 \| 0.8132 \| 0.1378 \| 0.0455 \| \| No log \| 62.0 \| 434 \| 1.0400 \| 0.82 \| 0.2637 \| 1.0958 \| 0.82 \| 0.8043 \| 0.1397 \| 0.0456 \| \| No log \| 63.0 \| 441 \| 1.0338 \| 0.82 \| 0.2629 \| 1.0960 \| 0.82 \| 0.8042 \| 0.1338 \| 0.0435 \| \| No log \| 64.0 \| 448 \| 1.0373 \| 0.84 \| 0.2508 \| 1.2817 \| 0.8400 \| 0.8260 \| 0.1325 \| 0.0433 \| \| No log \| 65.0 \| 455 \| 1.0266 \| 0.83 \| 0.2663 \| 1.1057 \| 0.83 \| 0.8163 \| 0.1383 \| 0.0460 \| \| No log \| 66.0 \| 462 \| 1.0303 \| 0.825 \| 0.2549 \| 1.1906 \| 0.825 \| 0.8098 \| 0.1399 \| 0.0450 \| \| No log \| 67.0 \| 469 \| 1.0224 \| 0.82 \| 0.2668 \| 1.0920 \| 0.82 \| 0.8042 \| 0.1252 \| 0.0433 \| \| No log \| 68.0 \| 476 \| 1.0274 \| 0.845 \| 0.2526 \| 1.1948 \| 0.845 \| 0.8368 \| 0.1423 \| 0.0442 \| \| No log \| 69.0 \| 483 \| 1.0145 \| 0.82 \| 0.2647 \| 1.0884 \| 0.82 \| 0.8070 \| 0.1345 \| 0.0449 \| \| No log \| 70.0 \| 490 \| 1.0194 \| 0.815 \| 0.2606 \| 1.1076 \| 0.815 \| 0.8014 \| 0.1529 \| 0.0446 \| \| No log \| 71.0 \| 497 \| 1.0153 \| 0.825 \| 0.2572 \| 1.2484 \| 0.825 \| 0.8142 \| 0.1425 \| 0.0445 \| \| 0.6377 \| 72.0 \| 504 \| 1.0265 \| 0.815 \| 0.2607 \| 1.1109 \| 0.815 \| 0.8039 \| 0.1457 \| 0.0445 \| \| 0.6377 \| 73.0 \| 511 \| 1.0081 \| 0.82 \| 0.2567 \| 1.1031 \| 0.82 \| 0.8040 \| 0.1321 \| 0.0440 \| \| 0.6377 \| 74.0 \| 518 \| 1.0135 \| 0.825 \| 0.2600 \| 1.1036 \| 0.825 \| 0.8074 \| 0.1477 \| 0.0450 \| \| 0.6377 \| 75.0 \| 525 \| 1.0053 \| 0.82 \| 0.2616 \| 1.1012 \| 0.82 \| 0.8044 \| 0.1542 \| 0.0442 \| \| 0.6377 \| 76.0 \| 532 \| 1.0187 \| 0.82 \| 0.2598 \| 1.1115 \| 0.82 \| 0.8069 \| 0.1566 \| 0.0445 \| \| 0.6377 \| 77.0 \| 539 \| 1.0127 \| 0.82 \| 0.2610 \| 1.1024 \| 0.82 \| 0.8097 \| 0.1489 \| 0.0443 \| \| 0.6377 \| 78.0 \| 546 \| 1.0079 \| 0.82 \| 0.2581 \| 1.1034 \| 0.82 \| 0.8069 \| 0.1463 \| 0.0434 \| \| 0.6377 \| 79.0 \| 553 \| 1.0097 \| 0.815 \| 0.2592 \| 1.1030 \| 0.815 \| 0.8014 \| 0.1478 \| 0.0438 \| \| 0.6377 \| 80.0 \| 560 \| 1.0131 \| 0.835 \| 0.2556 \| 1.1048 \| 0.835 \| 0.8281 \| 0.1508 \| 0.0441 \| \| 0.6377 \| 81.0 \| 567 \| 1.0183 \| 0.82 \| 0.2602 \| 1.1057 \| 0.82 \| 0.8044 \| 0.1417 \| 0.0446 \| \| 0.6377 \| 82.0 \| 574 \| 1.0190 \| 0.815 \| 0.2665 \| 1.0966 \| 0.815 \| 0.7987 \| 0.1370 \| 0.0462 \| \| 0.6377 \| 83.0 \| 581 \| 1.0117 \| 0.815 \| 0.2619 \| 1.0974 \| 0.815 \| 0.8014 \| 0.1614 \| 0.0442 \| \| 0.6377 \| 84.0 \| 588 \| 1.0099 \| 0.82 \| 0.2557 \| 1.1070 \| 0.82 \| 0.8044 \| 0.1327 \| 0.0436 \| \| 0.6377 \| 85.0 \| 595 \| 1.0088 \| 0.82 \| 0.2569 \| 1.1037 \| 0.82 \| 0.8044 \| 0.1446 \| 0.0437 \| \| 0.6377 \| 86.0 \| 602 \| 1.0110 \| 0.82 \| 0.2596 \| 1.0945 \| 0.82 \| 0.8043 \| 0.1505 \| 0.0442 \| \| 0.6377 \| 87.0 \| 609 \| 1.0151 \| 0.815 \| 0.2606 \| 1.1046 \| 0.815 \| 0.8014 \| 0.1416 \| 0.0451 \| \| 0.6377 \| 88.0 \| 616 \| 1.0101 \| 0.815 \| 0.2587 \| 1.1025 \| 0.815 \| 0.8014 \| 0.1435 \| 0.0440 \| \| 0.6377 \| 89.0 \| 623 \| 1.0106 \| 0.815 \| 0.2613 \| 1.0976 \| 0.815 \| 0.8014 \| 0.1489 \| 0.0443 \| \| 0.6377 \| 90.0 \| 630 \| 1.0097 \| 0.815 \| 0.2590 \| 1.0993 \| 0.815 \| 0.8014 \| 0.1490 \| 0.0439 \| \| 0.6377 \| 91.0 \| 637 \| 1.0098 \| 0.815 \| 0.2593 \| 1.1024 \| 0.815 \| 0.8014 \| 0.1510 \| 0.0440 \| \| 0.6377 \| 92.0 \| 644 \| 1.0116 \| 0.815 \| 0.2600 \| 1.1004 \| 0.815 \| 0.8014 \| 0.1465 \| 0.0442 \| \| 0.6377 \| 93.0 \| 651 \| 1.0107 \| 0.815 \| 0.2596 \| 1.1005 \| 0.815 \| 0.8014 \| 0.1548 \| 0.0442 \| \| 0.6377 \| 94.0 \| 658 \| 1.0110 \| 0.815 \| 0.2599 \| 1.0993 \| 0.815 \| 0.8014 \| 0.1463 \| 0.0440 \| \| 0.6377 \| 95.0 \| 665 \| 1.0106 \| 0.815 \| 0.2593 \| 1.1011 \| 0.815 \| 0.8014 \| 0.1409 \| 0.0441 \| \| 0.6377 \| 96.0 \| 672 \| 1.0106 \| 0.815 \| 0.2596 \| 1.1011 \| 0.815 \| 0.8014 \| 0.1496 \| 0.0442 \| \| 0.6377 \| 97.0 \| 679 \| 1.0109 \| 0.815 \| 0.2595 \| 1.1007 \| 0.815 \| 0.8014 \| 0.1462 \| 0.0442 \| \| 0.6377 \| 98.0 \| 686 \| 1.0107 \| 0.815 \| 0.2593 \| 1.1013 \| 0.815 \| 0.8014 \| 0.1409 \| 0.0441 \| \| 0.6377 \| 99.0 \| 693 \| 1.0107 \| 0.815 \| 0.2594 \| 1.1009 \| 0.815 \| 0.8014 \| 0.1462 \| 0.0441 \| \| 0.6377 \| 100.0 \| 700 \| 1.0108 \| 0.815 \| 0.2593 \| 1.1011 \| 0.815 \| 0.8014 \| 0.1462 \| 0.0442 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1418 - Accuracy: 0.84 - Brier Loss: 0.2718 - Nll: 0.9778 - F1 Micro: 0.8400 - F1 Macro: 0.8296 - Ece: 0.1553 - Aurc: 0.0479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 5.6749 \| 0.2 \| 0.9075 \| 8.1551 \| 0.2000 \| 0.1380 \| 0.2949 \| 0.8075 \| \| No log \| 2.0 \| 14 \| 5.1602 \| 0.15 \| 0.8781 \| 7.4212 \| 0.15 \| 0.1415 \| 0.2402 \| 0.7606 \| \| No log \| 3.0 \| 21 \| 4.4947 \| 0.46 \| 0.7066 \| 2.8419 \| 0.46 \| 0.4202 \| 0.3230 \| 0.3244 \| \| No log \| 4.0 \| 28 \| 3.9789 \| 0.555 \| 0.5827 \| 1.6986 \| 0.555 \| 0.5525 \| 0.2796 \| 0.2218 \| \| No log \| 5.0 \| 35 \| 3.6991 \| 0.65 \| 0.4828 \| 1.7197 \| 0.65 \| 0.6491 \| 0.2315 \| 0.1491 \| \| No log \| 6.0 \| 42 \| 3.6495 \| 0.68 \| 0.4691 \| 1.8258 \| 0.68 \| 0.6586 \| 0.2555 \| 0.1358 \| \| No log \| 7.0 \| 49 \| 3.3912 \| 0.75 \| 0.3899 \| 1.8385 \| 0.75 \| 0.7276 \| 0.2237 \| 0.0920 \| \| No log \| 8.0 \| 56 \| 3.3055 \| 0.71 \| 0.3792 \| 1.5754 \| 0.7100 \| 0.6922 \| 0.2130 \| 0.1023 \| \| No log \| 9.0 \| 63 \| 3.3535 \| 0.72 \| 0.3836 \| 1.7076 \| 0.72 \| 0.7195 \| 0.2015 \| 0.0978 \| \| No log \| 10.0 \| 70 \| 3.1877 \| 0.785 \| 0.3190 \| 1.5736 \| 0.785 \| 0.7582 \| 0.1905 \| 0.0693 \| \| No log \| 11.0 \| 77 \| 3.5578 \| 0.72 \| 0.3812 \| 2.2613 \| 0.72 \| 0.7241 \| 0.1684 \| 0.0846 \| \| No log \| 12.0 \| 84 \| 3.3589 \| 0.775 \| 0.3389 \| 1.3228 \| 0.775 \| 0.7540 \| 0.1665 \| 0.0764 \| \| No log \| 13.0 \| 91 \| 3.1097 \| 0.805 \| 0.2856 \| 1.4183 \| 0.805 \| 0.7929 \| 0.1603 \| 0.0484 \| \| No log \| 14.0 \| 98 \| 3.2661 \| 0.815 \| 0.3146 \| 1.9097 \| 0.815 \| 0.8066 \| 0.1753 \| 0.0636 \| \| No log \| 15.0 \| 105 \| 3.3637 \| 0.755 \| 0.3361 \| 1.5166 \| 0.755 \| 0.7492 \| 0.1804 \| 0.0720 \| \| No log \| 16.0 \| 112 \| 3.1495 \| 0.8 \| 0.2994 \| 1.4586 \| 0.8000 \| 0.7926 \| 0.1714 \| 0.0604 \| \| No log \| 17.0 \| 119 \| 3.1573 \| 0.8 \| 0.2941 \| 1.6755 \| 0.8000 \| 0.7899 \| 0.1545 \| 0.0577 \| \| No log \| 18.0 \| 126 \| 3.4445 \| 0.77 \| 0.3416 \| 1.4075 \| 0.7700 \| 0.7503 \| 0.1620 \| 0.0807 \| \| No log \| 19.0 \| 133 \| 3.1292 \| 0.805 \| 0.2816 \| 1.3835 \| 0.805 \| 0.7815 \| 0.1768 \| 0.0526 \| \| No log \| 20.0 \| 140 \| 3.4253 \| 0.75 \| 0.3459 \| 2.0430 \| 0.75 \| 0.7591 \| 0.1697 \| 0.0706 \| \| No log \| 21.0 \| 147 \| 3.1645 \| 0.81 \| 0.3000 \| 1.7363 \| 0.81 \| 0.8113 \| 0.1711 \| 0.0614 \| \| No log \| 22.0 \| 154 \| 3.0823 \| 0.815 \| 0.2791 \| 1.5997 \| 0.815 \| 0.8020 \| 0.1417 \| 0.0556 \| \| No log \| 23.0 \| 161 \| 2.9898 \| 0.83 \| 0.2521 \| 1.4274 \| 0.83 \| 0.8189 \| 0.1589 \| 0.0434 \| \| No log \| 24.0 \| 168 \| 3.0915 \| 0.83 \| 0.2770 \| 1.3516 \| 0.83 \| 0.8173 \| 0.1495 \| 0.0538 \| \| No log \| 25.0 \| 175 \| 3.0405 \| 0.825 \| 0.2621 \| 1.5191 \| 0.825 \| 0.8048 \| 0.1329 \| 0.0494 \| \| No log \| 26.0 \| 182 \| 3.0621 \| 0.815 \| 0.2735 \| 1.0698 \| 0.815 \| 0.7955 \| 0.1617 \| 0.0522 \| \| No log \| 27.0 \| 189 \| 3.0228 \| 0.835 \| 0.2650 \| 1.4235 \| 0.835 \| 0.8315 \| 0.1565 \| 0.0502 \| \| No log \| 28.0 \| 196 \| 3.0677 \| 0.82 \| 0.2778 \| 1.5299 \| 0.82 \| 0.8165 \| 0.1660 \| 0.0557 \| \| No log \| 29.0 \| 203 \| 3.0272 \| 0.825 \| 0.2699 \| 1.4726 \| 0.825 \| 0.8204 \| 0.1643 \| 0.0491 \| \| No log \| 30.0 \| 210 \| 3.1090 \| 0.815 \| 0.2892 \| 1.3258 \| 0.815 \| 0.8026 \| 0.1585 \| 0.0536 \| \| No log \| 31.0 \| 217 \| 3.1069 \| 0.81 \| 0.2866 \| 1.5638 \| 0.81 \| 0.8050 \| 0.1473 \| 0.0557 \| \| No log \| 32.0 \| 224 \| 3.0374 \| 0.815 \| 0.2765 \| 1.2895 \| 0.815 \| 0.8045 \| 0.1476 \| 0.0527 \| \| No log \| 33.0 \| 231 \| 3.0503 \| 0.815 \| 0.2750 \| 1.3113 \| 0.815 \| 0.7975 \| 0.1531 \| 0.0517 \| \| No log \| 34.0 \| 238 \| 2.9852 \| 0.82 \| 0.2613 \| 1.4575 \| 0.82 \| 0.8110 \| 0.1600 \| 0.0448 \| \| No log \| 35.0 \| 245 \| 3.0437 \| 0.83 \| 0.2724 \| 1.3491 \| 0.83 \| 0.8205 \| 0.1622 \| 0.0571 \| \| No log \| 36.0 \| 252 \| 3.0098 \| 0.82 \| 0.2717 \| 1.2671 \| 0.82 \| 0.8055 \| 0.1567 \| 0.0519 \| \| No log \| 37.0 \| 259 \| 3.0025 \| 0.845 \| 0.2599 \| 1.2628 \| 0.845 \| 0.8255 \| 0.1342 \| 0.0481 \| \| No log \| 38.0 \| 266 \| 3.1854 \| 0.805 \| 0.3015 \| 1.2550 \| 0.805 \| 0.7956 \| 0.1560 \| 0.0601 \| \| No log \| 39.0 \| 273 \| 3.0704 \| 0.82 \| 0.2793 \| 1.2393 \| 0.82 \| 0.8057 \| 0.1566 \| 0.0557 \| \| No log \| 40.0 \| 280 \| 3.0739 \| 0.825 \| 0.2842 \| 1.2701 \| 0.825 \| 0.8169 \| 0.1371 \| 0.0513 \| \| No log \| 41.0 \| 287 \| 3.0465 \| 0.835 \| 0.2747 \| 1.2598 \| 0.835 \| 0.8302 \| 0.1449 \| 0.0538 \| \| No log \| 42.0 \| 294 \| 3.0691 \| 0.825 \| 0.2773 \| 1.1796 \| 0.825 \| 0.8137 \| 0.1372 \| 0.0511 \| \| No log \| 43.0 \| 301 \| 3.0734 \| 0.84 \| 0.2732 \| 1.1765 \| 0.8400 \| 0.8282 \| 0.1564 \| 0.0565 \| \| No log \| 44.0 \| 308 \| 3.0262 \| 0.845 \| 0.2622 \| 1.2152 \| 0.845 \| 0.8306 \| 0.1457 \| 0.0541 \| \| No log \| 45.0 \| 315 \| 3.0610 \| 0.835 \| 0.2727 \| 1.2249 \| 0.835 \| 0.8261 \| 0.1606 \| 0.0544 \| \| No log \| 46.0 \| 322 \| 3.0358 \| 0.84 \| 0.2767 \| 1.1020 \| 0.8400 \| 0.8323 \| 0.1416 \| 0.0527 \| \| No log \| 47.0 \| 329 \| 2.9893 \| 0.835 \| 0.2650 \| 1.1536 \| 0.835 \| 0.8252 \| 0.1386 \| 0.0493 \| \| No log \| 48.0 \| 336 \| 3.0498 \| 0.84 \| 0.2726 \| 1.1253 \| 0.8400 \| 0.8320 \| 0.1302 \| 0.0535 \| \| No log \| 49.0 \| 343 \| 2.9816 \| 0.845 \| 0.2585 \| 1.2068 \| 0.845 \| 0.8355 \| 0.1455 \| 0.0451 \| \| No log \| 50.0 \| 350 \| 3.0431 \| 0.835 \| 0.2686 \| 1.0596 \| 0.835 \| 0.8238 \| 0.1542 \| 0.0540 \| \| No log \| 51.0 \| 357 \| 3.0200 \| 0.835 \| 0.2639 \| 1.1806 \| 0.835 \| 0.8290 \| 0.1434 \| 0.0501 \| \| No log \| 52.0 \| 364 \| 3.0217 \| 0.845 \| 0.2664 \| 1.0846 \| 0.845 \| 0.8324 \| 0.1671 \| 0.0503 \| \| No log \| 53.0 \| 371 \| 3.0255 \| 0.84 \| 0.2649 \| 1.1803 \| 0.8400 \| 0.8318 \| 0.1350 \| 0.0488 \| \| No log \| 54.0 \| 378 \| 3.0069 \| 0.835 \| 0.2616 \| 1.2057 \| 0.835 \| 0.8190 \| 0.1284 \| 0.0496 \| \| No log \| 55.0 \| 385 \| 3.0609 \| 0.815 \| 0.2746 \| 1.0378 \| 0.815 \| 0.7970 \| 0.1422 \| 0.0490 \| \| No log \| 56.0 \| 392 \| 3.0111 \| 0.84 \| 0.2622 \| 1.1806 \| 0.8400 \| 0.8341 \| 0.1428 \| 0.0513 \| \| No log \| 57.0 \| 399 \| 3.0050 \| 0.84 \| 0.2643 \| 1.1898 \| 0.8400 \| 0.8299 \| 0.1452 \| 0.0494 \| \| No log \| 58.0 \| 406 \| 3.0426 \| 0.84 \| 0.2662 \| 1.0337 \| 0.8400 \| 0.8307 \| 0.1397 \| 0.0514 \| \| No log \| 59.0 \| 413 \| 3.0427 \| 0.835 \| 0.2682 \| 1.0309 \| 0.835 \| 0.8247 \| 0.1453 \| 0.0491 \| \| No log \| 60.0 \| 420 \| 3.0449 \| 0.83 \| 0.2744 \| 1.0039 \| 0.83 \| 0.8141 \| 0.1436 \| 0.0484 \| \| No log \| 61.0 \| 427 \| 3.0524 \| 0.83 \| 0.2729 \| 1.1480 \| 0.83 \| 0.8162 \| 0.1454 \| 0.0477 \| \| No log \| 62.0 \| 434 \| 3.0290 \| 0.835 \| 0.2610 \| 1.1757 \| 0.835 \| 0.8264 \| 0.1476 \| 0.0506 \| \| No log \| 63.0 \| 441 \| 3.0574 \| 0.83 \| 0.2712 \| 1.0242 \| 0.83 \| 0.8161 \| 0.1464 \| 0.0485 \| \| No log \| 64.0 \| 448 \| 3.0436 \| 0.835 \| 0.2684 \| 1.1326 \| 0.835 \| 0.8267 \| 0.1417 \| 0.0470 \| \| No log \| 65.0 \| 455 \| 3.0170 \| 0.84 \| 0.2610 \| 1.1095 \| 0.8400 \| 0.8289 \| 0.1520 \| 0.0492 \| \| No log \| 66.0 \| 462 \| 3.0176 \| 0.835 \| 0.2623 \| 1.1140 \| 0.835 \| 0.8225 \| 0.1262 \| 0.0459 \| \| No log \| 67.0 \| 469 \| 3.0712 \| 0.84 \| 0.2735 \| 1.0884 \| 0.8400 \| 0.8296 \| 0.1421 \| 0.0516 \| \| No log \| 68.0 \| 476 \| 3.0258 \| 0.84 \| 0.2670 \| 1.1388 \| 0.8400 \| 0.8279 \| 0.1478 \| 0.0461 \| \| No log \| 69.0 \| 483 \| 3.0838 \| 0.835 \| 0.2707 \| 1.0937 \| 0.835 \| 0.8232 \| 0.1425 \| 0.0477 \| \| No log \| 70.0 \| 490 \| 3.1076 \| 0.82 \| 0.2819 \| 1.0030 \| 0.82 \| 0.7998 \| 0.1507 \| 0.0480 \| \| No log \| 71.0 \| 497 \| 3.0696 \| 0.84 \| 0.2725 \| 1.0175 \| 0.8400 \| 0.8349 \| 0.1567 \| 0.0501 \| \| 2.6485 \| 72.0 \| 504 \| 3.0535 \| 0.84 \| 0.2676 \| 1.0079 \| 0.8400 \| 0.8253 \| 0.1351 \| 0.0477 \| \| 2.6485 \| 73.0 \| 511 \| 3.0326 \| 0.83 \| 0.2667 \| 0.9792 \| 0.83 \| 0.8093 \| 0.1334 \| 0.0464 \| \| 2.6485 \| 74.0 \| 518 \| 3.0271 \| 0.835 \| 0.2616 \| 1.0865 \| 0.835 \| 0.8193 \| 0.1223 \| 0.0454 \| \| 2.6485 \| 75.0 \| 525 \| 3.0894 \| 0.83 \| 0.2732 \| 0.9764 \| 0.83 \| 0.8123 \| 0.1446 \| 0.0489 \| \| 2.6485 \| 76.0 \| 532 \| 3.0905 \| 0.835 \| 0.2730 \| 1.0736 \| 0.835 \| 0.8232 \| 0.1578 \| 0.0485 \| \| 2.6485 \| 77.0 \| 539 \| 3.0507 \| 0.84 \| 0.2646 \| 1.0716 \| 0.8400 \| 0.8279 \| 0.1424 \| 0.0469 \| \| 2.6485 \| 78.0 \| 546 \| 3.0981 \| 0.845 \| 0.2712 \| 0.9916 \| 0.845 \| 0.8324 \| 0.1452 \| 0.0508 \| \| 2.6485 \| 79.0 \| 553 \| 3.0820 \| 0.84 \| 0.2728 \| 0.9791 \| 0.8400 \| 0.8296 \| 0.1403 \| 0.0473 \| \| 2.6485 \| 80.0 \| 560 \| 3.0978 \| 0.84 \| 0.2733 \| 0.9864 \| 0.8400 \| 0.8296 \| 0.1480 \| 0.0485 \| \| 2.6485 \| 81.0 \| 567 \| 3.0936 \| 0.84 \| 0.2716 \| 0.9955 \| 0.8400 \| 0.8296 \| 0.1483 \| 0.0474 \| \| 2.6485 \| 82.0 \| 574 \| 3.0937 \| 0.845 \| 0.2685 \| 0.9875 \| 0.845 \| 0.8324 \| 0.1459 \| 0.0486 \| \| 2.6485 \| 83.0 \| 581 \| 3.0940 \| 0.84 \| 0.2719 \| 0.9863 \| 0.8400 \| 0.8296 \| 0.1481 \| 0.0470 \| \| 2.6485 \| 84.0 \| 588 \| 3.0745 \| 0.84 \| 0.2656 \| 1.0795 \| 0.8400 \| 0.8323 \| 0.1460 \| 0.0476 \| \| 2.6485 \| 85.0 \| 595 \| 3.1089 \| 0.845 \| 0.2681 \| 1.0050 \| 0.845 \| 0.8324 \| 0.1568 \| 0.0492 \| \| 2.6485 \| 86.0 \| 602 \| 3.0880 \| 0.84 \| 0.2695 \| 1.0607 \| 0.8400 \| 0.8296 \| 0.1409 \| 0.0474 \| \| 2.6485 \| 87.0 \| 609 \| 3.0848 \| 0.84 \| 0.2666 \| 0.9996 \| 0.8400 \| 0.8296 \| 0.1425 \| 0.0470 \| \| 2.6485 \| 88.0 \| 616 \| 3.1144 \| 0.84 \| 0.2682 \| 0.9937 \| 0.8400 \| 0.8296 \| 0.1380 \| 0.0482 \| \| 2.6485 \| 89.0 \| 623 \| 3.1316 \| 0.84 \| 0.2711 \| 0.9884 \| 0.8400 \| 0.8296 \| 0.1484 \| 0.0490 \| \| 2.6485 \| 90.0 \| 630 \| 3.1312 \| 0.84 \| 0.2726 \| 0.9732 \| 0.8400 \| 0.8296 \| 0.1525 \| 0.0488 \| \| 2.6485 \| 91.0 \| 637 \| 3.1312 \| 0.84 \| 0.2723 \| 0.9794 \| 0.8400 \| 0.8296 \| 0.1475 \| 0.0481 \| \| 2.6485 \| 92.0 \| 644 \| 3.1426 \| 0.84 \| 0.2731 \| 0.9728 \| 0.8400 \| 0.8296 \| 0.1478 \| 0.0491 \| \| 2.6485 \| 93.0 \| 651 \| 3.1351 \| 0.84 \| 0.2709 \| 0.9741 \| 0.8400 \| 0.8296 \| 0.1438 \| 0.0483 \| \| 2.6485 \| 94.0 \| 658 \| 3.1390 \| 0.84 \| 0.2716 \| 0.9764 \| 0.8400 \| 0.8296 \| 0.1576 \| 0.0483 \| \| 2.6485 \| 95.0 \| 665 \| 3.1366 \| 0.84 \| 0.2711 \| 0.9795 \| 0.8400 \| 0.8296 \| 0.1480 \| 0.0484 \| \| 2.6485 \| 96.0 \| 672 \| 3.1337 \| 0.84 \| 0.2710 \| 0.9828 \| 0.8400 \| 0.8296 \| 0.1475 \| 0.0478 \| \| 2.6485 \| 97.0 \| 679 \| 3.1431 \| 0.84 \| 0.2723 \| 0.9767 \| 0.8400 \| 0.8296 \| 0.1587 \| 0.0480 \| \| 2.6485 \| 98.0 \| 686 \| 3.1388 \| 0.84 \| 0.2713 \| 0.9808 \| 0.8400 \| 0.8296 \| 0.1476 \| 0.0480 \| \| 2.6485 \| 99.0 \| 693 \| 3.1420 \| 0.84 \| 0.2718 \| 0.9778 \| 0.8400 \| 0.8296 \| 0.1560 \| 0.0480 \| \| 2.6485 \| 100.0 \| 700 \| 3.1418 \| 0.84 \| 0.2718 \| 0.9778 \| 0.8400 \| 0.8296 \| 0.1553 \| 0.0479 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-tiny_tobacco3482_hint	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_hint This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1196 - Accuracy: 0.805 - Brier Loss: 0.3299 - Nll: 1.3687 - F1 Micro: 0.805 - F1 Macro: 0.7917 - Ece: 0.1606 - Aurc: 0.0741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 3.7183 \| 0.225 \| 0.9191 \| 7.9210 \| 0.225 \| 0.1037 \| 0.3197 \| 0.7539 \| \| No log \| 2.0 \| 14 \| 3.2602 \| 0.365 \| 0.7808 \| 4.4817 \| 0.3650 \| 0.2558 \| 0.2908 \| 0.5116 \| \| No log \| 3.0 \| 21 \| 2.8475 \| 0.53 \| 0.6305 \| 2.7026 \| 0.53 \| 0.4263 \| 0.2496 \| 0.2812 \| \| No log \| 4.0 \| 28 \| 2.6118 \| 0.575 \| 0.5382 \| 1.8007 \| 0.575 \| 0.5478 \| 0.2435 \| 0.2022 \| \| No log \| 5.0 \| 35 \| 2.4289 \| 0.66 \| 0.4404 \| 1.6436 \| 0.66 \| 0.6317 \| 0.2124 \| 0.1236 \| \| No log \| 6.0 \| 42 \| 2.4007 \| 0.69 \| 0.4123 \| 1.9255 \| 0.69 \| 0.6896 \| 0.2038 \| 0.1195 \| \| No log \| 7.0 \| 49 \| 2.3239 \| 0.745 \| 0.3775 \| 1.9428 \| 0.745 \| 0.7365 \| 0.1838 \| 0.0957 \| \| No log \| 8.0 \| 56 \| 2.3109 \| 0.735 \| 0.3660 \| 1.6472 \| 0.735 \| 0.7370 \| 0.1729 \| 0.0842 \| \| No log \| 9.0 \| 63 \| 2.3263 \| 0.74 \| 0.3867 \| 1.5935 \| 0.74 \| 0.7422 \| 0.1803 \| 0.0944 \| \| No log \| 10.0 \| 70 \| 2.3878 \| 0.735 \| 0.4037 \| 1.6604 \| 0.735 \| 0.7039 \| 0.2057 \| 0.0909 \| \| No log \| 11.0 \| 77 \| 2.5009 \| 0.715 \| 0.4220 \| 1.8526 \| 0.715 \| 0.6918 \| 0.2184 \| 0.1017 \| \| No log \| 12.0 \| 84 \| 2.5428 \| 0.72 \| 0.4169 \| 1.8799 \| 0.72 \| 0.6894 \| 0.2000 \| 0.0979 \| \| No log \| 13.0 \| 91 \| 2.5197 \| 0.765 \| 0.3884 \| 1.8802 \| 0.765 \| 0.7268 \| 0.1895 \| 0.0933 \| \| No log \| 14.0 \| 98 \| 2.4928 \| 0.725 \| 0.4147 \| 1.6096 \| 0.7250 \| 0.6923 \| 0.2078 \| 0.0940 \| \| No log \| 15.0 \| 105 \| 2.4172 \| 0.765 \| 0.3748 \| 1.6754 \| 0.765 \| 0.7317 \| 0.1874 \| 0.0824 \| \| No log \| 16.0 \| 112 \| 2.4116 \| 0.76 \| 0.3873 \| 1.4155 \| 0.76 \| 0.7280 \| 0.1972 \| 0.0890 \| \| No log \| 17.0 \| 119 \| 2.9196 \| 0.73 \| 0.4500 \| 1.8493 \| 0.7300 \| 0.7220 \| 0.2290 \| 0.1657 \| \| No log \| 18.0 \| 126 \| 2.5815 \| 0.765 \| 0.4175 \| 2.0326 \| 0.765 \| 0.7467 \| 0.2125 \| 0.0990 \| \| No log \| 19.0 \| 133 \| 2.7076 \| 0.735 \| 0.4475 \| 1.8526 \| 0.735 \| 0.7152 \| 0.2062 \| 0.1049 \| \| No log \| 20.0 \| 140 \| 2.6951 \| 0.71 \| 0.4709 \| 2.0521 \| 0.7100 \| 0.7258 \| 0.2380 \| 0.1188 \| \| No log \| 21.0 \| 147 \| 2.4037 \| 0.765 \| 0.4013 \| 1.8740 \| 0.765 \| 0.7691 \| 0.2009 \| 0.0949 \| \| No log \| 22.0 \| 154 \| 2.6585 \| 0.73 \| 0.4303 \| 2.0299 \| 0.7300 \| 0.7170 \| 0.2110 \| 0.1004 \| \| No log \| 23.0 \| 161 \| 2.4320 \| 0.75 \| 0.3950 \| 1.8720 \| 0.75 \| 0.7340 \| 0.2004 \| 0.0895 \| \| No log \| 24.0 \| 168 \| 2.4891 \| 0.74 \| 0.4199 \| 1.6458 \| 0.74 \| 0.7458 \| 0.2227 \| 0.1128 \| \| No log \| 25.0 \| 175 \| 2.6550 \| 0.705 \| 0.4833 \| 1.7755 \| 0.705 \| 0.7042 \| 0.2478 \| 0.1145 \| \| No log \| 26.0 \| 182 \| 2.3191 \| 0.765 \| 0.3965 \| 1.6941 \| 0.765 \| 0.7415 \| 0.1954 \| 0.0731 \| \| No log \| 27.0 \| 189 \| 2.3000 \| 0.785 \| 0.3763 \| 1.3143 \| 0.785 \| 0.7416 \| 0.2026 \| 0.0712 \| \| No log \| 28.0 \| 196 \| 2.2047 \| 0.78 \| 0.3409 \| 1.4818 \| 0.78 \| 0.7694 \| 0.1759 \| 0.0688 \| \| No log \| 29.0 \| 203 \| 2.3587 \| 0.77 \| 0.3781 \| 1.6779 \| 0.7700 \| 0.7571 \| 0.1937 \| 0.0766 \| \| No log \| 30.0 \| 210 \| 2.5027 \| 0.75 \| 0.4400 \| 1.8454 \| 0.75 \| 0.7338 \| 0.2232 \| 0.0817 \| \| No log \| 31.0 \| 217 \| 2.4092 \| 0.77 \| 0.3899 \| 1.5010 \| 0.7700 \| 0.7498 \| 0.1987 \| 0.0710 \| \| No log \| 32.0 \| 224 \| 2.7655 \| 0.74 \| 0.4520 \| 2.0720 \| 0.74 \| 0.7177 \| 0.2266 \| 0.0855 \| \| No log \| 33.0 \| 231 \| 2.3814 \| 0.76 \| 0.3979 \| 1.4053 \| 0.76 \| 0.7352 \| 0.1982 \| 0.0754 \| \| No log \| 34.0 \| 238 \| 2.3946 \| 0.775 \| 0.3790 \| 1.6969 \| 0.775 \| 0.7387 \| 0.1908 \| 0.0876 \| \| No log \| 35.0 \| 245 \| 2.5158 \| 0.775 \| 0.4064 \| 1.4329 \| 0.775 \| 0.7428 \| 0.2068 \| 0.0929 \| \| No log \| 36.0 \| 252 \| 2.4920 \| 0.75 \| 0.4281 \| 1.5724 \| 0.75 \| 0.7470 \| 0.2161 \| 0.1028 \| \| No log \| 37.0 \| 259 \| 2.4541 \| 0.765 \| 0.3842 \| 1.5272 \| 0.765 \| 0.7163 \| 0.1918 \| 0.0853 \| \| No log \| 38.0 \| 266 \| 2.3785 \| 0.82 \| 0.3319 \| 1.6360 \| 0.82 \| 0.7944 \| 0.1732 \| 0.0811 \| \| No log \| 39.0 \| 273 \| 2.3721 \| 0.77 \| 0.3831 \| 1.2724 \| 0.7700 \| 0.7649 \| 0.1880 \| 0.0818 \| \| No log \| 40.0 \| 280 \| 2.5684 \| 0.795 \| 0.3494 \| 1.7291 \| 0.795 \| 0.7724 \| 0.1771 \| 0.1126 \| \| No log \| 41.0 \| 287 \| 2.4835 \| 0.78 \| 0.3799 \| 1.7700 \| 0.78 \| 0.7594 \| 0.2024 \| 0.0821 \| \| No log \| 42.0 \| 294 \| 2.4690 \| 0.795 \| 0.3678 \| 1.6379 \| 0.795 \| 0.7804 \| 0.1836 \| 0.0840 \| \| No log \| 43.0 \| 301 \| 2.3069 \| 0.77 \| 0.3809 \| 1.4970 \| 0.7700 \| 0.7621 \| 0.1807 \| 0.0784 \| \| No log \| 44.0 \| 308 \| 2.4424 \| 0.795 \| 0.3558 \| 1.7162 \| 0.795 \| 0.7857 \| 0.1879 \| 0.0667 \| \| No log \| 45.0 \| 315 \| 2.0018 \| 0.86 \| 0.2387 \| 1.4924 \| 0.8600 \| 0.8558 \| 0.1180 \| 0.0526 \| \| No log \| 46.0 \| 322 \| 2.4174 \| 0.785 \| 0.3593 \| 1.7688 \| 0.785 \| 0.7662 \| 0.1859 \| 0.0773 \| \| No log \| 47.0 \| 329 \| 2.1816 \| 0.84 \| 0.2862 \| 1.5093 \| 0.8400 \| 0.8230 \| 0.1459 \| 0.0679 \| \| No log \| 48.0 \| 336 \| 2.3006 \| 0.78 \| 0.3601 \| 1.7675 \| 0.78 \| 0.7496 \| 0.1847 \| 0.0752 \| \| No log \| 49.0 \| 343 \| 2.2952 \| 0.81 \| 0.3231 \| 1.4851 \| 0.81 \| 0.7865 \| 0.1691 \| 0.0656 \| \| No log \| 50.0 \| 350 \| 2.1346 \| 0.8 \| 0.3132 \| 1.4625 \| 0.8000 \| 0.7937 \| 0.1660 \| 0.0789 \| \| No log \| 51.0 \| 357 \| 2.2935 \| 0.81 \| 0.3383 \| 1.5764 \| 0.81 \| 0.7914 \| 0.1770 \| 0.0717 \| \| No log \| 52.0 \| 364 \| 2.1792 \| 0.825 \| 0.3116 \| 1.5652 \| 0.825 \| 0.8163 \| 0.1454 \| 0.0654 \| \| No log \| 53.0 \| 371 \| 2.1231 \| 0.81 \| 0.3066 \| 1.3012 \| 0.81 \| 0.8062 \| 0.1552 \| 0.0604 \| \| No log \| 54.0 \| 378 \| 1.9712 \| 0.825 \| 0.2854 \| 1.2891 \| 0.825 \| 0.8137 \| 0.1430 \| 0.0521 \| \| No log \| 55.0 \| 385 \| 2.0133 \| 0.825 \| 0.2839 \| 1.3994 \| 0.825 \| 0.8086 \| 0.1433 \| 0.0560 \| \| No log \| 56.0 \| 392 \| 1.9978 \| 0.835 \| 0.2800 \| 1.4348 \| 0.835 \| 0.8232 \| 0.1415 \| 0.0573 \| \| No log \| 57.0 \| 399 \| 1.9847 \| 0.83 \| 0.2825 \| 1.3907 \| 0.83 \| 0.8153 \| 0.1421 \| 0.0560 \| \| No log \| 58.0 \| 406 \| 1.9892 \| 0.83 \| 0.2832 \| 1.4502 \| 0.83 \| 0.8153 \| 0.1503 \| 0.0566 \| \| No log \| 59.0 \| 413 \| 1.9848 \| 0.83 \| 0.2851 \| 1.4506 \| 0.83 \| 0.8156 \| 0.1462 \| 0.0560 \| \| No log \| 60.0 \| 420 \| 1.9871 \| 0.835 \| 0.2910 \| 1.4527 \| 0.835 \| 0.8191 \| 0.1608 \| 0.0566 \| \| No log \| 61.0 \| 427 \| 1.9914 \| 0.825 \| 0.2932 \| 1.4490 \| 0.825 \| 0.8095 \| 0.1464 \| 0.0587 \| \| No log \| 62.0 \| 434 \| 1.9908 \| 0.825 \| 0.2958 \| 1.4459 \| 0.825 \| 0.8095 \| 0.1493 \| 0.0597 \| \| No log \| 63.0 \| 441 \| 1.9954 \| 0.825 \| 0.3012 \| 1.4480 \| 0.825 \| 0.8095 \| 0.1469 \| 0.0606 \| \| No log \| 64.0 \| 448 \| 2.0111 \| 0.82 \| 0.3050 \| 1.4487 \| 0.82 \| 0.8026 \| 0.1507 \| 0.0619 \| \| No log \| 65.0 \| 455 \| 2.0212 \| 0.82 \| 0.3046 \| 1.4469 \| 0.82 \| 0.8026 \| 0.1604 \| 0.0634 \| \| No log \| 66.0 \| 462 \| 2.0170 \| 0.82 \| 0.3059 \| 1.4443 \| 0.82 \| 0.8040 \| 0.1539 \| 0.0639 \| \| No log \| 67.0 \| 469 \| 2.0170 \| 0.815 \| 0.3056 \| 1.4496 \| 0.815 \| 0.8019 \| 0.1534 \| 0.0643 \| \| No log \| 68.0 \| 476 \| 2.0316 \| 0.82 \| 0.3115 \| 1.4522 \| 0.82 \| 0.8026 \| 0.1606 \| 0.0645 \| \| No log \| 69.0 \| 483 \| 2.0335 \| 0.805 \| 0.3132 \| 1.3831 \| 0.805 \| 0.7855 \| 0.1607 \| 0.0654 \| \| No log \| 70.0 \| 490 \| 2.0362 \| 0.815 \| 0.3106 \| 1.3834 \| 0.815 \| 0.7989 \| 0.1614 \| 0.0655 \| \| No log \| 71.0 \| 497 \| 2.0318 \| 0.815 \| 0.3105 \| 1.3893 \| 0.815 \| 0.7947 \| 0.1541 \| 0.0662 \| \| 1.3661 \| 72.0 \| 504 \| 2.0434 \| 0.815 \| 0.3135 \| 1.4473 \| 0.815 \| 0.7955 \| 0.1579 \| 0.0653 \| \| 1.3661 \| 73.0 \| 511 \| 2.0517 \| 0.81 \| 0.3139 \| 1.3838 \| 0.81 \| 0.7917 \| 0.1564 \| 0.0680 \| \| 1.3661 \| 74.0 \| 518 \| 2.0594 \| 0.82 \| 0.3162 \| 1.3783 \| 0.82 \| 0.7975 \| 0.1626 \| 0.0681 \| \| 1.3661 \| 75.0 \| 525 \| 2.0628 \| 0.815 \| 0.3210 \| 1.3752 \| 0.815 \| 0.7944 \| 0.1598 \| 0.0706 \| \| 1.3661 \| 76.0 \| 532 \| 2.0605 \| 0.81 \| 0.3158 \| 1.3711 \| 0.81 \| 0.7886 \| 0.1639 \| 0.0684 \| \| 1.3661 \| 77.0 \| 539 \| 2.0718 \| 0.815 \| 0.3187 \| 1.3860 \| 0.815 \| 0.7944 \| 0.1710 \| 0.0705 \| \| 1.3661 \| 78.0 \| 546 \| 2.0749 \| 0.815 \| 0.3168 \| 1.3658 \| 0.815 \| 0.7958 \| 0.1569 \| 0.0713 \| \| 1.3661 \| 79.0 \| 553 \| 2.0796 \| 0.83 \| 0.3188 \| 1.3016 \| 0.83 \| 0.8147 \| 0.1646 \| 0.0722 \| \| 1.3661 \| 80.0 \| 560 \| 2.0746 \| 0.81 \| 0.3210 \| 1.3758 \| 0.81 \| 0.7916 \| 0.1580 \| 0.0729 \| \| 1.3661 \| 81.0 \| 567 \| 2.0819 \| 0.815 \| 0.3194 \| 1.3686 \| 0.815 \| 0.7913 \| 0.1576 \| 0.0722 \| \| 1.3661 \| 82.0 \| 574 \| 2.0866 \| 0.825 \| 0.3182 \| 1.3627 \| 0.825 \| 0.8085 \| 0.1602 \| 0.0718 \| \| 1.3661 \| 83.0 \| 581 \| 2.0942 \| 0.815 \| 0.3246 \| 1.3249 \| 0.815 \| 0.8008 \| 0.1591 \| 0.0727 \| \| 1.3661 \| 84.0 \| 588 \| 2.0938 \| 0.815 \| 0.3246 \| 1.3680 \| 0.815 \| 0.7984 \| 0.1848 \| 0.0727 \| \| 1.3661 \| 85.0 \| 595 \| 2.0912 \| 0.82 \| 0.3222 \| 1.3662 \| 0.82 \| 0.8012 \| 0.1594 \| 0.0702 \| \| 1.3661 \| 86.0 \| 602 \| 2.0941 \| 0.82 \| 0.3234 \| 1.3764 \| 0.82 \| 0.8012 \| 0.1576 \| 0.0738 \| \| 1.3661 \| 87.0 \| 609 \| 2.1037 \| 0.8 \| 0.3304 \| 1.3821 \| 0.8000 \| 0.7821 \| 0.1599 \| 0.0740 \| \| 1.3661 \| 88.0 \| 616 \| 2.1098 \| 0.805 \| 0.3288 \| 1.3587 \| 0.805 \| 0.7932 \| 0.1678 \| 0.0718 \| \| 1.3661 \| 89.0 \| 623 \| 2.1119 \| 0.81 \| 0.3276 \| 1.3636 \| 0.81 \| 0.7945 \| 0.1622 \| 0.0728 \| \| 1.3661 \| 90.0 \| 630 \| 2.1078 \| 0.805 \| 0.3279 \| 1.3641 \| 0.805 \| 0.7914 \| 0.1734 \| 0.0737 \| \| 1.3661 \| 91.0 \| 637 \| 2.1110 \| 0.8 \| 0.3296 \| 1.3686 \| 0.8000 \| 0.7879 \| 0.1636 \| 0.0744 \| \| 1.3661 \| 92.0 \| 644 \| 2.1150 \| 0.8 \| 0.3317 \| 1.3685 \| 0.8000 \| 0.7879 \| 0.1730 \| 0.0742 \| \| 1.3661 \| 93.0 \| 651 \| 2.1146 \| 0.8 \| 0.3303 \| 1.3693 \| 0.8000 \| 0.7881 \| 0.1631 \| 0.0742 \| \| 1.3661 \| 94.0 \| 658 \| 2.1153 \| 0.805 \| 0.3292 \| 1.3657 \| 0.805 \| 0.7917 \| 0.1676 \| 0.0734 \| \| 1.3661 \| 95.0 \| 665 \| 2.1188 \| 0.805 \| 0.3298 \| 1.3683 \| 0.805 \| 0.7917 \| 0.1690 \| 0.0735 \| \| 1.3661 \| 96.0 \| 672 \| 2.1183 \| 0.805 \| 0.3291 \| 1.3691 \| 0.805 \| 0.7914 \| 0.1687 \| 0.0742 \| \| 1.3661 \| 97.0 \| 679 \| 2.1155 \| 0.81 \| 0.3271 \| 1.3664 \| 0.81 \| 0.7942 \| 0.1599 \| 0.0743 \| \| 1.3661 \| 98.0 \| 686 \| 2.1183 \| 0.805 \| 0.3285 \| 1.3673 \| 0.805 \| 0.7914 \| 0.1638 \| 0.0740 \| \| 1.3661 \| 99.0 \| 693 \| 2.1179 \| 0.805 \| 0.3297 \| 1.3686 \| 0.805 \| 0.7917 \| 0.1613 \| 0.0741 \| \| 1.3661 \| 100.0 \| 700 \| 2.1196 \| 0.805 \| 0.3299 \| 1.3687 \| 0.805 \| 0.7917 \| 0.1606 \| 0.0741 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_small_sgd_00001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_00001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6074 - Accuracy: 0.2222 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4194 \| 1.0 \| 27 \| 1.6162 \| 0.2222 \| \| 1.4315 \| 2.0 \| 54 \| 1.6158 \| 0.2222 \| \| 1.4532 \| 3.0 \| 81 \| 1.6154 \| 0.2222 \| \| 1.4652 \| 4.0 \| 108 \| 1.6150 \| 0.2222 \| \| 1.4244 \| 5.0 \| 135 \| 1.6147 \| 0.2222 \| \| 1.4622 \| 6.0 \| 162 \| 1.6143 \| 0.2222 \| \| 1.4528 \| 7.0 \| 189 \| 1.6140 \| 0.2222 \| \| 1.4262 \| 8.0 \| 216 \| 1.6136 \| 0.2222 \| \| 1.4181 \| 9.0 \| 243 \| 1.6133 \| 0.2222 \| \| 1.4163 \| 10.0 \| 270 \| 1.6130 \| 0.2222 \| \| 1.4463 \| 11.0 \| 297 \| 1.6127 \| 0.2222 \| \| 1.4137 \| 12.0 \| 324 \| 1.6124 \| 0.2222 \| \| 1.4131 \| 13.0 \| 351 \| 1.6121 \| 0.2222 \| \| 1.4148 \| 14.0 \| 378 \| 1.6118 \| 0.2222 \| \| 1.444 \| 15.0 \| 405 \| 1.6115 \| 0.2222 \| \| 1.4135 \| 16.0 \| 432 \| 1.6113 \| 0.2222 \| \| 1.4356 \| 17.0 \| 459 \| 1.6110 \| 0.2222 \| \| 1.4146 \| 18.0 \| 486 \| 1.6108 \| 0.2222 \| \| 1.4096 \| 19.0 \| 513 \| 1.6105 \| 0.2222 \| \| 1.4038 \| 20.0 \| 540 \| 1.6103 \| 0.2222 \| \| 1.3926 \| 21.0 \| 567 \| 1.6101 \| 0.2222 \| \| 1.4332 \| 22.0 \| 594 \| 1.6099 \| 0.2222 \| \| 1.4214 \| 23.0 \| 621 \| 1.6097 \| 0.2222 \| \| 1.4083 \| 24.0 \| 648 \| 1.6095 \| 0.2222 \| \| 1.4271 \| 25.0 \| 675 \| 1.6093 \| 0.2222 \| \| 1.4496 \| 26.0 \| 702 \| 1.6091 \| 0.2222 \| \| 1.4117 \| 27.0 \| 729 \| 1.6090 \| 0.2222 \| \| 1.403 \| 28.0 \| 756 \| 1.6088 \| 0.2222 \| \| 1.3913 \| 29.0 \| 783 \| 1.6087 \| 0.2222 \| \| 1.4302 \| 30.0 \| 810 \| 1.6085 \| 0.2222 \| \| 1.4037 \| 31.0 \| 837 \| 1.6084 \| 0.2222 \| \| 1.4442 \| 32.0 \| 864 \| 1.6083 \| 0.2222 \| \| 1.4272 \| 33.0 \| 891 \| 1.6082 \| 0.2222 \| \| 1.4095 \| 34.0 \| 918 \| 1.6080 \| 0.2222 \| \| 1.4234 \| 35.0 \| 945 \| 1.6079 \| 0.2222 \| \| 1.4343 \| 36.0 \| 972 \| 1.6079 \| 0.2222 \| \| 1.4253 \| 37.0 \| 999 \| 1.6078 \| 0.2222 \| \| 1.4109 \| 38.0 \| 1026 \| 1.6077 \| 0.2222 \| \| 1.4096 \| 39.0 \| 1053 \| 1.6076 \| 0.2222 \| \| 1.3772 \| 40.0 \| 1080 \| 1.6076 \| 0.2222 \| \| 1.4046 \| 41.0 \| 1107 \| 1.6075 \| 0.2222 \| \| 1.384 \| 42.0 \| 1134 \| 1.6075 \| 0.2222 \| \| 1.4202 \| 43.0 \| 1161 \| 1.6075 \| 0.2222 \| \| 1.3963 \| 44.0 \| 1188 \| 1.6074 \| 0.2222 \| \| 1.4183 \| 45.0 \| 1215 \| 1.6074 \| 0.2222 \| \| 1.3888 \| 46.0 \| 1242 \| 1.6074 \| 0.2222 \| \| 1.4088 \| 47.0 \| 1269 \| 1.6074 \| 0.2222 \| \| 1.393 \| 48.0 \| 1296 \| 1.6074 \| 0.2222 \| \| 1.4397 \| 49.0 \| 1323 \| 1.6074 \| 0.2222 \| \| 1.4472 \| 50.0 \| 1350 \| 1.6074 \| 0.2222 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_00001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_00001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4921 - Accuracy: 0.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5247 \| 1.0 \| 27 \| 1.5122 \| 0.1778 \| \| 1.5208 \| 2.0 \| 54 \| 1.5113 \| 0.1778 \| \| 1.5431 \| 3.0 \| 81 \| 1.5104 \| 0.1778 \| \| 1.5874 \| 4.0 \| 108 \| 1.5095 \| 0.1778 \| \| 1.5185 \| 5.0 \| 135 \| 1.5086 \| 0.1778 \| \| 1.5124 \| 6.0 \| 162 \| 1.5078 \| 0.1778 \| \| 1.4656 \| 7.0 \| 189 \| 1.5070 \| 0.1778 \| \| 1.5113 \| 8.0 \| 216 \| 1.5062 \| 0.1778 \| \| 1.5043 \| 9.0 \| 243 \| 1.5054 \| 0.1778 \| \| 1.505 \| 10.0 \| 270 \| 1.5047 \| 0.1778 \| \| 1.4599 \| 11.0 \| 297 \| 1.5040 \| 0.1778 \| \| 1.5036 \| 12.0 \| 324 \| 1.5033 \| 0.1778 \| \| 1.5237 \| 13.0 \| 351 \| 1.5026 \| 0.1778 \| \| 1.511 \| 14.0 \| 378 \| 1.5019 \| 0.1778 \| \| 1.5324 \| 15.0 \| 405 \| 1.5013 \| 0.1778 \| \| 1.5272 \| 16.0 \| 432 \| 1.5007 \| 0.1778 \| \| 1.5263 \| 17.0 \| 459 \| 1.5002 \| 0.1778 \| \| 1.4937 \| 18.0 \| 486 \| 1.4996 \| 0.1778 \| \| 1.5117 \| 19.0 \| 513 \| 1.4991 \| 0.1778 \| \| 1.516 \| 20.0 \| 540 \| 1.4985 \| 0.1778 \| \| 1.5298 \| 21.0 \| 567 \| 1.4981 \| 0.1778 \| \| 1.5031 \| 22.0 \| 594 \| 1.4976 \| 0.1778 \| \| 1.496 \| 23.0 \| 621 \| 1.4971 \| 0.1778 \| \| 1.4984 \| 24.0 \| 648 \| 1.4967 \| 0.2 \| \| 1.4849 \| 25.0 \| 675 \| 1.4963 \| 0.2 \| \| 1.5277 \| 26.0 \| 702 \| 1.4959 \| 0.2 \| \| 1.4813 \| 27.0 \| 729 \| 1.4955 \| 0.2 \| \| 1.5008 \| 28.0 \| 756 \| 1.4952 \| 0.2 \| \| 1.5143 \| 29.0 \| 783 \| 1.4948 \| 0.2 \| \| 1.5063 \| 30.0 \| 810 \| 1.4945 \| 0.2 \| \| 1.5197 \| 31.0 \| 837 \| 1.4942 \| 0.2 \| \| 1.4689 \| 32.0 \| 864 \| 1.4940 \| 0.2 \| \| 1.5261 \| 33.0 \| 891 \| 1.4937 \| 0.2 \| \| 1.5047 \| 34.0 \| 918 \| 1.4935 \| 0.2 \| \| 1.4608 \| 35.0 \| 945 \| 1.4933 \| 0.2 \| \| 1.5134 \| 36.0 \| 972 \| 1.4931 \| 0.2 \| \| 1.4999 \| 37.0 \| 999 \| 1.4929 \| 0.2 \| \| 1.4901 \| 38.0 \| 1026 \| 1.4928 \| 0.2 \| \| 1.4933 \| 39.0 \| 1053 \| 1.4926 \| 0.2 \| \| 1.5285 \| 40.0 \| 1080 \| 1.4925 \| 0.2 \| \| 1.5189 \| 41.0 \| 1107 \| 1.4924 \| 0.2 \| \| 1.5357 \| 42.0 \| 1134 \| 1.4923 \| 0.2 \| \| 1.5726 \| 43.0 \| 1161 \| 1.4923 \| 0.2 \| \| 1.4926 \| 44.0 \| 1188 \| 1.4922 \| 0.2 \| \| 1.4915 \| 45.0 \| 1215 \| 1.4922 \| 0.2 \| \| 1.4934 \| 46.0 \| 1242 \| 1.4921 \| 0.2 \| \| 1.5214 \| 47.0 \| 1269 \| 1.4921 \| 0.2 \| \| 1.5071 \| 48.0 \| 1296 \| 1.4921 \| 0.2 \| \| 1.5711 \| 49.0 \| 1323 \| 1.4921 \| 0.2 \| \| 1.4665 \| 50.0 \| 1350 \| 1.4921 \| 0.2 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_00001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_00001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5763 - Accuracy: 0.2093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5114 \| 1.0 \| 28 \| 1.6002 \| 0.1860 \| \| 1.516 \| 2.0 \| 56 \| 1.5991 \| 0.2093 \| \| 1.448 \| 3.0 \| 84 \| 1.5981 \| 0.2093 \| \| 1.5009 \| 4.0 \| 112 \| 1.5971 \| 0.2093 \| \| 1.646 \| 5.0 \| 140 \| 1.5960 \| 0.2093 \| \| 1.55 \| 6.0 \| 168 \| 1.5950 \| 0.2093 \| \| 1.5383 \| 7.0 \| 196 \| 1.5941 \| 0.2093 \| \| 1.4892 \| 8.0 \| 224 \| 1.5932 \| 0.2093 \| \| 1.5021 \| 9.0 \| 252 \| 1.5922 \| 0.2093 \| \| 1.5057 \| 10.0 \| 280 \| 1.5913 \| 0.2093 \| \| 1.5326 \| 11.0 \| 308 \| 1.5905 \| 0.2093 \| \| 1.5462 \| 12.0 \| 336 \| 1.5897 \| 0.2093 \| \| 1.5032 \| 13.0 \| 364 \| 1.5889 \| 0.2093 \| \| 1.4978 \| 14.0 \| 392 \| 1.5881 \| 0.2093 \| \| 1.4757 \| 15.0 \| 420 \| 1.5874 \| 0.2093 \| \| 1.4813 \| 16.0 \| 448 \| 1.5867 \| 0.2093 \| \| 1.4635 \| 17.0 \| 476 \| 1.5860 \| 0.2093 \| \| 1.511 \| 18.0 \| 504 \| 1.5853 \| 0.2093 \| \| 1.4905 \| 19.0 \| 532 \| 1.5847 \| 0.2093 \| \| 1.4302 \| 20.0 \| 560 \| 1.5841 \| 0.2093 \| \| 1.5108 \| 21.0 \| 588 \| 1.5835 \| 0.2093 \| \| 1.504 \| 22.0 \| 616 \| 1.5829 \| 0.2093 \| \| 1.4924 \| 23.0 \| 644 \| 1.5824 \| 0.2093 \| \| 1.5112 \| 24.0 \| 672 \| 1.5819 \| 0.2093 \| \| 1.5256 \| 25.0 \| 700 \| 1.5814 \| 0.2093 \| \| 1.4493 \| 26.0 \| 728 \| 1.5809 \| 0.2093 \| \| 1.4989 \| 27.0 \| 756 \| 1.5805 \| 0.2093 \| \| 1.5477 \| 28.0 \| 784 \| 1.5801 \| 0.2093 \| \| 1.5116 \| 29.0 \| 812 \| 1.5797 \| 0.2093 \| \| 1.4275 \| 30.0 \| 840 \| 1.5793 \| 0.2093 \| \| 1.453 \| 31.0 \| 868 \| 1.5790 \| 0.2093 \| \| 1.524 \| 32.0 \| 896 \| 1.5786 \| 0.2093 \| \| 1.5026 \| 33.0 \| 924 \| 1.5783 \| 0.2093 \| \| 1.4458 \| 34.0 \| 952 \| 1.5780 \| 0.2093 \| \| 1.4708 \| 35.0 \| 980 \| 1.5778 \| 0.2093 \| \| 1.5117 \| 36.0 \| 1008 \| 1.5776 \| 0.2093 \| \| 1.5594 \| 37.0 \| 1036 \| 1.5773 \| 0.2093 \| \| 1.5028 \| 38.0 \| 1064 \| 1.5771 \| 0.2093 \| \| 1.4691 \| 39.0 \| 1092 \| 1.5770 \| 0.2093 \| \| 1.5214 \| 40.0 \| 1120 \| 1.5768 \| 0.2093 \| \| 1.5285 \| 41.0 \| 1148 \| 1.5767 \| 0.2093 \| \| 1.4667 \| 42.0 \| 1176 \| 1.5766 \| 0.2093 \| \| 1.4652 \| 43.0 \| 1204 \| 1.5765 \| 0.2093 \| \| 1.4952 \| 44.0 \| 1232 \| 1.5765 \| 0.2093 \| \| 1.4825 \| 45.0 \| 1260 \| 1.5764 \| 0.2093 \| \| 1.4816 \| 46.0 \| 1288 \| 1.5764 \| 0.2093 \| \| 1.4911 \| 47.0 \| 1316 \| 1.5764 \| 0.2093 \| \| 1.5027 \| 48.0 \| 1344 \| 1.5763 \| 0.2093 \| \| 1.4424 \| 49.0 \| 1372 \| 1.5763 \| 0.2093 \| \| 1.4881 \| 50.0 \| 1400 \| 1.5763 \| 0.2093 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-tiny_tobacco3482_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7298 - Accuracy: 0.8 - Brier Loss: 0.3356 - Nll: 1.1950 - F1 Micro: 0.8000 - F1 Macro: 0.7677 - Ece: 0.2868 - Aurc: 0.0614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 1.0044 \| 0.11 \| 0.8970 \| 5.3755 \| 0.11 \| 0.0297 \| 0.1810 \| 0.9082 \| \| No log \| 2.0 \| 100 \| 0.9997 \| 0.27 \| 0.8946 \| 5.6759 \| 0.27 \| 0.1038 \| 0.2752 \| 0.7229 \| \| No log \| 3.0 \| 150 \| 0.9946 \| 0.345 \| 0.8902 \| 4.6234 \| 0.345 \| 0.1969 \| 0.3377 \| 0.6577 \| \| No log \| 4.0 \| 200 \| 0.9814 \| 0.4 \| 0.8686 \| 3.0912 \| 0.4000 \| 0.2605 \| 0.3687 \| 0.3808 \| \| No log \| 5.0 \| 250 \| 0.9618 \| 0.56 \| 0.8277 \| 2.9065 \| 0.56 \| 0.4439 \| 0.4769 \| 0.2239 \| \| No log \| 6.0 \| 300 \| 0.9225 \| 0.58 \| 0.7429 \| 2.5647 \| 0.58 \| 0.4408 \| 0.4561 \| 0.1944 \| \| No log \| 7.0 \| 350 \| 0.8843 \| 0.705 \| 0.6414 \| 2.4145 \| 0.705 \| 0.5531 \| 0.4493 \| 0.1261 \| \| No log \| 8.0 \| 400 \| 0.8627 \| 0.685 \| 0.5773 \| 2.4171 \| 0.685 \| 0.5755 \| 0.3710 \| 0.1378 \| \| No log \| 9.0 \| 450 \| 0.8252 \| 0.73 \| 0.5158 \| 1.6133 \| 0.7300 \| 0.6403 \| 0.3706 \| 0.1066 \| \| 0.9306 \| 10.0 \| 500 \| 0.8164 \| 0.74 \| 0.4861 \| 1.9299 \| 0.74 \| 0.6672 \| 0.3352 \| 0.1090 \| \| 0.9306 \| 11.0 \| 550 \| 0.8350 \| 0.67 \| 0.5078 \| 2.0291 \| 0.67 \| 0.6083 \| 0.3271 \| 0.1514 \| \| 0.9306 \| 12.0 \| 600 \| 0.8089 \| 0.695 \| 0.4680 \| 1.6726 \| 0.695 \| 0.6065 \| 0.3049 \| 0.1040 \| \| 0.9306 \| 13.0 \| 650 \| 0.7847 \| 0.78 \| 0.4097 \| 1.3710 \| 0.78 \| 0.7067 \| 0.3090 \| 0.0825 \| \| 0.9306 \| 14.0 \| 700 \| 0.7793 \| 0.8 \| 0.3952 \| 1.4382 \| 0.8000 \| 0.7351 \| 0.3189 \| 0.0823 \| \| 0.9306 \| 15.0 \| 750 \| 0.7756 \| 0.775 \| 0.3979 \| 1.2640 \| 0.775 \| 0.6997 \| 0.2950 \| 0.0835 \| \| 0.9306 \| 16.0 \| 800 \| 0.7888 \| 0.765 \| 0.3927 \| 1.2499 \| 0.765 \| 0.6894 \| 0.3175 \| 0.0719 \| \| 0.9306 \| 17.0 \| 850 \| 0.7596 \| 0.795 \| 0.3603 \| 1.1834 \| 0.795 \| 0.7250 \| 0.2930 \| 0.0673 \| \| 0.9306 \| 18.0 \| 900 \| 0.7581 \| 0.795 \| 0.3580 \| 1.1902 \| 0.795 \| 0.7241 \| 0.3104 \| 0.0665 \| \| 0.9306 \| 19.0 \| 950 \| 0.7546 \| 0.81 \| 0.3547 \| 1.1055 \| 0.81 \| 0.7583 \| 0.3024 \| 0.0621 \| \| 0.7329 \| 20.0 \| 1000 \| 0.7520 \| 0.81 \| 0.3547 \| 1.1284 \| 0.81 \| 0.7533 \| 0.3209 \| 0.0581 \| \| 0.7329 \| 21.0 \| 1050 \| 0.7669 \| 0.775 \| 0.3906 \| 1.3812 \| 0.775 \| 0.7502 \| 0.3212 \| 0.0794 \| \| 0.7329 \| 22.0 \| 1100 \| 0.7532 \| 0.81 \| 0.3591 \| 1.0982 \| 0.81 \| 0.7836 \| 0.3035 \| 0.0708 \| \| 0.7329 \| 23.0 \| 1150 \| 0.7519 \| 0.805 \| 0.3643 \| 1.0628 \| 0.805 \| 0.7742 \| 0.2813 \| 0.0732 \| \| 0.7329 \| 24.0 \| 1200 \| 0.7494 \| 0.795 \| 0.3614 \| 1.1123 \| 0.795 \| 0.7618 \| 0.2988 \| 0.0699 \| \| 0.7329 \| 25.0 \| 1250 \| 0.7517 \| 0.79 \| 0.3696 \| 1.0703 \| 0.79 \| 0.7606 \| 0.3081 \| 0.0800 \| \| 0.7329 \| 26.0 \| 1300 \| 0.7513 \| 0.795 \| 0.3629 \| 1.1020 \| 0.795 \| 0.7769 \| 0.2797 \| 0.0722 \| \| 0.7329 \| 27.0 \| 1350 \| 0.7485 \| 0.795 \| 0.3552 \| 1.0352 \| 0.795 \| 0.7671 \| 0.2678 \| 0.0684 \| \| 0.7329 \| 28.0 \| 1400 \| 0.7442 \| 0.805 \| 0.3471 \| 1.0956 \| 0.805 \| 0.7706 \| 0.2807 \| 0.0630 \| \| 0.7329 \| 29.0 \| 1450 \| 0.7473 \| 0.795 \| 0.3592 \| 1.1204 \| 0.795 \| 0.7685 \| 0.2897 \| 0.0722 \| \| 0.6917 \| 30.0 \| 1500 \| 0.7449 \| 0.815 \| 0.3482 \| 1.0584 \| 0.815 \| 0.7862 \| 0.2949 \| 0.0629 \| \| 0.6917 \| 31.0 \| 1550 \| 0.7443 \| 0.8 \| 0.3512 \| 1.1010 \| 0.8000 \| 0.7770 \| 0.2954 \| 0.0622 \| \| 0.6917 \| 32.0 \| 1600 \| 0.7454 \| 0.785 \| 0.3543 \| 1.0994 \| 0.785 \| 0.7631 \| 0.2957 \| 0.0639 \| \| 0.6917 \| 33.0 \| 1650 \| 0.7421 \| 0.815 \| 0.3449 \| 1.1826 \| 0.815 \| 0.7853 \| 0.2996 \| 0.0592 \| \| 0.6917 \| 34.0 \| 1700 \| 0.7454 \| 0.79 \| 0.3559 \| 1.1000 \| 0.79 \| 0.7597 \| 0.2964 \| 0.0659 \| \| 0.6917 \| 35.0 \| 1750 \| 0.7418 \| 0.815 \| 0.3477 \| 1.1616 \| 0.815 \| 0.7867 \| 0.3133 \| 0.0617 \| \| 0.6917 \| 36.0 \| 1800 \| 0.7425 \| 0.815 \| 0.3464 \| 1.1274 \| 0.815 \| 0.7949 \| 0.3173 \| 0.0578 \| \| 0.6917 \| 37.0 \| 1850 \| 0.7421 \| 0.8 \| 0.3448 \| 1.1909 \| 0.8000 \| 0.7732 \| 0.2900 \| 0.0639 \| \| 0.6917 \| 38.0 \| 1900 \| 0.7415 \| 0.795 \| 0.3471 \| 1.1816 \| 0.795 \| 0.7594 \| 0.2860 \| 0.0655 \| \| 0.6917 \| 39.0 \| 1950 \| 0.7405 \| 0.78 \| 0.3502 \| 1.1084 \| 0.78 \| 0.7491 \| 0.2709 \| 0.0650 \| \| 0.6764 \| 40.0 \| 2000 \| 0.7398 \| 0.81 \| 0.3457 \| 1.1746 \| 0.81 \| 0.7797 \| 0.2973 \| 0.0603 \| \| 0.6764 \| 41.0 \| 2050 \| 0.7394 \| 0.805 \| 0.3437 \| 1.1201 \| 0.805 \| 0.7764 \| 0.2915 \| 0.0626 \| \| 0.6764 \| 42.0 \| 2100 \| 0.7380 \| 0.81 \| 0.3420 \| 1.0987 \| 0.81 \| 0.7861 \| 0.2815 \| 0.0583 \| \| 0.6764 \| 43.0 \| 2150 \| 0.7386 \| 0.8 \| 0.3437 \| 1.1855 \| 0.8000 \| 0.7667 \| 0.2804 \| 0.0617 \| \| 0.6764 \| 44.0 \| 2200 \| 0.7398 \| 0.795 \| 0.3437 \| 1.1138 \| 0.795 \| 0.7660 \| 0.2719 \| 0.0614 \| \| 0.6764 \| 45.0 \| 2250 \| 0.7384 \| 0.805 \| 0.3441 \| 1.1100 \| 0.805 \| 0.7699 \| 0.3065 \| 0.0628 \| \| 0.6764 \| 46.0 \| 2300 \| 0.7389 \| 0.79 \| 0.3488 \| 1.1079 \| 0.79 \| 0.7552 \| 0.2615 \| 0.0647 \| \| 0.6764 \| 47.0 \| 2350 \| 0.7368 \| 0.8 \| 0.3440 \| 1.1095 \| 0.8000 \| 0.7698 \| 0.2908 \| 0.0624 \| \| 0.6764 \| 48.0 \| 2400 \| 0.7365 \| 0.8 \| 0.3452 \| 1.0995 \| 0.8000 \| 0.7739 \| 0.2838 \| 0.0645 \| \| 0.6764 \| 49.0 \| 2450 \| 0.7365 \| 0.8 \| 0.3367 \| 1.0442 \| 0.8000 \| 0.7712 \| 0.2735 \| 0.0585 \| \| 0.6662 \| 50.0 \| 2500 \| 0.7342 \| 0.815 \| 0.3379 \| 1.1009 \| 0.815 \| 0.7815 \| 0.2964 \| 0.0584 \| \| 0.6662 \| 51.0 \| 2550 \| 0.7340 \| 0.805 \| 0.3358 \| 1.0985 \| 0.805 \| 0.7723 \| 0.2635 \| 0.0593 \| \| 0.6662 \| 52.0 \| 2600 \| 0.7370 \| 0.8 \| 0.3429 \| 1.1227 \| 0.8000 \| 0.7709 \| 0.2841 \| 0.0603 \| \| 0.6662 \| 53.0 \| 2650 \| 0.7325 \| 0.81 \| 0.3380 \| 1.1110 \| 0.81 \| 0.7790 \| 0.3022 \| 0.0601 \| \| 0.6662 \| 54.0 \| 2700 \| 0.7320 \| 0.8 \| 0.3363 \| 1.0621 \| 0.8000 \| 0.7647 \| 0.2815 \| 0.0607 \| \| 0.6662 \| 55.0 \| 2750 \| 0.7324 \| 0.805 \| 0.3321 \| 0.9926 \| 0.805 \| 0.7693 \| 0.2972 \| 0.0600 \| \| 0.6662 \| 56.0 \| 2800 \| 0.7318 \| 0.805 \| 0.3364 \| 1.0537 \| 0.805 \| 0.7681 \| 0.2554 \| 0.0612 \| \| 0.6662 \| 57.0 \| 2850 \| 0.7311 \| 0.82 \| 0.3355 \| 1.1133 \| 0.82 \| 0.7862 \| 0.2776 \| 0.0594 \| \| 0.6662 \| 58.0 \| 2900 \| 0.7317 \| 0.81 \| 0.3331 \| 1.0662 \| 0.81 \| 0.7797 \| 0.2600 \| 0.0579 \| \| 0.6662 \| 59.0 \| 2950 \| 0.7327 \| 0.805 \| 0.3382 \| 1.1876 \| 0.805 \| 0.7735 \| 0.2797 \| 0.0621 \| \| 0.6577 \| 60.0 \| 3000 \| 0.7322 \| 0.8 \| 0.3356 \| 1.1864 \| 0.8000 \| 0.7680 \| 0.2797 \| 0.0612 \| \| 0.6577 \| 61.0 \| 3050 \| 0.7327 \| 0.795 \| 0.3391 \| 1.1347 \| 0.795 \| 0.7614 \| 0.2883 \| 0.0641 \| \| 0.6577 \| 62.0 \| 3100 \| 0.7315 \| 0.815 \| 0.3364 \| 1.1227 \| 0.815 \| 0.7848 \| 0.2681 \| 0.0599 \| \| 0.6577 \| 63.0 \| 3150 \| 0.7316 \| 0.805 \| 0.3392 \| 1.0608 \| 0.805 \| 0.7717 \| 0.2742 \| 0.0632 \| \| 0.6577 \| 64.0 \| 3200 \| 0.7313 \| 0.82 \| 0.3341 \| 1.0601 \| 0.82 \| 0.7878 \| 0.2950 \| 0.0583 \| \| 0.6577 \| 65.0 \| 3250 \| 0.7322 \| 0.805 \| 0.3388 \| 1.1837 \| 0.805 \| 0.7747 \| 0.2806 \| 0.0638 \| \| 0.6577 \| 66.0 \| 3300 \| 0.7311 \| 0.805 \| 0.3373 \| 1.0157 \| 0.805 \| 0.7757 \| 0.2880 \| 0.0629 \| \| 0.6577 \| 67.0 \| 3350 \| 0.7310 \| 0.805 \| 0.3344 \| 1.1878 \| 0.805 \| 0.7766 \| 0.2499 \| 0.0609 \| \| 0.6577 \| 68.0 \| 3400 \| 0.7326 \| 0.805 \| 0.3391 \| 1.0847 \| 0.805 \| 0.7729 \| 0.2824 \| 0.0636 \| \| 0.6577 \| 69.0 \| 3450 \| 0.7302 \| 0.805 \| 0.3376 \| 1.1932 \| 0.805 \| 0.7778 \| 0.2789 \| 0.0617 \| \| 0.6528 \| 70.0 \| 3500 \| 0.7305 \| 0.81 \| 0.3359 \| 0.9988 \| 0.81 \| 0.7787 \| 0.2769 \| 0.0622 \| \| 0.6528 \| 71.0 \| 3550 \| 0.7300 \| 0.81 \| 0.3328 \| 1.0833 \| 0.81 \| 0.7776 \| 0.2914 \| 0.0594 \| \| 0.6528 \| 72.0 \| 3600 \| 0.7300 \| 0.81 \| 0.3343 \| 1.1426 \| 0.81 \| 0.7776 \| 0.2843 \| 0.0594 \| \| 0.6528 \| 73.0 \| 3650 \| 0.7285 \| 0.805 \| 0.3341 \| 1.1237 \| 0.805 \| 0.7701 \| 0.2723 \| 0.0614 \| \| 0.6528 \| 74.0 \| 3700 \| 0.7303 \| 0.81 \| 0.3368 \| 1.1928 \| 0.81 \| 0.7768 \| 0.2926 \| 0.0612 \| \| 0.6528 \| 75.0 \| 3750 \| 0.7290 \| 0.805 \| 0.3318 \| 1.0669 \| 0.805 \| 0.7709 \| 0.2810 \| 0.0603 \| \| 0.6528 \| 76.0 \| 3800 \| 0.7316 \| 0.8 \| 0.3382 \| 1.1392 \| 0.8000 \| 0.7687 \| 0.2505 \| 0.0636 \| \| 0.6528 \| 77.0 \| 3850 \| 0.7284 \| 0.8 \| 0.3337 \| 1.1338 \| 0.8000 \| 0.7720 \| 0.2677 \| 0.0610 \| \| 0.6528 \| 78.0 \| 3900 \| 0.7303 \| 0.805 \| 0.3373 \| 1.1969 \| 0.805 \| 0.7729 \| 0.2745 \| 0.0618 \| \| 0.6528 \| 79.0 \| 3950 \| 0.7297 \| 0.805 \| 0.3369 \| 1.1970 \| 0.805 \| 0.7743 \| 0.2731 \| 0.0606 \| \| 0.6489 \| 80.0 \| 4000 \| 0.7296 \| 0.795 \| 0.3362 \| 1.1328 \| 0.795 \| 0.7656 \| 0.2620 \| 0.0627 \| \| 0.6489 \| 81.0 \| 4050 \| 0.7295 \| 0.805 \| 0.3363 \| 1.1358 \| 0.805 \| 0.7726 \| 0.2540 \| 0.0608 \| \| 0.6489 \| 82.0 \| 4100 \| 0.7290 \| 0.795 \| 0.3341 \| 1.1389 \| 0.795 \| 0.7668 \| 0.2661 \| 0.0630 \| \| 0.6489 \| 83.0 \| 4150 \| 0.7289 \| 0.8 \| 0.3364 \| 1.0597 \| 0.8000 \| 0.7678 \| 0.2838 \| 0.0615 \| \| 0.6489 \| 84.0 \| 4200 \| 0.7291 \| 0.805 \| 0.3351 \| 1.1277 \| 0.805 \| 0.7743 \| 0.2621 \| 0.0608 \| \| 0.6489 \| 85.0 \| 4250 \| 0.7297 \| 0.795 \| 0.3353 \| 1.1953 \| 0.795 \| 0.7668 \| 0.2666 \| 0.0622 \| \| 0.6489 \| 86.0 \| 4300 \| 0.7286 \| 0.805 \| 0.3339 \| 1.1278 \| 0.805 \| 0.7735 \| 0.2668 \| 0.0608 \| \| 0.6489 \| 87.0 \| 4350 \| 0.7298 \| 0.8 \| 0.3361 \| 1.1423 \| 0.8000 \| 0.7677 \| 0.2613 \| 0.0614 \| \| 0.6489 \| 88.0 \| 4400 \| 0.7296 \| 0.805 \| 0.3346 \| 1.1927 \| 0.805 \| 0.7743 \| 0.2789 \| 0.0612 \| \| 0.6489 \| 89.0 \| 4450 \| 0.7299 \| 0.8 \| 0.3359 \| 1.1950 \| 0.8000 \| 0.7686 \| 0.2500 \| 0.0613 \| \| 0.6462 \| 90.0 \| 4500 \| 0.7297 \| 0.805 \| 0.3354 \| 1.1934 \| 0.805 \| 0.7743 \| 0.2939 \| 0.0613 \| \| 0.6462 \| 91.0 \| 4550 \| 0.7294 \| 0.8 \| 0.3353 \| 1.1313 \| 0.8000 \| 0.7685 \| 0.2808 \| 0.0610 \| \| 0.6462 \| 92.0 \| 4600 \| 0.7297 \| 0.805 \| 0.3356 \| 1.1349 \| 0.805 \| 0.7765 \| 0.2668 \| 0.0614 \| \| 0.6462 \| 93.0 \| 4650 \| 0.7298 \| 0.8 \| 0.3354 \| 1.1954 \| 0.8000 \| 0.7685 \| 0.2700 \| 0.0613 \| \| 0.6462 \| 94.0 \| 4700 \| 0.7301 \| 0.8 \| 0.3362 \| 1.1951 \| 0.8000 \| 0.7677 \| 0.2722 \| 0.0616 \| \| 0.6462 \| 95.0 \| 4750 \| 0.7299 \| 0.805 \| 0.3360 \| 1.1957 \| 0.805 \| 0.7743 \| 0.2619 \| 0.0614 \| \| 0.6462 \| 96.0 \| 4800 \| 0.7299 \| 0.805 \| 0.3357 \| 1.1946 \| 0.805 \| 0.7743 \| 0.2892 \| 0.0611 \| \| 0.6462 \| 97.0 \| 4850 \| 0.7297 \| 0.8 \| 0.3355 \| 1.1954 \| 0.8000 \| 0.7686 \| 0.2703 \| 0.0613 \| \| 0.6462 \| 98.0 \| 4900 \| 0.7298 \| 0.8 \| 0.3359 \| 1.1952 \| 0.8000 \| 0.7677 \| 0.2892 \| 0.0615 \| \| 0.6462 \| 99.0 \| 4950 \| 0.7298 \| 0.8 \| 0.3357 \| 1.1951 \| 0.8000 \| 0.7677 \| 0.2720 \| 0.0614 \| \| 0.645 \| 100.0 \| 5000 \| 0.7298 \| 0.8 \| 0.3356 \| 1.1950 \| 0.8000 \| 0.7677 \| 0.2868 \| 0.0614 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-tiny_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 318.4368 - Accuracy: 0.805 - Brier Loss: 0.3825 - Nll: 1.1523 - F1 Micro: 0.805 - F1 Macro: 0.7673 - Ece: 0.2987 - Aurc: 0.0702 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 328.9614 \| 0.155 \| 0.8984 \| 7.4608 \| 0.155 \| 0.0353 \| 0.2035 \| 0.8760 \| \| No log \| 2.0 \| 14 \| 328.8199 \| 0.235 \| 0.8940 \| 6.4907 \| 0.235 \| 0.1148 \| 0.2643 \| 0.7444 \| \| No log \| 3.0 \| 21 \| 328.4224 \| 0.38 \| 0.8711 \| 2.8184 \| 0.38 \| 0.3279 \| 0.3440 \| 0.4817 \| \| No log \| 4.0 \| 28 \| 327.5357 \| 0.51 \| 0.8072 \| 2.0744 \| 0.51 \| 0.4221 \| 0.4111 \| 0.3319 \| \| No log \| 5.0 \| 35 \| 326.2037 \| 0.53 \| 0.6860 \| 2.0669 \| 0.53 \| 0.4313 \| 0.3619 \| 0.2744 \| \| No log \| 6.0 \| 42 \| 324.8763 \| 0.565 \| 0.6008 \| 1.9437 \| 0.565 \| 0.4477 \| 0.3009 \| 0.2469 \| \| No log \| 7.0 \| 49 \| 323.9205 \| 0.6 \| 0.5390 \| 1.7694 \| 0.6 \| 0.4647 \| 0.2365 \| 0.1978 \| \| No log \| 8.0 \| 56 \| 323.2227 \| 0.65 \| 0.4632 \| 1.7803 \| 0.65 \| 0.5195 \| 0.2313 \| 0.1422 \| \| No log \| 9.0 \| 63 \| 322.5265 \| 0.74 \| 0.4177 \| 1.7538 \| 0.74 \| 0.6302 \| 0.2442 \| 0.1113 \| \| No log \| 10.0 \| 70 \| 322.1928 \| 0.705 \| 0.4013 \| 1.5880 \| 0.705 \| 0.5864 \| 0.2147 \| 0.1118 \| \| No log \| 11.0 \| 77 \| 322.2687 \| 0.795 \| 0.4006 \| 1.2854 \| 0.795 \| 0.7476 \| 0.2719 \| 0.0942 \| \| No log \| 12.0 \| 84 \| 321.6652 \| 0.725 \| 0.3754 \| 1.3462 \| 0.7250 \| 0.6521 \| 0.2238 \| 0.0920 \| \| No log \| 13.0 \| 91 \| 322.3688 \| 0.785 \| 0.3951 \| 1.3209 \| 0.785 \| 0.7260 \| 0.2712 \| 0.0805 \| \| No log \| 14.0 \| 98 \| 321.7083 \| 0.72 \| 0.3915 \| 1.4854 \| 0.72 \| 0.6220 \| 0.1963 \| 0.0986 \| \| No log \| 15.0 \| 105 \| 321.6171 \| 0.8 \| 0.3614 \| 1.3397 \| 0.8000 \| 0.7427 \| 0.2531 \| 0.0741 \| \| No log \| 16.0 \| 112 \| 321.0427 \| 0.77 \| 0.3502 \| 1.1461 \| 0.7700 \| 0.7082 \| 0.1976 \| 0.0769 \| \| No log \| 17.0 \| 119 \| 321.1529 \| 0.735 \| 0.3827 \| 1.5751 \| 0.735 \| 0.6769 \| 0.1926 \| 0.0973 \| \| No log \| 18.0 \| 126 \| 321.0808 \| 0.78 \| 0.3611 \| 1.2529 \| 0.78 \| 0.7199 \| 0.2242 \| 0.0762 \| \| No log \| 19.0 \| 133 \| 321.6684 \| 0.795 \| 0.3835 \| 1.1789 \| 0.795 \| 0.7506 \| 0.2823 \| 0.0712 \| \| No log \| 20.0 \| 140 \| 321.2322 \| 0.78 \| 0.3682 \| 1.1715 \| 0.78 \| 0.7356 \| 0.2532 \| 0.0752 \| \| No log \| 21.0 \| 147 \| 320.4927 \| 0.795 \| 0.3458 \| 1.3764 \| 0.795 \| 0.7504 \| 0.2178 \| 0.0710 \| \| No log \| 22.0 \| 154 \| 320.8896 \| 0.8 \| 0.3568 \| 1.0908 \| 0.8000 \| 0.7536 \| 0.2709 \| 0.0677 \| \| No log \| 23.0 \| 161 \| 320.9060 \| 0.785 \| 0.3774 \| 1.1571 \| 0.785 \| 0.7414 \| 0.2712 \| 0.0719 \| \| No log \| 24.0 \| 168 \| 320.9026 \| 0.795 \| 0.3718 \| 1.0871 \| 0.795 \| 0.7465 \| 0.2718 \| 0.0690 \| \| No log \| 25.0 \| 175 \| 320.7932 \| 0.805 \| 0.3601 \| 1.0998 \| 0.805 \| 0.7699 \| 0.2620 \| 0.0614 \| \| No log \| 26.0 \| 182 \| 321.2285 \| 0.735 \| 0.4164 \| 1.8530 \| 0.735 \| 0.7051 \| 0.2814 \| 0.0889 \| \| No log \| 27.0 \| 189 \| 320.8364 \| 0.775 \| 0.4028 \| 1.4063 \| 0.775 \| 0.7412 \| 0.2687 \| 0.0836 \| \| No log \| 28.0 \| 196 \| 320.0800 \| 0.785 \| 0.3548 \| 1.2123 \| 0.785 \| 0.7394 \| 0.2055 \| 0.0740 \| \| No log \| 29.0 \| 203 \| 319.9995 \| 0.79 \| 0.3526 \| 1.2296 \| 0.79 \| 0.7381 \| 0.2363 \| 0.0691 \| \| No log \| 30.0 \| 210 \| 320.0685 \| 0.795 \| 0.3588 \| 1.2765 \| 0.795 \| 0.7447 \| 0.2310 \| 0.0725 \| \| No log \| 31.0 \| 217 \| 320.0981 \| 0.805 \| 0.3699 \| 1.0128 \| 0.805 \| 0.7690 \| 0.2868 \| 0.0701 \| \| No log \| 32.0 \| 224 \| 320.5063 \| 0.8 \| 0.3900 \| 1.1437 \| 0.8000 \| 0.7650 \| 0.3141 \| 0.0679 \| \| No log \| 33.0 \| 231 \| 319.8609 \| 0.795 \| 0.3549 \| 1.2051 \| 0.795 \| 0.7526 \| 0.2485 \| 0.0697 \| \| No log \| 34.0 \| 238 \| 319.6974 \| 0.81 \| 0.3600 \| 1.0124 \| 0.81 \| 0.7724 \| 0.2671 \| 0.0672 \| \| No log \| 35.0 \| 245 \| 319.5988 \| 0.795 \| 0.3513 \| 1.1480 \| 0.795 \| 0.7540 \| 0.2425 \| 0.0679 \| \| No log \| 36.0 \| 252 \| 319.6317 \| 0.8 \| 0.3544 \| 1.2190 \| 0.8000 \| 0.7607 \| 0.2449 \| 0.0674 \| \| No log \| 37.0 \| 259 \| 319.6821 \| 0.81 \| 0.3531 \| 1.0714 \| 0.81 \| 0.7672 \| 0.2590 \| 0.0662 \| \| No log \| 38.0 \| 266 \| 319.7618 \| 0.805 \| 0.3754 \| 1.0421 \| 0.805 \| 0.7625 \| 0.2973 \| 0.0701 \| \| No log \| 39.0 \| 273 \| 319.9920 \| 0.775 \| 0.3843 \| 1.0821 \| 0.775 \| 0.7374 \| 0.2801 \| 0.0723 \| \| No log \| 40.0 \| 280 \| 319.3407 \| 0.765 \| 0.3633 \| 1.2213 \| 0.765 \| 0.7041 \| 0.2274 \| 0.0767 \| \| No log \| 41.0 \| 287 \| 319.2732 \| 0.765 \| 0.3696 \| 1.2638 \| 0.765 \| 0.7184 \| 0.2315 \| 0.0835 \| \| No log \| 42.0 \| 294 \| 319.5948 \| 0.805 \| 0.3685 \| 1.0782 \| 0.805 \| 0.7625 \| 0.2678 \| 0.0661 \| \| No log \| 43.0 \| 301 \| 319.7181 \| 0.8 \| 0.3776 \| 1.0004 \| 0.8000 \| 0.7507 \| 0.2598 \| 0.0672 \| \| No log \| 44.0 \| 308 \| 319.1170 \| 0.77 \| 0.3619 \| 1.2129 \| 0.7700 \| 0.7159 \| 0.2557 \| 0.0787 \| \| No log \| 45.0 \| 315 \| 319.5949 \| 0.8 \| 0.3809 \| 1.1448 \| 0.8000 \| 0.7670 \| 0.2868 \| 0.0688 \| \| No log \| 46.0 \| 322 \| 319.0327 \| 0.79 \| 0.3675 \| 1.2386 \| 0.79 \| 0.7315 \| 0.2546 \| 0.0790 \| \| No log \| 47.0 \| 329 \| 319.3806 \| 0.805 \| 0.3665 \| 1.1368 \| 0.805 \| 0.7620 \| 0.2737 \| 0.0700 \| \| No log \| 48.0 \| 336 \| 319.4999 \| 0.795 \| 0.3836 \| 1.0256 \| 0.795 \| 0.7550 \| 0.2800 \| 0.0748 \| \| No log \| 49.0 \| 343 \| 319.2553 \| 0.8 \| 0.3660 \| 1.2011 \| 0.8000 \| 0.7573 \| 0.2698 \| 0.0679 \| \| No log \| 50.0 \| 350 \| 319.3495 \| 0.805 \| 0.3836 \| 1.1055 \| 0.805 \| 0.7634 \| 0.3004 \| 0.0671 \| \| No log \| 51.0 \| 357 \| 319.1643 \| 0.8 \| 0.3660 \| 1.1980 \| 0.8000 \| 0.7497 \| 0.2641 \| 0.0709 \| \| No log \| 52.0 \| 364 \| 319.1483 \| 0.795 \| 0.3651 \| 1.0776 \| 0.795 \| 0.7561 \| 0.2856 \| 0.0683 \| \| No log \| 53.0 \| 371 \| 319.0104 \| 0.79 \| 0.3724 \| 1.1653 \| 0.79 \| 0.7422 \| 0.2512 \| 0.0724 \| \| No log \| 54.0 \| 378 \| 319.1622 \| 0.795 \| 0.3814 \| 1.2807 \| 0.795 \| 0.7456 \| 0.2644 \| 0.0759 \| \| No log \| 55.0 \| 385 \| 319.1554 \| 0.8 \| 0.3694 \| 1.2710 \| 0.8000 \| 0.7570 \| 0.2877 \| 0.0667 \| \| No log \| 56.0 \| 392 \| 319.2158 \| 0.79 \| 0.3795 \| 1.1678 \| 0.79 \| 0.7509 \| 0.2942 \| 0.0692 \| \| No log \| 57.0 \| 399 \| 319.1813 \| 0.795 \| 0.3839 \| 1.1243 \| 0.795 \| 0.7529 \| 0.2835 \| 0.0733 \| \| No log \| 58.0 \| 406 \| 318.7599 \| 0.81 \| 0.3632 \| 1.1484 \| 0.81 \| 0.7738 \| 0.3030 \| 0.0691 \| \| No log \| 59.0 \| 413 \| 319.0827 \| 0.805 \| 0.3792 \| 1.2070 \| 0.805 \| 0.7685 \| 0.2901 \| 0.0674 \| \| No log \| 60.0 \| 420 \| 318.6928 \| 0.805 \| 0.3661 \| 1.1517 \| 0.805 \| 0.7534 \| 0.2492 \| 0.0719 \| \| No log \| 61.0 \| 427 \| 318.8309 \| 0.805 \| 0.3714 \| 1.2785 \| 0.805 \| 0.7517 \| 0.2674 \| 0.0699 \| \| No log \| 62.0 \| 434 \| 318.9468 \| 0.8 \| 0.3794 \| 1.1549 \| 0.8000 \| 0.7566 \| 0.2862 \| 0.0707 \| \| No log \| 63.0 \| 441 \| 318.8059 \| 0.785 \| 0.3774 \| 1.2460 \| 0.785 \| 0.7487 \| 0.2721 \| 0.0752 \| \| No log \| 64.0 \| 448 \| 318.7155 \| 0.81 \| 0.3659 \| 1.1963 \| 0.81 \| 0.7660 \| 0.2676 \| 0.0680 \| \| No log \| 65.0 \| 455 \| 318.8439 \| 0.795 \| 0.3799 \| 1.0230 \| 0.795 \| 0.7464 \| 0.2797 \| 0.0700 \| \| No log \| 66.0 \| 462 \| 318.7784 \| 0.79 \| 0.3783 \| 1.3168 \| 0.79 \| 0.7503 \| 0.2618 \| 0.0804 \| \| No log \| 67.0 \| 469 \| 318.9019 \| 0.795 \| 0.3802 \| 1.2003 \| 0.795 \| 0.7503 \| 0.2934 \| 0.0702 \| \| No log \| 68.0 \| 476 \| 318.6647 \| 0.8 \| 0.3728 \| 1.1395 \| 0.8000 \| 0.7590 \| 0.2718 \| 0.0699 \| \| No log \| 69.0 \| 483 \| 318.3780 \| 0.8 \| 0.3688 \| 1.2812 \| 0.8000 \| 0.7602 \| 0.2690 \| 0.0728 \| \| No log \| 70.0 \| 490 \| 318.8004 \| 0.8 \| 0.3779 \| 1.0682 \| 0.8000 \| 0.7607 \| 0.2887 \| 0.0682 \| \| No log \| 71.0 \| 497 \| 318.7021 \| 0.8 \| 0.3748 \| 1.1101 \| 0.8000 \| 0.7545 \| 0.2977 \| 0.0691 \| \| 322.4844 \| 72.0 \| 504 \| 318.3595 \| 0.79 \| 0.3779 \| 1.2333 \| 0.79 \| 0.7386 \| 0.2617 \| 0.0843 \| \| 322.4844 \| 73.0 \| 511 \| 318.5725 \| 0.805 \| 0.3740 \| 1.2108 \| 0.805 \| 0.7674 \| 0.2762 \| 0.0677 \| \| 322.4844 \| 74.0 \| 518 \| 318.7131 \| 0.81 \| 0.3822 \| 1.2048 \| 0.81 \| 0.7660 \| 0.2971 \| 0.0696 \| \| 322.4844 \| 75.0 \| 525 \| 318.6258 \| 0.775 \| 0.3806 \| 1.1511 \| 0.775 \| 0.7228 \| 0.2824 \| 0.0743 \| \| 322.4844 \| 76.0 \| 532 \| 318.5414 \| 0.8 \| 0.3746 \| 1.2136 \| 0.8000 \| 0.7563 \| 0.2872 \| 0.0708 \| \| 322.4844 \| 77.0 \| 539 \| 318.5404 \| 0.795 \| 0.3765 \| 1.1414 \| 0.795 \| 0.7551 \| 0.2905 \| 0.0707 \| \| 322.4844 \| 78.0 \| 546 \| 318.5820 \| 0.8 \| 0.3806 \| 1.1653 \| 0.8000 \| 0.7573 \| 0.2888 \| 0.0707 \| \| 322.4844 \| 79.0 \| 553 \| 318.5909 \| 0.8 \| 0.3838 \| 1.2343 \| 0.8000 \| 0.7563 \| 0.2778 \| 0.0754 \| \| 322.4844 \| 80.0 \| 560 \| 318.6398 \| 0.795 \| 0.3874 \| 1.1097 \| 0.795 \| 0.7520 \| 0.3045 \| 0.0727 \| \| 322.4844 \| 81.0 \| 567 \| 318.6250 \| 0.795 \| 0.3860 \| 1.1612 \| 0.795 \| 0.7542 \| 0.3079 \| 0.0727 \| \| 322.4844 \| 82.0 \| 574 \| 318.5269 \| 0.795 \| 0.3825 \| 1.2812 \| 0.795 \| 0.7451 \| 0.2723 \| 0.0737 \| \| 322.4844 \| 83.0 \| 581 \| 318.5790 \| 0.795 \| 0.3846 \| 1.1575 \| 0.795 \| 0.7455 \| 0.2984 \| 0.0723 \| \| 322.4844 \| 84.0 \| 588 \| 318.4343 \| 0.795 \| 0.3826 \| 1.2088 \| 0.795 \| 0.7532 \| 0.2852 \| 0.0746 \| \| 322.4844 \| 85.0 \| 595 \| 318.3853 \| 0.795 \| 0.3792 \| 1.2784 \| 0.795 \| 0.7456 \| 0.3003 \| 0.0729 \| \| 322.4844 \| 86.0 \| 602 \| 318.5143 \| 0.805 \| 0.3854 \| 1.1745 \| 0.805 \| 0.7636 \| 0.3071 \| 0.0705 \| \| 322.4844 \| 87.0 \| 609 \| 318.3533 \| 0.805 \| 0.3763 \| 1.1579 \| 0.805 \| 0.7679 \| 0.2805 \| 0.0694 \| \| 322.4844 \| 88.0 \| 616 \| 318.4745 \| 0.795 \| 0.3860 \| 1.0964 \| 0.795 \| 0.7539 \| 0.2952 \| 0.0712 \| \| 322.4844 \| 89.0 \| 623 \| 318.4909 \| 0.805 \| 0.3829 \| 1.1544 \| 0.805 \| 0.7673 \| 0.3035 \| 0.0700 \| \| 322.4844 \| 90.0 \| 630 \| 318.4910 \| 0.8 \| 0.3828 \| 1.1537 \| 0.8000 \| 0.7497 \| 0.2730 \| 0.0717 \| \| 322.4844 \| 91.0 \| 637 \| 318.5176 \| 0.8 \| 0.3855 \| 1.1613 \| 0.8000 \| 0.7552 \| 0.2815 \| 0.0718 \| \| 322.4844 \| 92.0 \| 644 \| 318.4100 \| 0.795 \| 0.3810 \| 1.2215 \| 0.795 \| 0.7532 \| 0.2696 \| 0.0731 \| \| 322.4844 \| 93.0 \| 651 \| 318.3500 \| 0.805 \| 0.3765 \| 1.2181 \| 0.805 \| 0.7702 \| 0.2790 \| 0.0705 \| \| 322.4844 \| 94.0 \| 658 \| 318.3257 \| 0.805 \| 0.3785 \| 1.2218 \| 0.805 \| 0.7678 \| 0.3114 \| 0.0704 \| \| 322.4844 \| 95.0 \| 665 \| 318.3990 \| 0.8 \| 0.3823 \| 1.1485 \| 0.8000 \| 0.7585 \| 0.2901 \| 0.0710 \| \| 322.4844 \| 96.0 \| 672 \| 318.5006 \| 0.81 \| 0.3862 \| 1.1518 \| 0.81 \| 0.7724 \| 0.2925 \| 0.0698 \| \| 322.4844 \| 97.0 \| 679 \| 318.3142 \| 0.8 \| 0.3780 \| 1.1608 \| 0.8000 \| 0.7557 \| 0.2916 \| 0.0716 \| \| 322.4844 \| 98.0 \| 686 \| 318.3767 \| 0.795 \| 0.3819 \| 1.2208 \| 0.795 \| 0.7526 \| 0.2764 \| 0.0731 \| \| 322.4844 \| 99.0 \| 693 \| 318.4233 \| 0.8 \| 0.3810 \| 1.1532 \| 0.8000 \| 0.7557 \| 0.2786 \| 0.0706 \| \| 322.4844 \| 100.0 \| 700 \| 318.4368 \| 0.805 \| 0.3825 \| 1.1523 \| 0.805 \| 0.7673 \| 0.2987 \| 0.0702 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_small_sgd_00001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_00001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4657 - Accuracy: 0.2857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5422 \| 1.0 \| 28 \| 1.4834 \| 0.2619 \| \| 1.6237 \| 2.0 \| 56 \| 1.4826 \| 0.2619 \| \| 1.5562 \| 3.0 \| 84 \| 1.4817 \| 0.2619 \| \| 1.5833 \| 4.0 \| 112 \| 1.4810 \| 0.2619 \| \| 1.5467 \| 5.0 \| 140 \| 1.4803 \| 0.2619 \| \| 1.5372 \| 6.0 \| 168 \| 1.4795 \| 0.2857 \| \| 1.5683 \| 7.0 \| 196 \| 1.4788 \| 0.2857 \| \| 1.5057 \| 8.0 \| 224 \| 1.4781 \| 0.2857 \| \| 1.5994 \| 9.0 \| 252 \| 1.4774 \| 0.2857 \| \| 1.5076 \| 10.0 \| 280 \| 1.4768 \| 0.2857 \| \| 1.5466 \| 11.0 \| 308 \| 1.4762 \| 0.2857 \| \| 1.544 \| 12.0 \| 336 \| 1.4756 \| 0.2857 \| \| 1.5866 \| 13.0 \| 364 \| 1.4750 \| 0.2857 \| \| 1.5384 \| 14.0 \| 392 \| 1.4744 \| 0.2857 \| \| 1.6111 \| 15.0 \| 420 \| 1.4739 \| 0.2857 \| \| 1.5625 \| 16.0 \| 448 \| 1.4733 \| 0.2857 \| \| 1.547 \| 17.0 \| 476 \| 1.4728 \| 0.2857 \| \| 1.5362 \| 18.0 \| 504 \| 1.4723 \| 0.2857 \| \| 1.5318 \| 19.0 \| 532 \| 1.4718 \| 0.2857 \| \| 1.5453 \| 20.0 \| 560 \| 1.4714 \| 0.2857 \| \| 1.5434 \| 21.0 \| 588 \| 1.4709 \| 0.2857 \| \| 1.548 \| 22.0 \| 616 \| 1.4705 \| 0.2857 \| \| 1.5105 \| 23.0 \| 644 \| 1.4701 \| 0.2857 \| \| 1.5176 \| 24.0 \| 672 \| 1.4697 \| 0.2857 \| \| 1.5194 \| 25.0 \| 700 \| 1.4694 \| 0.2857 \| \| 1.5543 \| 26.0 \| 728 \| 1.4690 \| 0.2857 \| \| 1.5727 \| 27.0 \| 756 \| 1.4687 \| 0.2857 \| \| 1.5476 \| 28.0 \| 784 \| 1.4684 \| 0.2857 \| \| 1.5163 \| 29.0 \| 812 \| 1.4681 \| 0.2857 \| \| 1.4767 \| 30.0 \| 840 \| 1.4678 \| 0.2857 \| \| 1.5623 \| 31.0 \| 868 \| 1.4676 \| 0.2857 \| \| 1.4924 \| 32.0 \| 896 \| 1.4674 \| 0.2857 \| \| 1.5673 \| 33.0 \| 924 \| 1.4672 \| 0.2857 \| \| 1.4842 \| 34.0 \| 952 \| 1.4670 \| 0.2857 \| \| 1.4908 \| 35.0 \| 980 \| 1.4668 \| 0.2857 \| \| 1.5184 \| 36.0 \| 1008 \| 1.4666 \| 0.2857 \| \| 1.5315 \| 37.0 \| 1036 \| 1.4664 \| 0.2857 \| \| 1.4892 \| 38.0 \| 1064 \| 1.4663 \| 0.2857 \| \| 1.5241 \| 39.0 \| 1092 \| 1.4662 \| 0.2857 \| \| 1.5587 \| 40.0 \| 1120 \| 1.4661 \| 0.2857 \| \| 1.5867 \| 41.0 \| 1148 \| 1.4660 \| 0.2857 \| \| 1.5357 \| 42.0 \| 1176 \| 1.4659 \| 0.2857 \| \| 1.479 \| 43.0 \| 1204 \| 1.4659 \| 0.2857 \| \| 1.4798 \| 44.0 \| 1232 \| 1.4658 \| 0.2857 \| \| 1.5998 \| 45.0 \| 1260 \| 1.4658 \| 0.2857 \| \| 1.5487 \| 46.0 \| 1288 \| 1.4658 \| 0.2857 \| \| 1.5234 \| 47.0 \| 1316 \| 1.4657 \| 0.2857 \| \| 1.5142 \| 48.0 \| 1344 \| 1.4657 \| 0.2857 \| \| 1.5259 \| 49.0 \| 1372 \| 1.4657 \| 0.2857 \| \| 1.5344 \| 50.0 \| 1400 \| 1.4657 \| 0.2857 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_00001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_00001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4968 - Accuracy: 0.2439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5975 \| 1.0 \| 28 \| 1.5197 \| 0.2195 \| \| 1.5191 \| 2.0 \| 56 \| 1.5186 \| 0.2195 \| \| 1.5652 \| 3.0 \| 84 \| 1.5176 \| 0.2195 \| \| 1.5368 \| 4.0 \| 112 \| 1.5166 \| 0.2195 \| \| 1.5533 \| 5.0 \| 140 \| 1.5156 \| 0.2195 \| \| 1.5934 \| 6.0 \| 168 \| 1.5147 \| 0.2195 \| \| 1.5997 \| 7.0 \| 196 \| 1.5138 \| 0.2195 \| \| 1.543 \| 8.0 \| 224 \| 1.5129 \| 0.2195 \| \| 1.5785 \| 9.0 \| 252 \| 1.5120 \| 0.2195 \| \| 1.5476 \| 10.0 \| 280 \| 1.5112 \| 0.2195 \| \| 1.5374 \| 11.0 \| 308 \| 1.5104 \| 0.2195 \| \| 1.5776 \| 12.0 \| 336 \| 1.5096 \| 0.2195 \| \| 1.552 \| 13.0 \| 364 \| 1.5088 \| 0.2195 \| \| 1.5084 \| 14.0 \| 392 \| 1.5081 \| 0.2195 \| \| 1.5475 \| 15.0 \| 420 \| 1.5073 \| 0.2195 \| \| 1.5527 \| 16.0 \| 448 \| 1.5067 \| 0.2195 \| \| 1.5461 \| 17.0 \| 476 \| 1.5060 \| 0.2195 \| \| 1.553 \| 18.0 \| 504 \| 1.5054 \| 0.2195 \| \| 1.5466 \| 19.0 \| 532 \| 1.5047 \| 0.2195 \| \| 1.5068 \| 20.0 \| 560 \| 1.5041 \| 0.2195 \| \| 1.5792 \| 21.0 \| 588 \| 1.5036 \| 0.2195 \| \| 1.5408 \| 22.0 \| 616 \| 1.5030 \| 0.2195 \| \| 1.4869 \| 23.0 \| 644 \| 1.5025 \| 0.2195 \| \| 1.5203 \| 24.0 \| 672 \| 1.5020 \| 0.2439 \| \| 1.5205 \| 25.0 \| 700 \| 1.5016 \| 0.2439 \| \| 1.5334 \| 26.0 \| 728 \| 1.5011 \| 0.2439 \| \| 1.5195 \| 27.0 \| 756 \| 1.5007 \| 0.2439 \| \| 1.555 \| 28.0 \| 784 \| 1.5003 \| 0.2439 \| \| 1.5231 \| 29.0 \| 812 \| 1.4999 \| 0.2439 \| \| 1.5521 \| 30.0 \| 840 \| 1.4996 \| 0.2439 \| \| 1.5405 \| 31.0 \| 868 \| 1.4992 \| 0.2439 \| \| 1.5223 \| 32.0 \| 896 \| 1.4989 \| 0.2439 \| \| 1.533 \| 33.0 \| 924 \| 1.4986 \| 0.2439 \| \| 1.5569 \| 34.0 \| 952 \| 1.4984 \| 0.2439 \| \| 1.5415 \| 35.0 \| 980 \| 1.4981 \| 0.2439 \| \| 1.5242 \| 36.0 \| 1008 \| 1.4979 \| 0.2439 \| \| 1.5342 \| 37.0 \| 1036 \| 1.4977 \| 0.2439 \| \| 1.51 \| 38.0 \| 1064 \| 1.4975 \| 0.2439 \| \| 1.4915 \| 39.0 \| 1092 \| 1.4974 \| 0.2439 \| \| 1.533 \| 40.0 \| 1120 \| 1.4972 \| 0.2439 \| \| 1.559 \| 41.0 \| 1148 \| 1.4971 \| 0.2439 \| \| 1.5496 \| 42.0 \| 1176 \| 1.4970 \| 0.2439 \| \| 1.5368 \| 43.0 \| 1204 \| 1.4969 \| 0.2439 \| \| 1.5602 \| 44.0 \| 1232 \| 1.4969 \| 0.2439 \| \| 1.5291 \| 45.0 \| 1260 \| 1.4968 \| 0.2439 \| \| 1.5316 \| 46.0 \| 1288 \| 1.4968 \| 0.2439 \| \| 1.5518 \| 47.0 \| 1316 \| 1.4968 \| 0.2439 \| \| 1.5141 \| 48.0 \| 1344 \| 1.4968 \| 0.2439 \| \| 1.515 \| 49.0 \| 1372 \| 1.4968 \| 0.2439 \| \| 1.544 \| 50.0 \| 1400 \| 1.4968 \| 0.2439 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-small_tobacco3482_kd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_kd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5105 - Accuracy: 0.815 - Brier Loss: 0.2790 - Nll: 1.4944 - F1 Micro: 0.815 - F1 Macro: 0.7942 - Ece: 0.1287 - Aurc: 0.0524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.2378 \| 0.17 \| 0.8975 \| 4.4036 \| 0.17 \| 0.1418 \| 0.2519 \| 0.8078 \| \| No log \| 2.0 \| 14 \| 1.7484 \| 0.38 \| 0.7667 \| 4.1809 \| 0.38 \| 0.2513 \| 0.3132 \| 0.4423 \| \| No log \| 3.0 \| 21 \| 1.1417 \| 0.55 \| 0.5683 \| 1.8669 \| 0.55 \| 0.4592 \| 0.2551 \| 0.2287 \| \| No log \| 4.0 \| 28 \| 0.8020 \| 0.685 \| 0.4327 \| 1.7476 \| 0.685 \| 0.6393 \| 0.2274 \| 0.1292 \| \| No log \| 5.0 \| 35 \| 0.8347 \| 0.645 \| 0.4502 \| 1.6809 \| 0.645 \| 0.6306 \| 0.1939 \| 0.1346 \| \| No log \| 6.0 \| 42 \| 0.6546 \| 0.735 \| 0.3657 \| 1.5210 \| 0.735 \| 0.7191 \| 0.1995 \| 0.0901 \| \| No log \| 7.0 \| 49 \| 0.6447 \| 0.76 \| 0.3375 \| 1.5117 \| 0.76 \| 0.7450 \| 0.1781 \| 0.0875 \| \| No log \| 8.0 \| 56 \| 0.7089 \| 0.775 \| 0.3650 \| 1.4823 \| 0.775 \| 0.7554 \| 0.2026 \| 0.0971 \| \| No log \| 9.0 \| 63 \| 0.5721 \| 0.785 \| 0.3083 \| 1.4053 \| 0.785 \| 0.7633 \| 0.1647 \| 0.0651 \| \| No log \| 10.0 \| 70 \| 0.5953 \| 0.795 \| 0.3130 \| 1.4301 \| 0.795 \| 0.7971 \| 0.1661 \| 0.0701 \| \| No log \| 11.0 \| 77 \| 0.6352 \| 0.79 \| 0.3131 \| 1.5018 \| 0.79 \| 0.7607 \| 0.1503 \| 0.0789 \| \| No log \| 12.0 \| 84 \| 0.7999 \| 0.735 \| 0.3916 \| 1.7141 \| 0.735 \| 0.7065 \| 0.2143 \| 0.1178 \| \| No log \| 13.0 \| 91 \| 0.6602 \| 0.8 \| 0.3099 \| 1.8022 \| 0.8000 \| 0.7746 \| 0.1709 \| 0.0805 \| \| No log \| 14.0 \| 98 \| 0.6529 \| 0.785 \| 0.3298 \| 1.3607 \| 0.785 \| 0.7658 \| 0.1771 \| 0.0858 \| \| No log \| 15.0 \| 105 \| 0.6170 \| 0.8 \| 0.3098 \| 1.3676 \| 0.8000 \| 0.7838 \| 0.1630 \| 0.0723 \| \| No log \| 16.0 \| 112 \| 0.6484 \| 0.775 \| 0.3342 \| 1.2826 \| 0.775 \| 0.7752 \| 0.1837 \| 0.0827 \| \| No log \| 17.0 \| 119 \| 0.5817 \| 0.78 \| 0.3019 \| 1.6577 \| 0.78 \| 0.7730 \| 0.1566 \| 0.0582 \| \| No log \| 18.0 \| 126 \| 0.6528 \| 0.78 \| 0.3376 \| 1.5044 \| 0.78 \| 0.7788 \| 0.1687 \| 0.0768 \| \| No log \| 19.0 \| 133 \| 0.6241 \| 0.805 \| 0.3038 \| 1.3465 \| 0.805 \| 0.7796 \| 0.1498 \| 0.0759 \| \| No log \| 20.0 \| 140 \| 0.5610 \| 0.79 \| 0.2948 \| 1.4395 \| 0.79 \| 0.7716 \| 0.1515 \| 0.0708 \| \| No log \| 21.0 \| 147 \| 0.6829 \| 0.78 \| 0.3241 \| 1.3252 \| 0.78 \| 0.7687 \| 0.1782 \| 0.0852 \| \| No log \| 22.0 \| 154 \| 0.5443 \| 0.795 \| 0.3117 \| 1.4374 \| 0.795 \| 0.7822 \| 0.1730 \| 0.0679 \| \| No log \| 23.0 \| 161 \| 0.6968 \| 0.78 \| 0.3474 \| 1.7830 \| 0.78 \| 0.7880 \| 0.1745 \| 0.0813 \| \| No log \| 24.0 \| 168 \| 0.7422 \| 0.75 \| 0.3639 \| 1.5379 \| 0.75 \| 0.7238 \| 0.1982 \| 0.0940 \| \| No log \| 25.0 \| 175 \| 0.5756 \| 0.785 \| 0.3150 \| 1.4739 \| 0.785 \| 0.7723 \| 0.1615 \| 0.0675 \| \| No log \| 26.0 \| 182 \| 0.6127 \| 0.805 \| 0.3036 \| 1.5553 \| 0.805 \| 0.7990 \| 0.1416 \| 0.0659 \| \| No log \| 27.0 \| 189 \| 0.5852 \| 0.795 \| 0.3104 \| 1.5149 \| 0.795 \| 0.7808 \| 0.1583 \| 0.0625 \| \| No log \| 28.0 \| 196 \| 0.5421 \| 0.83 \| 0.2808 \| 1.4320 \| 0.83 \| 0.8147 \| 0.1475 \| 0.0558 \| \| No log \| 29.0 \| 203 \| 0.5588 \| 0.79 \| 0.2888 \| 1.5801 \| 0.79 \| 0.7723 \| 0.1465 \| 0.0580 \| \| No log \| 30.0 \| 210 \| 0.5532 \| 0.795 \| 0.2892 \| 1.5724 \| 0.795 \| 0.7790 \| 0.1453 \| 0.0576 \| \| No log \| 31.0 \| 217 \| 0.5050 \| 0.835 \| 0.2685 \| 1.4206 \| 0.835 \| 0.8221 \| 0.1459 \| 0.0549 \| \| No log \| 32.0 \| 224 \| 0.5067 \| 0.82 \| 0.2762 \| 1.4460 \| 0.82 \| 0.8017 \| 0.1494 \| 0.0538 \| \| No log \| 33.0 \| 231 \| 0.5200 \| 0.815 \| 0.2798 \| 1.5300 \| 0.815 \| 0.7973 \| 0.1442 \| 0.0541 \| \| No log \| 34.0 \| 238 \| 0.5110 \| 0.825 \| 0.2802 \| 1.6009 \| 0.825 \| 0.8095 \| 0.1462 \| 0.0537 \| \| No log \| 35.0 \| 245 \| 0.5125 \| 0.815 \| 0.2804 \| 1.5209 \| 0.815 \| 0.8013 \| 0.1555 \| 0.0540 \| \| No log \| 36.0 \| 252 \| 0.4981 \| 0.82 \| 0.2728 \| 1.4498 \| 0.82 \| 0.8032 \| 0.1557 \| 0.0522 \| \| No log \| 37.0 \| 259 \| 0.5196 \| 0.82 \| 0.2796 \| 1.5297 \| 0.82 \| 0.8057 \| 0.1396 \| 0.0523 \| \| No log \| 38.0 \| 266 \| 0.5034 \| 0.82 \| 0.2755 \| 1.4577 \| 0.82 \| 0.8000 \| 0.1449 \| 0.0524 \| \| No log \| 39.0 \| 273 \| 0.5190 \| 0.815 \| 0.2810 \| 1.5240 \| 0.815 \| 0.8003 \| 0.1516 \| 0.0533 \| \| No log \| 40.0 \| 280 \| 0.4926 \| 0.83 \| 0.2697 \| 1.4598 \| 0.83 \| 0.8161 \| 0.1248 \| 0.0514 \| \| No log \| 41.0 \| 287 \| 0.5117 \| 0.815 \| 0.2808 \| 1.5168 \| 0.815 \| 0.7965 \| 0.1306 \| 0.0525 \| \| No log \| 42.0 \| 294 \| 0.5034 \| 0.825 \| 0.2721 \| 1.5263 \| 0.825 \| 0.8143 \| 0.1389 \| 0.0533 \| \| No log \| 43.0 \| 301 \| 0.5073 \| 0.815 \| 0.2762 \| 1.5308 \| 0.815 \| 0.7916 \| 0.1452 \| 0.0511 \| \| No log \| 44.0 \| 308 \| 0.5017 \| 0.825 \| 0.2751 \| 1.5202 \| 0.825 \| 0.8095 \| 0.1473 \| 0.0525 \| \| No log \| 45.0 \| 315 \| 0.5052 \| 0.815 \| 0.2783 \| 1.5143 \| 0.815 \| 0.7965 \| 0.1451 \| 0.0525 \| \| No log \| 46.0 \| 322 \| 0.5043 \| 0.83 \| 0.2743 \| 1.5172 \| 0.83 \| 0.8172 \| 0.1481 \| 0.0517 \| \| No log \| 47.0 \| 329 \| 0.5057 \| 0.825 \| 0.2767 \| 1.5164 \| 0.825 \| 0.8089 \| 0.1325 \| 0.0520 \| \| No log \| 48.0 \| 336 \| 0.5033 \| 0.82 \| 0.2752 \| 1.5168 \| 0.82 \| 0.8061 \| 0.1430 \| 0.0523 \| \| No log \| 49.0 \| 343 \| 0.5042 \| 0.82 \| 0.2755 \| 1.5163 \| 0.82 \| 0.8061 \| 0.1394 \| 0.0517 \| \| No log \| 50.0 \| 350 \| 0.5068 \| 0.82 \| 0.2767 \| 1.5153 \| 0.82 \| 0.8061 \| 0.1471 \| 0.0517 \| \| No log \| 51.0 \| 357 \| 0.5048 \| 0.82 \| 0.2759 \| 1.5137 \| 0.82 \| 0.8061 \| 0.1419 \| 0.0519 \| \| No log \| 52.0 \| 364 \| 0.5044 \| 0.825 \| 0.2759 \| 1.5112 \| 0.825 \| 0.8064 \| 0.1342 \| 0.0518 \| \| No log \| 53.0 \| 371 \| 0.5046 \| 0.825 \| 0.2756 \| 1.5122 \| 0.825 \| 0.8116 \| 0.1388 \| 0.0514 \| \| No log \| 54.0 \| 378 \| 0.5078 \| 0.815 \| 0.2777 \| 1.5111 \| 0.815 \| 0.7984 \| 0.1442 \| 0.0519 \| \| No log \| 55.0 \| 385 \| 0.5059 \| 0.815 \| 0.2767 \| 1.5109 \| 0.815 \| 0.7984 \| 0.1351 \| 0.0518 \| \| No log \| 56.0 \| 392 \| 0.5087 \| 0.82 \| 0.2779 \| 1.5089 \| 0.82 \| 0.8061 \| 0.1391 \| 0.0518 \| \| No log \| 57.0 \| 399 \| 0.5072 \| 0.82 \| 0.2771 \| 1.5094 \| 0.82 \| 0.8061 \| 0.1339 \| 0.0517 \| \| No log \| 58.0 \| 406 \| 0.5079 \| 0.82 \| 0.2776 \| 1.5074 \| 0.82 \| 0.8061 \| 0.1366 \| 0.0518 \| \| No log \| 59.0 \| 413 \| 0.5072 \| 0.82 \| 0.2771 \| 1.5072 \| 0.82 \| 0.8061 \| 0.1308 \| 0.0518 \| \| No log \| 60.0 \| 420 \| 0.5084 \| 0.825 \| 0.2776 \| 1.5059 \| 0.825 \| 0.8116 \| 0.1303 \| 0.0520 \| \| No log \| 61.0 \| 427 \| 0.5074 \| 0.82 \| 0.2772 \| 1.5066 \| 0.82 \| 0.8038 \| 0.1244 \| 0.0520 \| \| No log \| 62.0 \| 434 \| 0.5090 \| 0.82 \| 0.2781 \| 1.5053 \| 0.82 \| 0.8061 \| 0.1367 \| 0.0519 \| \| No log \| 63.0 \| 441 \| 0.5094 \| 0.825 \| 0.2779 \| 1.5050 \| 0.825 \| 0.8116 \| 0.1305 \| 0.0520 \| \| No log \| 64.0 \| 448 \| 0.5098 \| 0.82 \| 0.2782 \| 1.5049 \| 0.82 \| 0.8038 \| 0.1314 \| 0.0520 \| \| No log \| 65.0 \| 455 \| 0.5086 \| 0.82 \| 0.2780 \| 1.5038 \| 0.82 \| 0.8038 \| 0.1249 \| 0.0520 \| \| No log \| 66.0 \| 462 \| 0.5103 \| 0.82 \| 0.2787 \| 1.5023 \| 0.82 \| 0.8038 \| 0.1222 \| 0.0522 \| \| No log \| 67.0 \| 469 \| 0.5095 \| 0.82 \| 0.2782 \| 1.5025 \| 0.82 \| 0.8038 \| 0.1228 \| 0.0521 \| \| No log \| 68.0 \| 476 \| 0.5095 \| 0.82 \| 0.2783 \| 1.5027 \| 0.82 \| 0.8038 \| 0.1330 \| 0.0522 \| \| No log \| 69.0 \| 483 \| 0.5097 \| 0.82 \| 0.2785 \| 1.5015 \| 0.82 \| 0.8038 \| 0.1228 \| 0.0521 \| \| No log \| 70.0 \| 490 \| 0.5109 \| 0.82 \| 0.2788 \| 1.5005 \| 0.82 \| 0.8038 \| 0.1322 \| 0.0520 \| \| No log \| 71.0 \| 497 \| 0.5096 \| 0.82 \| 0.2784 \| 1.5012 \| 0.82 \| 0.8038 \| 0.1320 \| 0.0522 \| \| 0.1366 \| 72.0 \| 504 \| 0.5095 \| 0.82 \| 0.2784 \| 1.5011 \| 0.82 \| 0.8038 \| 0.1219 \| 0.0522 \| \| 0.1366 \| 73.0 \| 511 \| 0.5109 \| 0.82 \| 0.2791 \| 1.4998 \| 0.82 \| 0.8038 \| 0.1249 \| 0.0523 \| \| 0.1366 \| 74.0 \| 518 \| 0.5100 \| 0.82 \| 0.2786 \| 1.5000 \| 0.82 \| 0.8038 \| 0.1219 \| 0.0521 \| \| 0.1366 \| 75.0 \| 525 \| 0.5096 \| 0.82 \| 0.2784 \| 1.5000 \| 0.82 \| 0.8038 \| 0.1238 \| 0.0521 \| \| 0.1366 \| 76.0 \| 532 \| 0.5104 \| 0.82 \| 0.2787 \| 1.4988 \| 0.82 \| 0.8038 \| 0.1341 \| 0.0523 \| \| 0.1366 \| 77.0 \| 539 \| 0.5105 \| 0.82 \| 0.2788 \| 1.4985 \| 0.82 \| 0.8038 \| 0.1340 \| 0.0521 \| \| 0.1366 \| 78.0 \| 546 \| 0.5103 \| 0.82 \| 0.2788 \| 1.4985 \| 0.82 \| 0.8038 \| 0.1338 \| 0.0520 \| \| 0.1366 \| 79.0 \| 553 \| 0.5105 \| 0.82 \| 0.2788 \| 1.4983 \| 0.82 \| 0.8038 \| 0.1317 \| 0.0522 \| \| 0.1366 \| 80.0 \| 560 \| 0.5106 \| 0.82 \| 0.2789 \| 1.4977 \| 0.82 \| 0.8038 \| 0.1337 \| 0.0523 \| \| 0.1366 \| 81.0 \| 567 \| 0.5108 \| 0.82 \| 0.2790 \| 1.4971 \| 0.82 \| 0.8038 \| 0.1339 \| 0.0523 \| \| 0.1366 \| 82.0 \| 574 \| 0.5107 \| 0.82 \| 0.2790 \| 1.4970 \| 0.82 \| 0.8038 \| 0.1317 \| 0.0521 \| \| 0.1366 \| 83.0 \| 581 \| 0.5108 \| 0.82 \| 0.2790 \| 1.4968 \| 0.82 \| 0.8038 \| 0.1339 \| 0.0522 \| \| 0.1366 \| 84.0 \| 588 \| 0.5105 \| 0.82 \| 0.2789 \| 1.4966 \| 0.82 \| 0.8038 \| 0.1340 \| 0.0522 \| \| 0.1366 \| 85.0 \| 595 \| 0.5106 \| 0.82 \| 0.2789 \| 1.4961 \| 0.82 \| 0.8038 \| 0.1338 \| 0.0523 \| \| 0.1366 \| 86.0 \| 602 \| 0.5109 \| 0.82 \| 0.2790 \| 1.4958 \| 0.82 \| 0.8038 \| 0.1336 \| 0.0524 \| \| 0.1366 \| 87.0 \| 609 \| 0.5105 \| 0.815 \| 0.2789 \| 1.4956 \| 0.815 \| 0.7942 \| 0.1290 \| 0.0525 \| \| 0.1366 \| 88.0 \| 616 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4954 \| 0.815 \| 0.7942 \| 0.1290 \| 0.0525 \| \| 0.1366 \| 89.0 \| 623 \| 0.5106 \| 0.815 \| 0.2790 \| 1.4952 \| 0.815 \| 0.7942 \| 0.1290 \| 0.0526 \| \| 0.1366 \| 90.0 \| 630 \| 0.5106 \| 0.82 \| 0.2790 \| 1.4951 \| 0.82 \| 0.8038 \| 0.1338 \| 0.0523 \| \| 0.1366 \| 91.0 \| 637 \| 0.5107 \| 0.815 \| 0.2790 \| 1.4949 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0526 \| \| 0.1366 \| 92.0 \| 644 \| 0.5107 \| 0.815 \| 0.2790 \| 1.4947 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0526 \| \| 0.1366 \| 93.0 \| 651 \| 0.5107 \| 0.815 \| 0.2790 \| 1.4947 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0525 \| \| 0.1366 \| 94.0 \| 658 \| 0.5107 \| 0.82 \| 0.2790 \| 1.4946 \| 0.82 \| 0.8038 \| 0.1335 \| 0.0523 \| \| 0.1366 \| 95.0 \| 665 \| 0.5106 \| 0.82 \| 0.2790 \| 1.4946 \| 0.82 \| 0.8038 \| 0.1335 \| 0.0523 \| \| 0.1366 \| 96.0 \| 672 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4945 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0524 \| \| 0.1366 \| 97.0 \| 679 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4945 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0524 \| \| 0.1366 \| 98.0 \| 686 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4944 \| 0.815 \| 0.7942 \| 0.1289 \| 0.0524 \| \| 0.1366 \| 99.0 \| 693 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4944 \| 0.815 \| 0.7942 \| 0.1287 \| 0.0524 \| \| 0.1366 \| 100.0 \| 700 \| 0.5105 \| 0.815 \| 0.2790 \| 1.4944 \| 0.815 \| 0.7942 \| 0.1287 \| 0.0524 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_small_sgd_0001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_0001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3695 - Accuracy: 0.3111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5195 \| 1.0 \| 27 \| 1.4999 \| 0.2889 \| \| 1.4914 \| 2.0 \| 54 \| 1.4892 \| 0.2889 \| \| 1.5108 \| 3.0 \| 81 \| 1.4789 \| 0.2889 \| \| 1.5345 \| 4.0 \| 108 \| 1.4698 \| 0.2667 \| \| 1.4684 \| 5.0 \| 135 \| 1.4617 \| 0.2667 \| \| 1.4525 \| 6.0 \| 162 \| 1.4534 \| 0.2667 \| \| 1.4298 \| 7.0 \| 189 \| 1.4465 \| 0.2667 \| \| 1.4569 \| 8.0 \| 216 \| 1.4397 \| 0.2444 \| \| 1.4283 \| 9.0 \| 243 \| 1.4337 \| 0.2444 \| \| 1.4203 \| 10.0 \| 270 \| 1.4280 \| 0.2444 \| \| 1.3871 \| 11.0 \| 297 \| 1.4228 \| 0.2444 \| \| 1.4156 \| 12.0 \| 324 \| 1.4180 \| 0.2444 \| \| 1.4346 \| 13.0 \| 351 \| 1.4134 \| 0.2444 \| \| 1.4076 \| 14.0 \| 378 \| 1.4093 \| 0.2444 \| \| 1.425 \| 15.0 \| 405 \| 1.4059 \| 0.2444 \| \| 1.4406 \| 16.0 \| 432 \| 1.4025 \| 0.2444 \| \| 1.4069 \| 17.0 \| 459 \| 1.3996 \| 0.2444 \| \| 1.3779 \| 18.0 \| 486 \| 1.3968 \| 0.2444 \| \| 1.3991 \| 19.0 \| 513 \| 1.3941 \| 0.2667 \| \| 1.3962 \| 20.0 \| 540 \| 1.3918 \| 0.2667 \| \| 1.3954 \| 21.0 \| 567 \| 1.3897 \| 0.2889 \| \| 1.3886 \| 22.0 \| 594 \| 1.3877 \| 0.2889 \| \| 1.3775 \| 23.0 \| 621 \| 1.3858 \| 0.2889 \| \| 1.3714 \| 24.0 \| 648 \| 1.3842 \| 0.2889 \| \| 1.4056 \| 25.0 \| 675 \| 1.3826 \| 0.2889 \| \| 1.4026 \| 26.0 \| 702 \| 1.3812 \| 0.2889 \| \| 1.359 \| 27.0 \| 729 \| 1.3799 \| 0.2889 \| \| 1.3709 \| 28.0 \| 756 \| 1.3787 \| 0.2889 \| \| 1.3667 \| 29.0 \| 783 \| 1.3776 \| 0.2889 \| \| 1.3672 \| 30.0 \| 810 \| 1.3766 \| 0.2889 \| \| 1.3762 \| 31.0 \| 837 \| 1.3757 \| 0.2889 \| \| 1.3384 \| 32.0 \| 864 \| 1.3749 \| 0.2889 \| \| 1.3698 \| 33.0 \| 891 \| 1.3742 \| 0.2889 \| \| 1.3636 \| 34.0 \| 918 \| 1.3735 \| 0.3111 \| \| 1.3439 \| 35.0 \| 945 \| 1.3729 \| 0.3111 \| \| 1.3571 \| 36.0 \| 972 \| 1.3723 \| 0.3111 \| \| 1.3688 \| 37.0 \| 999 \| 1.3718 \| 0.3111 \| \| 1.3527 \| 38.0 \| 1026 \| 1.3714 \| 0.3111 \| \| 1.3641 \| 39.0 \| 1053 \| 1.3710 \| 0.3111 \| \| 1.3538 \| 40.0 \| 1080 \| 1.3707 \| 0.3111 \| \| 1.3693 \| 41.0 \| 1107 \| 1.3704 \| 0.3111 \| \| 1.3789 \| 42.0 \| 1134 \| 1.3701 \| 0.3111 \| \| 1.3917 \| 43.0 \| 1161 \| 1.3699 \| 0.3111 \| \| 1.3524 \| 44.0 \| 1188 \| 1.3698 \| 0.3111 \| \| 1.367 \| 45.0 \| 1215 \| 1.3696 \| 0.3111 \| \| 1.3553 \| 46.0 \| 1242 \| 1.3696 \| 0.3111 \| \| 1.3523 \| 47.0 \| 1269 \| 1.3695 \| 0.3111 \| \| 1.3646 \| 48.0 \| 1296 \| 1.3695 \| 0.3111 \| \| 1.3891 \| 49.0 \| 1323 \| 1.3695 \| 0.3111 \| \| 1.3396 \| 50.0 \| 1350 \| 1.3695 \| 0.3111 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_0001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_0001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3896 - Accuracy: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5183 \| 1.0 \| 27 \| 1.5038 \| 0.1778 \| \| 1.502 \| 2.0 \| 54 \| 1.4946 \| 0.2 \| \| 1.5109 \| 3.0 \| 81 \| 1.4859 \| 0.2222 \| \| 1.5446 \| 4.0 \| 108 \| 1.4781 \| 0.2444 \| \| 1.4687 \| 5.0 \| 135 \| 1.4710 \| 0.2444 \| \| 1.4554 \| 6.0 \| 162 \| 1.4641 \| 0.2889 \| \| 1.4113 \| 7.0 \| 189 \| 1.4582 \| 0.2889 \| \| 1.4434 \| 8.0 \| 216 \| 1.4525 \| 0.2667 \| \| 1.4243 \| 9.0 \| 243 \| 1.4473 \| 0.2667 \| \| 1.4268 \| 10.0 \| 270 \| 1.4425 \| 0.2889 \| \| 1.386 \| 11.0 \| 297 \| 1.4382 \| 0.2889 \| \| 1.4235 \| 12.0 \| 324 \| 1.4341 \| 0.2667 \| \| 1.4228 \| 13.0 \| 351 \| 1.4304 \| 0.2667 \| \| 1.4091 \| 14.0 \| 378 \| 1.4269 \| 0.2889 \| \| 1.4135 \| 15.0 \| 405 \| 1.4239 \| 0.2667 \| \| 1.4228 \| 16.0 \| 432 \| 1.4210 \| 0.2889 \| \| 1.4188 \| 17.0 \| 459 \| 1.4184 \| 0.2889 \| \| 1.3824 \| 18.0 \| 486 \| 1.4159 \| 0.3333 \| \| 1.3861 \| 19.0 \| 513 \| 1.4136 \| 0.3111 \| \| 1.393 \| 20.0 \| 540 \| 1.4115 \| 0.3111 \| \| 1.4051 \| 21.0 \| 567 \| 1.4096 \| 0.3111 \| \| 1.373 \| 22.0 \| 594 \| 1.4077 \| 0.3333 \| \| 1.3737 \| 23.0 \| 621 \| 1.4060 \| 0.3333 \| \| 1.3668 \| 24.0 \| 648 \| 1.4044 \| 0.3556 \| \| 1.362 \| 25.0 \| 675 \| 1.4030 \| 0.3556 \| \| 1.3931 \| 26.0 \| 702 \| 1.4016 \| 0.3556 \| \| 1.3504 \| 27.0 \| 729 \| 1.4003 \| 0.3556 \| \| 1.3706 \| 28.0 \| 756 \| 1.3992 \| 0.3556 \| \| 1.359 \| 29.0 \| 783 \| 1.3981 \| 0.3556 \| \| 1.3774 \| 30.0 \| 810 \| 1.3972 \| 0.3556 \| \| 1.3678 \| 31.0 \| 837 \| 1.3963 \| 0.3556 \| \| 1.3418 \| 32.0 \| 864 \| 1.3955 \| 0.3556 \| \| 1.3702 \| 33.0 \| 891 \| 1.3947 \| 0.3556 \| \| 1.3589 \| 34.0 \| 918 \| 1.3940 \| 0.3556 \| \| 1.3212 \| 35.0 \| 945 \| 1.3933 \| 0.3333 \| \| 1.3648 \| 36.0 \| 972 \| 1.3928 \| 0.3333 \| \| 1.3509 \| 37.0 \| 999 \| 1.3922 \| 0.3333 \| \| 1.3506 \| 38.0 \| 1026 \| 1.3917 \| 0.3333 \| \| 1.3673 \| 39.0 \| 1053 \| 1.3913 \| 0.3333 \| \| 1.3657 \| 40.0 \| 1080 \| 1.3910 \| 0.3333 \| \| 1.3651 \| 41.0 \| 1107 \| 1.3906 \| 0.3333 \| \| 1.3688 \| 42.0 \| 1134 \| 1.3904 \| 0.3333 \| \| 1.3871 \| 43.0 \| 1161 \| 1.3901 \| 0.3333 \| \| 1.3307 \| 44.0 \| 1188 \| 1.3899 \| 0.3333 \| \| 1.3505 \| 45.0 \| 1215 \| 1.3898 \| 0.3333 \| \| 1.3367 \| 46.0 \| 1242 \| 1.3897 \| 0.3333 \| \| 1.3605 \| 47.0 \| 1269 \| 1.3896 \| 0.3333 \| \| 1.3556 \| 48.0 \| 1296 \| 1.3896 \| 0.3333 \| \| 1.3876 \| 49.0 \| 1323 \| 1.3896 \| 0.3333 \| \| 1.3357 \| 50.0 \| 1350 \| 1.3896 \| 0.3333 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_0001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_0001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4264 - Accuracy: 0.2093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5059 \| 1.0 \| 28 \| 1.5906 \| 0.2093 \| \| 1.4969 \| 2.0 \| 56 \| 1.5795 \| 0.2093 \| \| 1.4242 \| 3.0 \| 84 \| 1.5692 \| 0.2093 \| \| 1.4628 \| 4.0 \| 112 \| 1.5599 \| 0.2093 \| \| 1.5851 \| 5.0 \| 140 \| 1.5506 \| 0.2093 \| \| 1.4881 \| 6.0 \| 168 \| 1.5418 \| 0.2093 \| \| 1.4744 \| 7.0 \| 196 \| 1.5338 \| 0.2326 \| \| 1.427 \| 8.0 \| 224 \| 1.5266 \| 0.2558 \| \| 1.4274 \| 9.0 \| 252 \| 1.5192 \| 0.2558 \| \| 1.4168 \| 10.0 \| 280 \| 1.5125 \| 0.2558 \| \| 1.4418 \| 11.0 \| 308 \| 1.5064 \| 0.2558 \| \| 1.4361 \| 12.0 \| 336 \| 1.5007 \| 0.2558 \| \| 1.4139 \| 13.0 \| 364 \| 1.4950 \| 0.2558 \| \| 1.3932 \| 14.0 \| 392 \| 1.4898 \| 0.2558 \| \| 1.4041 \| 15.0 \| 420 \| 1.4850 \| 0.2326 \| \| 1.3745 \| 16.0 \| 448 \| 1.4806 \| 0.2326 \| \| 1.3653 \| 17.0 \| 476 \| 1.4764 \| 0.2093 \| \| 1.3841 \| 18.0 \| 504 \| 1.4723 \| 0.2093 \| \| 1.3735 \| 19.0 \| 532 \| 1.4687 \| 0.2093 \| \| 1.3391 \| 20.0 \| 560 \| 1.4653 \| 0.2093 \| \| 1.3879 \| 21.0 \| 588 \| 1.4620 \| 0.2093 \| \| 1.3861 \| 22.0 \| 616 \| 1.4589 \| 0.2093 \| \| 1.3726 \| 23.0 \| 644 \| 1.4561 \| 0.2093 \| \| 1.3725 \| 24.0 \| 672 \| 1.4534 \| 0.2093 \| \| 1.3587 \| 25.0 \| 700 \| 1.4508 \| 0.2093 \| \| 1.3359 \| 26.0 \| 728 \| 1.4485 \| 0.2093 \| \| 1.3627 \| 27.0 \| 756 \| 1.4462 \| 0.2326 \| \| 1.3855 \| 28.0 \| 784 \| 1.4442 \| 0.2326 \| \| 1.353 \| 29.0 \| 812 \| 1.4424 \| 0.2093 \| \| 1.301 \| 30.0 \| 840 \| 1.4407 \| 0.2093 \| \| 1.3248 \| 31.0 \| 868 \| 1.4390 \| 0.2093 \| \| 1.3654 \| 32.0 \| 896 \| 1.4375 \| 0.2093 \| \| 1.364 \| 33.0 \| 924 \| 1.4361 \| 0.2093 \| \| 1.322 \| 34.0 \| 952 \| 1.4347 \| 0.2093 \| \| 1.3619 \| 35.0 \| 980 \| 1.4335 \| 0.2093 \| \| 1.3562 \| 36.0 \| 1008 \| 1.4324 \| 0.2093 \| \| 1.4034 \| 37.0 \| 1036 \| 1.4314 \| 0.2093 \| \| 1.3401 \| 38.0 \| 1064 \| 1.4304 \| 0.2093 \| \| 1.3307 \| 39.0 \| 1092 \| 1.4297 \| 0.2093 \| \| 1.3736 \| 40.0 \| 1120 \| 1.4290 \| 0.2093 \| \| 1.3675 \| 41.0 \| 1148 \| 1.4284 \| 0.2093 \| \| 1.3234 \| 42.0 \| 1176 \| 1.4279 \| 0.2093 \| \| 1.3321 \| 43.0 \| 1204 \| 1.4274 \| 0.2093 \| \| 1.3436 \| 44.0 \| 1232 \| 1.4270 \| 0.2093 \| \| 1.3719 \| 45.0 \| 1260 \| 1.4268 \| 0.2093 \| \| 1.3462 \| 46.0 \| 1288 \| 1.4266 \| 0.2093 \| \| 1.3448 \| 47.0 \| 1316 \| 1.4265 \| 0.2093 \| \| 1.3465 \| 48.0 \| 1344 \| 1.4264 \| 0.2093 \| \| 1.2951 \| 49.0 \| 1372 \| 1.4264 \| 0.2093 \| \| 1.3665 \| 50.0 \| 1400 \| 1.4264 \| 0.2093 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_0001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_0001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3721 - Accuracy: 0.2619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5364 \| 1.0 \| 28 \| 1.4759 \| 0.2857 \| \| 1.6032 \| 2.0 \| 56 \| 1.4677 \| 0.2857 \| \| 1.5235 \| 3.0 \| 84 \| 1.4598 \| 0.2857 \| \| 1.5363 \| 4.0 \| 112 \| 1.4530 \| 0.2857 \| \| 1.4963 \| 5.0 \| 140 \| 1.4466 \| 0.2857 \| \| 1.4798 \| 6.0 \| 168 \| 1.4404 \| 0.2857 \| \| 1.4963 \| 7.0 \| 196 \| 1.4349 \| 0.2857 \| \| 1.441 \| 8.0 \| 224 \| 1.4297 \| 0.3095 \| \| 1.5032 \| 9.0 \| 252 \| 1.4249 \| 0.3095 \| \| 1.4231 \| 10.0 \| 280 \| 1.4205 \| 0.3095 \| \| 1.4482 \| 11.0 \| 308 \| 1.4164 \| 0.3095 \| \| 1.4398 \| 12.0 \| 336 \| 1.4127 \| 0.3095 \| \| 1.468 \| 13.0 \| 364 \| 1.4093 \| 0.3095 \| \| 1.4278 \| 14.0 \| 392 \| 1.4061 \| 0.3095 \| \| 1.4624 \| 15.0 \| 420 \| 1.4032 \| 0.3095 \| \| 1.438 \| 16.0 \| 448 \| 1.4004 \| 0.2857 \| \| 1.4401 \| 17.0 \| 476 \| 1.3979 \| 0.2857 \| \| 1.416 \| 18.0 \| 504 \| 1.3956 \| 0.3095 \| \| 1.4033 \| 19.0 \| 532 \| 1.3934 \| 0.3333 \| \| 1.4123 \| 20.0 \| 560 \| 1.3916 \| 0.3333 \| \| 1.4056 \| 21.0 \| 588 \| 1.3899 \| 0.3095 \| \| 1.4089 \| 22.0 \| 616 \| 1.3883 \| 0.3333 \| \| 1.3801 \| 23.0 \| 644 \| 1.3868 \| 0.3333 \| \| 1.3733 \| 24.0 \| 672 \| 1.3854 \| 0.3095 \| \| 1.3798 \| 25.0 \| 700 \| 1.3840 \| 0.3095 \| \| 1.4051 \| 26.0 \| 728 \| 1.3828 \| 0.3095 \| \| 1.4017 \| 27.0 \| 756 \| 1.3817 \| 0.3095 \| \| 1.4006 \| 28.0 \| 784 \| 1.3807 \| 0.3095 \| \| 1.368 \| 29.0 \| 812 \| 1.3797 \| 0.3095 \| \| 1.3628 \| 30.0 \| 840 \| 1.3788 \| 0.3333 \| \| 1.3803 \| 31.0 \| 868 \| 1.3780 \| 0.2619 \| \| 1.3495 \| 32.0 \| 896 \| 1.3773 \| 0.2619 \| \| 1.393 \| 33.0 \| 924 \| 1.3766 \| 0.2619 \| \| 1.3379 \| 34.0 \| 952 \| 1.3760 \| 0.2619 \| \| 1.3609 \| 35.0 \| 980 \| 1.3754 \| 0.2619 \| \| 1.3521 \| 36.0 \| 1008 \| 1.3748 \| 0.2619 \| \| 1.3648 \| 37.0 \| 1036 \| 1.3744 \| 0.2619 \| \| 1.341 \| 38.0 \| 1064 \| 1.3740 \| 0.2619 \| \| 1.3689 \| 39.0 \| 1092 \| 1.3736 \| 0.2619 \| \| 1.3877 \| 40.0 \| 1120 \| 1.3733 \| 0.2619 \| \| 1.4062 \| 41.0 \| 1148 \| 1.3730 \| 0.2619 \| \| 1.3585 \| 42.0 \| 1176 \| 1.3727 \| 0.2619 \| \| 1.3339 \| 43.0 \| 1204 \| 1.3725 \| 0.2619 \| \| 1.3351 \| 44.0 \| 1232 \| 1.3724 \| 0.2619 \| \| 1.3978 \| 45.0 \| 1260 \| 1.3722 \| 0.2619 \| \| 1.3819 \| 46.0 \| 1288 \| 1.3721 \| 0.2619 \| \| 1.3511 \| 47.0 \| 1316 \| 1.3721 \| 0.2619 \| \| 1.3593 \| 48.0 \| 1344 \| 1.3721 \| 0.2619 \| \| 1.3691 \| 49.0 \| 1372 \| 1.3721 \| 0.2619 \| \| 1.3757 \| 50.0 \| 1400 \| 1.3721 \| 0.2619 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-small_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6146 - Accuracy: 0.8 - Brier Loss: 0.2784 - Nll: 1.4268 - F1 Micro: 0.8000 - F1 Macro: 0.7846 - Ece: 0.1626 - Aurc: 0.0474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.1581 \| 0.18 \| 0.8974 \| 4.2254 \| 0.18 \| 0.1559 \| 0.2651 \| 0.8061 \| \| No log \| 2.0 \| 14 \| 3.2929 \| 0.355 \| 0.7710 \| 4.0541 \| 0.3550 \| 0.2167 \| 0.2742 \| 0.4326 \| \| No log \| 3.0 \| 21 \| 2.2155 \| 0.55 \| 0.5837 \| 2.0462 \| 0.55 \| 0.4296 \| 0.2323 \| 0.2481 \| \| No log \| 4.0 \| 28 \| 1.5197 \| 0.7 \| 0.4370 \| 1.7716 \| 0.7 \| 0.6411 \| 0.2342 \| 0.1327 \| \| No log \| 5.0 \| 35 \| 1.2831 \| 0.715 \| 0.4289 \| 1.7142 \| 0.715 \| 0.6859 \| 0.2047 \| 0.1211 \| \| No log \| 6.0 \| 42 \| 1.2204 \| 0.72 \| 0.3989 \| 1.6102 \| 0.72 \| 0.6999 \| 0.1961 \| 0.1066 \| \| No log \| 7.0 \| 49 \| 0.9767 \| 0.755 \| 0.3317 \| 1.5919 \| 0.755 \| 0.7148 \| 0.1724 \| 0.0775 \| \| No log \| 8.0 \| 56 \| 0.8875 \| 0.785 \| 0.3049 \| 1.4209 \| 0.785 \| 0.7633 \| 0.1478 \| 0.0716 \| \| No log \| 9.0 \| 63 \| 0.9311 \| 0.79 \| 0.3185 \| 1.5420 \| 0.79 \| 0.7474 \| 0.1645 \| 0.0741 \| \| No log \| 10.0 \| 70 \| 0.8116 \| 0.835 \| 0.2672 \| 1.5127 \| 0.835 \| 0.8232 \| 0.1463 \| 0.0563 \| \| No log \| 11.0 \| 77 \| 0.8315 \| 0.805 \| 0.3054 \| 1.6275 \| 0.805 \| 0.7897 \| 0.1695 \| 0.0618 \| \| No log \| 12.0 \| 84 \| 0.7678 \| 0.815 \| 0.2917 \| 1.5009 \| 0.815 \| 0.8012 \| 0.1469 \| 0.0542 \| \| No log \| 13.0 \| 91 \| 0.7249 \| 0.81 \| 0.2816 \| 1.4685 \| 0.81 \| 0.7880 \| 0.1437 \| 0.0576 \| \| No log \| 14.0 \| 98 \| 0.8116 \| 0.815 \| 0.2894 \| 1.5975 \| 0.815 \| 0.7941 \| 0.1481 \| 0.0604 \| \| No log \| 15.0 \| 105 \| 0.7985 \| 0.81 \| 0.3098 \| 1.4721 \| 0.81 \| 0.7819 \| 0.1646 \| 0.0662 \| \| No log \| 16.0 \| 112 \| 0.6839 \| 0.815 \| 0.2781 \| 1.4357 \| 0.815 \| 0.7992 \| 0.1589 \| 0.0529 \| \| No log \| 17.0 \| 119 \| 0.6590 \| 0.82 \| 0.2670 \| 1.4487 \| 0.82 \| 0.8061 \| 0.1336 \| 0.0461 \| \| No log \| 18.0 \| 126 \| 0.7253 \| 0.81 \| 0.2938 \| 1.5163 \| 0.81 \| 0.7951 \| 0.1617 \| 0.0558 \| \| No log \| 19.0 \| 133 \| 0.6935 \| 0.795 \| 0.2949 \| 1.4516 \| 0.795 \| 0.7758 \| 0.1736 \| 0.0531 \| \| No log \| 20.0 \| 140 \| 0.6991 \| 0.795 \| 0.2875 \| 1.3932 \| 0.795 \| 0.7735 \| 0.1584 \| 0.0519 \| \| No log \| 21.0 \| 147 \| 0.7059 \| 0.815 \| 0.2966 \| 1.5011 \| 0.815 \| 0.7927 \| 0.1579 \| 0.0565 \| \| No log \| 22.0 \| 154 \| 0.6754 \| 0.79 \| 0.2896 \| 1.4549 \| 0.79 \| 0.7742 \| 0.1534 \| 0.0531 \| \| No log \| 23.0 \| 161 \| 0.6981 \| 0.785 \| 0.2989 \| 1.4261 \| 0.785 \| 0.7705 \| 0.1490 \| 0.0530 \| \| No log \| 24.0 \| 168 \| 0.6503 \| 0.805 \| 0.2842 \| 1.4998 \| 0.805 \| 0.7885 \| 0.1415 \| 0.0512 \| \| No log \| 25.0 \| 175 \| 0.6680 \| 0.79 \| 0.2891 \| 1.4228 \| 0.79 \| 0.7742 \| 0.1504 \| 0.0519 \| \| No log \| 26.0 \| 182 \| 0.6835 \| 0.81 \| 0.2948 \| 1.4400 \| 0.81 \| 0.7944 \| 0.1545 \| 0.0516 \| \| No log \| 27.0 \| 189 \| 0.6495 \| 0.81 \| 0.2846 \| 1.4433 \| 0.81 \| 0.7868 \| 0.1552 \| 0.0503 \| \| No log \| 28.0 \| 196 \| 0.6450 \| 0.81 \| 0.2851 \| 1.4037 \| 0.81 \| 0.7913 \| 0.1476 \| 0.0498 \| \| No log \| 29.0 \| 203 \| 0.6634 \| 0.815 \| 0.2861 \| 1.4186 \| 0.815 \| 0.7966 \| 0.1397 \| 0.0521 \| \| No log \| 30.0 \| 210 \| 0.6212 \| 0.805 \| 0.2739 \| 1.4265 \| 0.805 \| 0.7902 \| 0.1444 \| 0.0482 \| \| No log \| 31.0 \| 217 \| 0.6271 \| 0.815 \| 0.2800 \| 1.4392 \| 0.815 \| 0.7986 \| 0.1370 \| 0.0494 \| \| No log \| 32.0 \| 224 \| 0.6256 \| 0.8 \| 0.2786 \| 1.3677 \| 0.8000 \| 0.7811 \| 0.1454 \| 0.0496 \| \| No log \| 33.0 \| 231 \| 0.6219 \| 0.805 \| 0.2779 \| 1.4276 \| 0.805 \| 0.7857 \| 0.1580 \| 0.0465 \| \| No log \| 34.0 \| 238 \| 0.6203 \| 0.81 \| 0.2779 \| 1.4392 \| 0.81 \| 0.7914 \| 0.1275 \| 0.0470 \| \| No log \| 35.0 \| 245 \| 0.6193 \| 0.81 \| 0.2793 \| 1.4258 \| 0.81 \| 0.7934 \| 0.1438 \| 0.0483 \| \| No log \| 36.0 \| 252 \| 0.6261 \| 0.83 \| 0.2743 \| 1.4227 \| 0.83 \| 0.8098 \| 0.1482 \| 0.0501 \| \| No log \| 37.0 \| 259 \| 0.6190 \| 0.815 \| 0.2776 \| 1.4301 \| 0.815 \| 0.7977 \| 0.1446 \| 0.0484 \| \| No log \| 38.0 \| 266 \| 0.6210 \| 0.805 \| 0.2867 \| 1.4958 \| 0.805 \| 0.7878 \| 0.1477 \| 0.0496 \| \| No log \| 39.0 \| 273 \| 0.5974 \| 0.805 \| 0.2771 \| 1.5068 \| 0.805 \| 0.7901 \| 0.1381 \| 0.0476 \| \| No log \| 40.0 \| 280 \| 0.6224 \| 0.8 \| 0.2869 \| 1.4325 \| 0.8000 \| 0.7869 \| 0.1443 \| 0.0472 \| \| No log \| 41.0 \| 287 \| 0.6178 \| 0.805 \| 0.2796 \| 1.4316 \| 0.805 \| 0.7912 \| 0.1454 \| 0.0471 \| \| No log \| 42.0 \| 294 \| 0.6194 \| 0.825 \| 0.2765 \| 1.5001 \| 0.825 \| 0.8059 \| 0.1401 \| 0.0474 \| \| No log \| 43.0 \| 301 \| 0.6224 \| 0.805 \| 0.2769 \| 1.4268 \| 0.805 \| 0.7888 \| 0.1398 \| 0.0493 \| \| No log \| 44.0 \| 308 \| 0.6265 \| 0.8 \| 0.2819 \| 1.4401 \| 0.8000 \| 0.7846 \| 0.1422 \| 0.0481 \| \| No log \| 45.0 \| 315 \| 0.6275 \| 0.8 \| 0.2819 \| 1.4206 \| 0.8000 \| 0.7847 \| 0.1465 \| 0.0487 \| \| No log \| 46.0 \| 322 \| 0.6173 \| 0.805 \| 0.2806 \| 1.3618 \| 0.805 \| 0.7870 \| 0.1383 \| 0.0478 \| \| No log \| 47.0 \| 329 \| 0.6177 \| 0.81 \| 0.2804 \| 1.4988 \| 0.81 \| 0.7906 \| 0.1468 \| 0.0488 \| \| No log \| 48.0 \| 336 \| 0.6175 \| 0.81 \| 0.2788 \| 1.4356 \| 0.81 \| 0.7917 \| 0.1460 \| 0.0476 \| \| No log \| 49.0 \| 343 \| 0.6209 \| 0.81 \| 0.2775 \| 1.4290 \| 0.81 \| 0.7925 \| 0.1603 \| 0.0478 \| \| No log \| 50.0 \| 350 \| 0.6244 \| 0.815 \| 0.2780 \| 1.3662 \| 0.815 \| 0.7974 \| 0.1322 \| 0.0480 \| \| No log \| 51.0 \| 357 \| 0.6176 \| 0.81 \| 0.2777 \| 1.4307 \| 0.81 \| 0.7941 \| 0.1258 \| 0.0478 \| \| No log \| 52.0 \| 364 \| 0.6150 \| 0.805 \| 0.2774 \| 1.4310 \| 0.805 \| 0.7896 \| 0.1369 \| 0.0477 \| \| No log \| 53.0 \| 371 \| 0.6164 \| 0.81 \| 0.2772 \| 1.4298 \| 0.81 \| 0.7941 \| 0.1391 \| 0.0479 \| \| No log \| 54.0 \| 378 \| 0.6137 \| 0.81 \| 0.2766 \| 1.4291 \| 0.81 \| 0.7928 \| 0.1358 \| 0.0474 \| \| No log \| 55.0 \| 385 \| 0.6163 \| 0.81 \| 0.2776 \| 1.4298 \| 0.81 \| 0.7928 \| 0.1278 \| 0.0475 \| \| No log \| 56.0 \| 392 \| 0.6148 \| 0.81 \| 0.2776 \| 1.4286 \| 0.81 \| 0.7928 \| 0.1480 \| 0.0471 \| \| No log \| 57.0 \| 399 \| 0.6154 \| 0.81 \| 0.2773 \| 1.4290 \| 0.81 \| 0.7928 \| 0.1485 \| 0.0474 \| \| No log \| 58.0 \| 406 \| 0.6143 \| 0.8 \| 0.2781 \| 1.4281 \| 0.8000 \| 0.7852 \| 0.1405 \| 0.0473 \| \| No log \| 59.0 \| 413 \| 0.6158 \| 0.805 \| 0.2785 \| 1.4295 \| 0.805 \| 0.7899 \| 0.1455 \| 0.0473 \| \| No log \| 60.0 \| 420 \| 0.6146 \| 0.805 \| 0.2774 \| 1.4310 \| 0.805 \| 0.7899 \| 0.1346 \| 0.0472 \| \| No log \| 61.0 \| 427 \| 0.6154 \| 0.805 \| 0.2780 \| 1.4292 \| 0.805 \| 0.7899 \| 0.1451 \| 0.0472 \| \| No log \| 62.0 \| 434 \| 0.6148 \| 0.805 \| 0.2780 \| 1.4304 \| 0.805 \| 0.7905 \| 0.1543 \| 0.0473 \| \| No log \| 63.0 \| 441 \| 0.6150 \| 0.8 \| 0.2783 \| 1.4284 \| 0.8000 \| 0.7846 \| 0.1502 \| 0.0473 \| \| No log \| 64.0 \| 448 \| 0.6143 \| 0.805 \| 0.2780 \| 1.4294 \| 0.805 \| 0.7899 \| 0.1453 \| 0.0470 \| \| No log \| 65.0 \| 455 \| 0.6152 \| 0.805 \| 0.2782 \| 1.4298 \| 0.805 \| 0.7899 \| 0.1373 \| 0.0469 \| \| No log \| 66.0 \| 462 \| 0.6148 \| 0.8 \| 0.2781 \| 1.4287 \| 0.8000 \| 0.7852 \| 0.1492 \| 0.0475 \| \| No log \| 67.0 \| 469 \| 0.6134 \| 0.805 \| 0.2776 \| 1.4286 \| 0.805 \| 0.7899 \| 0.1526 \| 0.0470 \| \| No log \| 68.0 \| 476 \| 0.6150 \| 0.8 \| 0.2785 \| 1.4270 \| 0.8000 \| 0.7846 \| 0.1497 \| 0.0474 \| \| No log \| 69.0 \| 483 \| 0.6145 \| 0.8 \| 0.2783 \| 1.4281 \| 0.8000 \| 0.7846 \| 0.1483 \| 0.0471 \| \| No log \| 70.0 \| 490 \| 0.6145 \| 0.805 \| 0.2778 \| 1.4292 \| 0.805 \| 0.7899 \| 0.1472 \| 0.0471 \| \| No log \| 71.0 \| 497 \| 0.6143 \| 0.805 \| 0.2779 \| 1.4284 \| 0.805 \| 0.7899 \| 0.1529 \| 0.0470 \| \| 0.2616 \| 72.0 \| 504 \| 0.6148 \| 0.805 \| 0.2780 \| 1.4276 \| 0.805 \| 0.7899 \| 0.1414 \| 0.0471 \| \| 0.2616 \| 73.0 \| 511 \| 0.6147 \| 0.8 \| 0.2781 \| 1.4285 \| 0.8000 \| 0.7852 \| 0.1400 \| 0.0473 \| \| 0.2616 \| 74.0 \| 518 \| 0.6147 \| 0.8 \| 0.2783 \| 1.4281 \| 0.8000 \| 0.7846 \| 0.1501 \| 0.0473 \| \| 0.2616 \| 75.0 \| 525 \| 0.6150 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1417 \| 0.0473 \| \| 0.2616 \| 76.0 \| 532 \| 0.6143 \| 0.805 \| 0.2782 \| 1.4273 \| 0.805 \| 0.7899 \| 0.1524 \| 0.0470 \| \| 0.2616 \| 77.0 \| 539 \| 0.6147 \| 0.805 \| 0.2782 \| 1.4277 \| 0.805 \| 0.7899 \| 0.1526 \| 0.0470 \| \| 0.2616 \| 78.0 \| 546 \| 0.6149 \| 0.8 \| 0.2785 \| 1.4277 \| 0.8000 \| 0.7846 \| 0.1572 \| 0.0474 \| \| 0.2616 \| 79.0 \| 553 \| 0.6147 \| 0.805 \| 0.2782 \| 1.4276 \| 0.805 \| 0.7899 \| 0.1529 \| 0.0471 \| \| 0.2616 \| 80.0 \| 560 \| 0.6145 \| 0.805 \| 0.2783 \| 1.4278 \| 0.805 \| 0.7899 \| 0.1527 \| 0.0471 \| \| 0.2616 \| 81.0 \| 567 \| 0.6147 \| 0.8 \| 0.2783 \| 1.4277 \| 0.8000 \| 0.7846 \| 0.1483 \| 0.0472 \| \| 0.2616 \| 82.0 \| 574 \| 0.6146 \| 0.8 \| 0.2783 \| 1.4275 \| 0.8000 \| 0.7846 \| 0.1623 \| 0.0473 \| \| 0.2616 \| 83.0 \| 581 \| 0.6145 \| 0.8 \| 0.2783 \| 1.4274 \| 0.8000 \| 0.7846 \| 0.1571 \| 0.0473 \| \| 0.2616 \| 84.0 \| 588 \| 0.6146 \| 0.8 \| 0.2782 \| 1.4276 \| 0.8000 \| 0.7846 \| 0.1538 \| 0.0473 \| \| 0.2616 \| 85.0 \| 595 \| 0.6146 \| 0.805 \| 0.2783 \| 1.4274 \| 0.805 \| 0.7899 \| 0.1493 \| 0.0471 \| \| 0.2616 \| 86.0 \| 602 \| 0.6147 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1627 \| 0.0473 \| \| 0.2616 \| 87.0 \| 609 \| 0.6146 \| 0.8 \| 0.2783 \| 1.4270 \| 0.8000 \| 0.7846 \| 0.1623 \| 0.0472 \| \| 0.2616 \| 88.0 \| 616 \| 0.6145 \| 0.805 \| 0.2783 \| 1.4272 \| 0.805 \| 0.7899 \| 0.1579 \| 0.0470 \| \| 0.2616 \| 89.0 \| 623 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4272 \| 0.8000 \| 0.7846 \| 0.1627 \| 0.0474 \| \| 0.2616 \| 90.0 \| 630 \| 0.6147 \| 0.8 \| 0.2783 \| 1.4270 \| 0.8000 \| 0.7846 \| 0.1536 \| 0.0473 \| \| 0.2616 \| 91.0 \| 637 \| 0.6147 \| 0.8 \| 0.2784 \| 1.4268 \| 0.8000 \| 0.7846 \| 0.1627 \| 0.0475 \| \| 0.2616 \| 92.0 \| 644 \| 0.6145 \| 0.805 \| 0.2783 \| 1.4268 \| 0.805 \| 0.7899 \| 0.1582 \| 0.0471 \| \| 0.2616 \| 93.0 \| 651 \| 0.6145 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| \| 0.2616 \| 94.0 \| 658 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4268 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0473 \| \| 0.2616 \| 95.0 \| 665 \| 0.6147 \| 0.8 \| 0.2784 \| 1.4268 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0473 \| \| 0.2616 \| 96.0 \| 672 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| \| 0.2616 \| 97.0 \| 679 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| \| 0.2616 \| 98.0 \| 686 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4269 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| \| 0.2616 \| 99.0 \| 693 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4268 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| \| 0.2616 \| 100.0 \| 700 \| 0.6146 \| 0.8 \| 0.2784 \| 1.4268 \| 0.8000 \| 0.7846 \| 0.1626 \| 0.0474 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_small_sgd_0001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_0001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3702 - Accuracy: 0.2683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5914 \| 1.0 \| 28 \| 1.5102 \| 0.2195 \| \| 1.502 \| 2.0 \| 56 \| 1.4998 \| 0.2439 \| \| 1.5359 \| 3.0 \| 84 \| 1.4897 \| 0.2439 \| \| 1.4953 \| 4.0 \| 112 \| 1.4806 \| 0.2195 \| \| 1.505 \| 5.0 \| 140 \| 1.4721 \| 0.2439 \| \| 1.5366 \| 6.0 \| 168 \| 1.4645 \| 0.2439 \| \| 1.5251 \| 7.0 \| 196 \| 1.4572 \| 0.2439 \| \| 1.4698 \| 8.0 \| 224 \| 1.4506 \| 0.2439 \| \| 1.4915 \| 9.0 \| 252 \| 1.4443 \| 0.2439 \| \| 1.4618 \| 10.0 \| 280 \| 1.4384 \| 0.2439 \| \| 1.4473 \| 11.0 \| 308 \| 1.4329 \| 0.2439 \| \| 1.4682 \| 12.0 \| 336 \| 1.4279 \| 0.2439 \| \| 1.4426 \| 13.0 \| 364 \| 1.4233 \| 0.2439 \| \| 1.4128 \| 14.0 \| 392 \| 1.4190 \| 0.2683 \| \| 1.4363 \| 15.0 \| 420 \| 1.4150 \| 0.2683 \| \| 1.4383 \| 16.0 \| 448 \| 1.4113 \| 0.2683 \| \| 1.4168 \| 17.0 \| 476 \| 1.4079 \| 0.2683 \| \| 1.4317 \| 18.0 \| 504 \| 1.4047 \| 0.2683 \| \| 1.4208 \| 19.0 \| 532 \| 1.4016 \| 0.2927 \| \| 1.4021 \| 20.0 \| 560 \| 1.3989 \| 0.2927 \| \| 1.4325 \| 21.0 \| 588 \| 1.3963 \| 0.2927 \| \| 1.4072 \| 22.0 \| 616 \| 1.3940 \| 0.2927 \| \| 1.3729 \| 23.0 \| 644 \| 1.3918 \| 0.2927 \| \| 1.3955 \| 24.0 \| 672 \| 1.3898 \| 0.2927 \| \| 1.3868 \| 25.0 \| 700 \| 1.3879 \| 0.2927 \| \| 1.3985 \| 26.0 \| 728 \| 1.3861 \| 0.2683 \| \| 1.3854 \| 27.0 \| 756 \| 1.3845 \| 0.2683 \| \| 1.3968 \| 28.0 \| 784 \| 1.3830 \| 0.2683 \| \| 1.3689 \| 29.0 \| 812 \| 1.3816 \| 0.2683 \| \| 1.4069 \| 30.0 \| 840 \| 1.3803 \| 0.2683 \| \| 1.387 \| 31.0 \| 868 \| 1.3791 \| 0.2683 \| \| 1.3786 \| 32.0 \| 896 \| 1.3780 \| 0.2683 \| \| 1.3773 \| 33.0 \| 924 \| 1.3769 \| 0.2683 \| \| 1.3779 \| 34.0 \| 952 \| 1.3760 \| 0.2683 \| \| 1.3797 \| 35.0 \| 980 \| 1.3751 \| 0.2683 \| \| 1.3671 \| 36.0 \| 1008 \| 1.3744 \| 0.2683 \| \| 1.3638 \| 37.0 \| 1036 \| 1.3737 \| 0.2683 \| \| 1.3614 \| 38.0 \| 1064 \| 1.3731 \| 0.2683 \| \| 1.3646 \| 39.0 \| 1092 \| 1.3725 \| 0.2683 \| \| 1.3609 \| 40.0 \| 1120 \| 1.3720 \| 0.2683 \| \| 1.3899 \| 41.0 \| 1148 \| 1.3716 \| 0.2683 \| \| 1.3896 \| 42.0 \| 1176 \| 1.3712 \| 0.2683 \| \| 1.3725 \| 43.0 \| 1204 \| 1.3709 \| 0.2683 \| \| 1.3896 \| 44.0 \| 1232 \| 1.3706 \| 0.2683 \| \| 1.3695 \| 45.0 \| 1260 \| 1.3704 \| 0.2683 \| \| 1.3698 \| 46.0 \| 1288 \| 1.3703 \| 0.2683 \| \| 1.3813 \| 47.0 \| 1316 \| 1.3702 \| 0.2683 \| \| 1.3636 \| 48.0 \| 1344 \| 1.3702 \| 0.2683 \| \| 1.3528 \| 49.0 \| 1372 \| 1.3702 \| 0.2683 \| \| 1.3747 \| 50.0 \| 1400 \| 1.3702 \| 0.2683 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2065 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.469 \| 1.0 \| 27 \| 1.4260 \| 0.2444 \| \| 1.3863 \| 2.0 \| 54 \| 1.3825 \| 0.2889 \| \| 1.3604 \| 3.0 \| 81 \| 1.3624 \| 0.3333 \| \| 1.3505 \| 4.0 \| 108 \| 1.3566 \| 0.4 \| \| 1.2837 \| 5.0 \| 135 \| 1.3537 \| 0.4 \| \| 1.2588 \| 6.0 \| 162 \| 1.3503 \| 0.4 \| \| 1.243 \| 7.0 \| 189 \| 1.3481 \| 0.4 \| \| 1.2286 \| 8.0 \| 216 \| 1.3453 \| 0.4222 \| \| 1.1857 \| 9.0 \| 243 \| 1.3417 \| 0.4222 \| \| 1.1652 \| 10.0 \| 270 \| 1.3373 \| 0.4222 \| \| 1.135 \| 11.0 \| 297 \| 1.3328 \| 0.4222 \| \| 1.1257 \| 12.0 \| 324 \| 1.3276 \| 0.4222 \| \| 1.1465 \| 13.0 \| 351 \| 1.3209 \| 0.4444 \| \| 1.0952 \| 14.0 \| 378 \| 1.3151 \| 0.4444 \| \| 1.0919 \| 15.0 \| 405 \| 1.3098 \| 0.4444 \| \| 1.0847 \| 16.0 \| 432 \| 1.3049 \| 0.4222 \| \| 1.0446 \| 17.0 \| 459 \| 1.2985 \| 0.4667 \| \| 1.0054 \| 18.0 \| 486 \| 1.2928 \| 0.4444 \| \| 1.0169 \| 19.0 \| 513 \| 1.2866 \| 0.4222 \| \| 1.0001 \| 20.0 \| 540 \| 1.2827 \| 0.4222 \| \| 0.9603 \| 21.0 \| 567 \| 1.2785 \| 0.4222 \| \| 0.9735 \| 22.0 \| 594 \| 1.2740 \| 0.4444 \| \| 0.9444 \| 23.0 \| 621 \| 1.2697 \| 0.4667 \| \| 0.9156 \| 24.0 \| 648 \| 1.2621 \| 0.4444 \| \| 0.9658 \| 25.0 \| 675 \| 1.2568 \| 0.4444 \| \| 0.9699 \| 26.0 \| 702 \| 1.2527 \| 0.4444 \| \| 0.8985 \| 27.0 \| 729 \| 1.2504 \| 0.4444 \| \| 0.8958 \| 28.0 \| 756 \| 1.2453 \| 0.4222 \| \| 0.9214 \| 29.0 \| 783 \| 1.2429 \| 0.4444 \| \| 0.9068 \| 30.0 \| 810 \| 1.2374 \| 0.4222 \| \| 0.8659 \| 31.0 \| 837 \| 1.2336 \| 0.4222 \| \| 0.8774 \| 32.0 \| 864 \| 1.2303 \| 0.4222 \| \| 0.8587 \| 33.0 \| 891 \| 1.2279 \| 0.4222 \| \| 0.8719 \| 34.0 \| 918 \| 1.2241 \| 0.4222 \| \| 0.8448 \| 35.0 \| 945 \| 1.2216 \| 0.4222 \| \| 0.8563 \| 36.0 \| 972 \| 1.2203 \| 0.4222 \| \| 0.8555 \| 37.0 \| 999 \| 1.2179 \| 0.4222 \| \| 0.8252 \| 38.0 \| 1026 \| 1.2169 \| 0.4222 \| \| 0.83 \| 39.0 \| 1053 \| 1.2146 \| 0.4222 \| \| 0.8062 \| 40.0 \| 1080 \| 1.2129 \| 0.4222 \| \| 0.8472 \| 41.0 \| 1107 \| 1.2110 \| 0.4222 \| \| 0.8075 \| 42.0 \| 1134 \| 1.2099 \| 0.4222 \| \| 0.8415 \| 43.0 \| 1161 \| 1.2087 \| 0.4222 \| \| 0.8064 \| 44.0 \| 1188 \| 1.2081 \| 0.4444 \| \| 0.8219 \| 45.0 \| 1215 \| 1.2076 \| 0.4444 \| \| 0.8297 \| 46.0 \| 1242 \| 1.2069 \| 0.4444 \| \| 0.8108 \| 47.0 \| 1269 \| 1.2066 \| 0.4444 \| \| 0.8128 \| 48.0 \| 1296 \| 1.2065 \| 0.4444 \| \| 0.8385 \| 49.0 \| 1323 \| 1.2065 \| 0.4444 \| \| 0.8247 \| 50.0 \| 1350 \| 1.2065 \| 0.4444 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3629 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4659 \| 1.0 \| 27 \| 1.4409 \| 0.2889 \| \| 1.3866 \| 2.0 \| 54 \| 1.4026 \| 0.3556 \| \| 1.3486 \| 3.0 \| 81 \| 1.3816 \| 0.3333 \| \| 1.3477 \| 4.0 \| 108 \| 1.3676 \| 0.3111 \| \| 1.2816 \| 5.0 \| 135 \| 1.3557 \| 0.3333 \| \| 1.2558 \| 6.0 \| 162 \| 1.3444 \| 0.3556 \| \| 1.2259 \| 7.0 \| 189 \| 1.3343 \| 0.3556 \| \| 1.2042 \| 8.0 \| 216 \| 1.3245 \| 0.3556 \| \| 1.1683 \| 9.0 \| 243 \| 1.3158 \| 0.4 \| \| 1.1515 \| 10.0 \| 270 \| 1.3086 \| 0.4222 \| \| 1.1156 \| 11.0 \| 297 \| 1.3037 \| 0.4222 \| \| 1.1061 \| 12.0 \| 324 \| 1.2999 \| 0.4444 \| \| 1.0903 \| 13.0 \| 351 \| 1.3002 \| 0.4444 \| \| 1.0661 \| 14.0 \| 378 \| 1.3028 \| 0.4444 \| \| 1.0598 \| 15.0 \| 405 \| 1.3085 \| 0.4444 \| \| 1.0378 \| 16.0 \| 432 \| 1.3130 \| 0.4444 \| \| 1.0191 \| 17.0 \| 459 \| 1.3179 \| 0.4444 \| \| 0.9884 \| 18.0 \| 486 \| 1.3238 \| 0.4444 \| \| 0.9629 \| 19.0 \| 513 \| 1.3282 \| 0.4444 \| \| 0.9575 \| 20.0 \| 540 \| 1.3319 \| 0.4222 \| \| 0.9397 \| 21.0 \| 567 \| 1.3353 \| 0.4222 \| \| 0.9296 \| 22.0 \| 594 \| 1.3380 \| 0.4222 \| \| 0.9149 \| 23.0 \| 621 \| 1.3408 \| 0.4222 \| \| 0.9023 \| 24.0 \| 648 \| 1.3446 \| 0.4222 \| \| 0.8747 \| 25.0 \| 675 \| 1.3454 \| 0.4667 \| \| 0.9184 \| 26.0 \| 702 \| 1.3472 \| 0.4444 \| \| 0.8454 \| 27.0 \| 729 \| 1.3479 \| 0.4444 \| \| 0.8505 \| 28.0 \| 756 \| 1.3510 \| 0.4444 \| \| 0.8567 \| 29.0 \| 783 \| 1.3517 \| 0.4444 \| \| 0.8854 \| 30.0 \| 810 \| 1.3544 \| 0.4667 \| \| 0.834 \| 31.0 \| 837 \| 1.3546 \| 0.4444 \| \| 0.8438 \| 32.0 \| 864 \| 1.3560 \| 0.4444 \| \| 0.8236 \| 33.0 \| 891 \| 1.3564 \| 0.4444 \| \| 0.8208 \| 34.0 \| 918 \| 1.3570 \| 0.4444 \| \| 0.8066 \| 35.0 \| 945 \| 1.3589 \| 0.4444 \| \| 0.8073 \| 36.0 \| 972 \| 1.3591 \| 0.4444 \| \| 0.8089 \| 37.0 \| 999 \| 1.3595 \| 0.4444 \| \| 0.777 \| 38.0 \| 1026 \| 1.3599 \| 0.4444 \| \| 0.7828 \| 39.0 \| 1053 \| 1.3610 \| 0.4444 \| \| 0.787 \| 40.0 \| 1080 \| 1.3609 \| 0.4444 \| \| 0.8016 \| 41.0 \| 1107 \| 1.3612 \| 0.4444 \| \| 0.7822 \| 42.0 \| 1134 \| 1.3619 \| 0.4444 \| \| 0.8105 \| 43.0 \| 1161 \| 1.3621 \| 0.4444 \| \| 0.7646 \| 44.0 \| 1188 \| 1.3622 \| 0.4444 \| \| 0.7928 \| 45.0 \| 1215 \| 1.3624 \| 0.4444 \| \| 0.7714 \| 46.0 \| 1242 \| 1.3625 \| 0.4444 \| \| 0.7741 \| 47.0 \| 1269 \| 1.3627 \| 0.4444 \| \| 0.7688 \| 48.0 \| 1296 \| 1.3629 \| 0.4444 \| \| 0.834 \| 49.0 \| 1323 \| 1.3629 \| 0.4444 \| \| 0.7751 \| 50.0 \| 1350 \| 1.3629 \| 0.4444 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1692 - Accuracy: 0.4186 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4612 \| 1.0 \| 28 \| 1.5118 \| 0.2558 \| \| 1.3805 \| 2.0 \| 56 \| 1.4497 \| 0.2093 \| \| 1.3041 \| 3.0 \| 84 \| 1.4123 \| 0.2093 \| \| 1.3096 \| 4.0 \| 112 \| 1.3866 \| 0.2558 \| \| 1.3306 \| 5.0 \| 140 \| 1.3677 \| 0.2558 \| \| 1.2628 \| 6.0 \| 168 \| 1.3531 \| 0.2791 \| \| 1.2467 \| 7.0 \| 196 \| 1.3404 \| 0.2791 \| \| 1.2266 \| 8.0 \| 224 \| 1.3306 \| 0.3023 \| \| 1.194 \| 9.0 \| 252 \| 1.3214 \| 0.3023 \| \| 1.1663 \| 10.0 \| 280 \| 1.3122 \| 0.2791 \| \| 1.1821 \| 11.0 \| 308 \| 1.3053 \| 0.3023 \| \| 1.1428 \| 12.0 \| 336 \| 1.2975 \| 0.3023 \| \| 1.1221 \| 13.0 \| 364 \| 1.2914 \| 0.3023 \| \| 1.0981 \| 14.0 \| 392 \| 1.2852 \| 0.3023 \| \| 1.1081 \| 15.0 \| 420 \| 1.2792 \| 0.3488 \| \| 1.0434 \| 16.0 \| 448 \| 1.2755 \| 0.3488 \| \| 1.0661 \| 17.0 \| 476 \| 1.2676 \| 0.3721 \| \| 1.0349 \| 18.0 \| 504 \| 1.2624 \| 0.3721 \| \| 1.0251 \| 19.0 \| 532 \| 1.2563 \| 0.3721 \| \| 0.9878 \| 20.0 \| 560 \| 1.2508 \| 0.3721 \| \| 1.0276 \| 21.0 \| 588 \| 1.2459 \| 0.3721 \| \| 0.9848 \| 22.0 \| 616 \| 1.2419 \| 0.3721 \| \| 0.9825 \| 23.0 \| 644 \| 1.2362 \| 0.3721 \| \| 0.9289 \| 24.0 \| 672 \| 1.2312 \| 0.3721 \| \| 0.9221 \| 25.0 \| 700 \| 1.2274 \| 0.3721 \| \| 0.9187 \| 26.0 \| 728 \| 1.2222 \| 0.3721 \| \| 0.9248 \| 27.0 \| 756 \| 1.2177 \| 0.3953 \| \| 0.9505 \| 28.0 \| 784 \| 1.2135 \| 0.3721 \| \| 0.9022 \| 29.0 \| 812 \| 1.2094 \| 0.3721 \| \| 0.8445 \| 30.0 \| 840 \| 1.2060 \| 0.3721 \| \| 0.861 \| 31.0 \| 868 \| 1.2023 \| 0.3953 \| \| 0.9005 \| 32.0 \| 896 \| 1.1985 \| 0.3953 \| \| 0.8936 \| 33.0 \| 924 \| 1.1956 \| 0.3953 \| \| 0.8469 \| 34.0 \| 952 \| 1.1923 \| 0.3953 \| \| 0.9675 \| 35.0 \| 980 \| 1.1892 \| 0.3953 \| \| 0.8615 \| 36.0 \| 1008 \| 1.1869 \| 0.3953 \| \| 0.8944 \| 37.0 \| 1036 \| 1.1838 \| 0.3953 \| \| 0.8374 \| 38.0 \| 1064 \| 1.1820 \| 0.3953 \| \| 0.8431 \| 39.0 \| 1092 \| 1.1797 \| 0.3953 \| \| 0.8451 \| 40.0 \| 1120 \| 1.1776 \| 0.3953 \| \| 0.8614 \| 41.0 \| 1148 \| 1.1756 \| 0.3953 \| \| 0.8347 \| 42.0 \| 1176 \| 1.1741 \| 0.3953 \| \| 0.8159 \| 43.0 \| 1204 \| 1.1724 \| 0.4419 \| \| 0.8229 \| 44.0 \| 1232 \| 1.1713 \| 0.4419 \| \| 0.8046 \| 45.0 \| 1260 \| 1.1703 \| 0.4186 \| \| 0.8129 \| 46.0 \| 1288 \| 1.1698 \| 0.4186 \| \| 0.8156 \| 47.0 \| 1316 \| 1.1693 \| 0.4186 \| \| 0.8259 \| 48.0 \| 1344 \| 1.1692 \| 0.4186 \| \| 0.7943 \| 49.0 \| 1372 \| 1.1692 \| 0.4186 \| \| 0.8288 \| 50.0 \| 1400 \| 1.1692 \| 0.4186 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-small_tobacco3482_kd_MSE	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7746 - Accuracy: 0.81 - Brier Loss: 0.2775 - Nll: 1.1981 - F1 Micro: 0.81 - F1 Macro: 0.7980 - Ece: 0.1403 - Aurc: 0.0500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 7.0083 \| 0.14 \| 0.9250 \| 5.4655 \| 0.14 \| 0.1468 \| 0.2824 \| 0.8920 \| \| No log \| 2.0 \| 14 \| 5.8247 \| 0.35 \| 0.7844 \| 3.6804 \| 0.35 \| 0.2240 \| 0.2541 \| 0.5601 \| \| No log \| 3.0 \| 21 \| 4.1788 \| 0.49 \| 0.6140 \| 1.8305 \| 0.49 \| 0.4563 \| 0.2512 \| 0.2825 \| \| No log \| 4.0 \| 28 \| 2.7911 \| 0.66 \| 0.4534 \| 1.6541 \| 0.66 \| 0.5604 \| 0.2299 \| 0.1475 \| \| No log \| 5.0 \| 35 \| 2.3354 \| 0.74 \| 0.3892 \| 1.8678 \| 0.74 \| 0.6851 \| 0.2104 \| 0.0989 \| \| No log \| 6.0 \| 42 \| 1.9675 \| 0.73 \| 0.3585 \| 1.3943 \| 0.7300 \| 0.6822 \| 0.1846 \| 0.0930 \| \| No log \| 7.0 \| 49 \| 1.7187 \| 0.79 \| 0.3190 \| 1.3921 \| 0.79 \| 0.7510 \| 0.1739 \| 0.0760 \| \| No log \| 8.0 \| 56 \| 1.6507 \| 0.77 \| 0.3469 \| 1.3682 \| 0.7700 \| 0.7289 \| 0.1834 \| 0.0851 \| \| No log \| 9.0 \| 63 \| 1.2713 \| 0.79 \| 0.3040 \| 1.4042 \| 0.79 \| 0.7622 \| 0.1505 \| 0.0540 \| \| No log \| 10.0 \| 70 \| 1.1461 \| 0.805 \| 0.2852 \| 1.3953 \| 0.805 \| 0.7849 \| 0.1371 \| 0.0522 \| \| No log \| 11.0 \| 77 \| 1.1328 \| 0.81 \| 0.2713 \| 1.3113 \| 0.81 \| 0.7901 \| 0.1371 \| 0.0442 \| \| No log \| 12.0 \| 84 \| 1.2818 \| 0.8 \| 0.3192 \| 1.2680 \| 0.8000 \| 0.7808 \| 0.1674 \| 0.0725 \| \| No log \| 13.0 \| 91 \| 1.0493 \| 0.805 \| 0.2767 \| 1.2512 \| 0.805 \| 0.7846 \| 0.1451 \| 0.0535 \| \| No log \| 14.0 \| 98 \| 0.9657 \| 0.815 \| 0.2802 \| 1.1796 \| 0.815 \| 0.7965 \| 0.1680 \| 0.0487 \| \| No log \| 15.0 \| 105 \| 0.9910 \| 0.82 \| 0.2695 \| 1.3658 \| 0.82 \| 0.8000 \| 0.1400 \| 0.0475 \| \| No log \| 16.0 \| 112 \| 0.9828 \| 0.81 \| 0.2823 \| 1.3175 \| 0.81 \| 0.7974 \| 0.1390 \| 0.0549 \| \| No log \| 17.0 \| 119 \| 0.9279 \| 0.8 \| 0.2815 \| 1.3727 \| 0.8000 \| 0.7882 \| 0.1599 \| 0.0454 \| \| No log \| 18.0 \| 126 \| 1.0076 \| 0.805 \| 0.2929 \| 1.2999 \| 0.805 \| 0.7825 \| 0.1480 \| 0.0562 \| \| No log \| 19.0 \| 133 \| 0.9524 \| 0.82 \| 0.2705 \| 1.3029 \| 0.82 \| 0.8122 \| 0.1481 \| 0.0454 \| \| No log \| 20.0 \| 140 \| 1.0584 \| 0.795 \| 0.3010 \| 1.3019 \| 0.795 \| 0.7699 \| 0.1669 \| 0.0650 \| \| No log \| 21.0 \| 147 \| 0.9390 \| 0.805 \| 0.2775 \| 1.4073 \| 0.805 \| 0.7888 \| 0.1211 \| 0.0513 \| \| No log \| 22.0 \| 154 \| 0.9857 \| 0.81 \| 0.2895 \| 1.2894 \| 0.81 \| 0.7879 \| 0.1469 \| 0.0548 \| \| No log \| 23.0 \| 161 \| 0.9137 \| 0.795 \| 0.2809 \| 1.4461 \| 0.795 \| 0.7872 \| 0.1528 \| 0.0472 \| \| No log \| 24.0 \| 168 \| 0.8545 \| 0.815 \| 0.2844 \| 1.2582 \| 0.815 \| 0.7981 \| 0.1466 \| 0.0484 \| \| No log \| 25.0 \| 175 \| 0.8860 \| 0.81 \| 0.2766 \| 1.4525 \| 0.81 \| 0.8010 \| 0.1241 \| 0.0457 \| \| No log \| 26.0 \| 182 \| 0.8624 \| 0.83 \| 0.2813 \| 1.1993 \| 0.83 \| 0.8222 \| 0.1536 \| 0.0512 \| \| No log \| 27.0 \| 189 \| 0.9119 \| 0.805 \| 0.2894 \| 1.4164 \| 0.805 \| 0.7869 \| 0.1576 \| 0.0519 \| \| No log \| 28.0 \| 196 \| 0.9072 \| 0.82 \| 0.2753 \| 1.2927 \| 0.82 \| 0.8149 \| 0.1292 \| 0.0514 \| \| No log \| 29.0 \| 203 \| 0.8428 \| 0.8 \| 0.2805 \| 1.3065 \| 0.8000 \| 0.7820 \| 0.1368 \| 0.0502 \| \| No log \| 30.0 \| 210 \| 0.8696 \| 0.81 \| 0.2858 \| 1.2825 \| 0.81 \| 0.7989 \| 0.1454 \| 0.0524 \| \| No log \| 31.0 \| 217 \| 0.8542 \| 0.8 \| 0.2861 \| 1.2029 \| 0.8000 \| 0.7766 \| 0.1412 \| 0.0496 \| \| No log \| 32.0 \| 224 \| 0.8576 \| 0.805 \| 0.2896 \| 1.3371 \| 0.805 \| 0.7814 \| 0.1513 \| 0.0515 \| \| No log \| 33.0 \| 231 \| 0.8615 \| 0.8 \| 0.2859 \| 1.2347 \| 0.8000 \| 0.7826 \| 0.1473 \| 0.0522 \| \| No log \| 34.0 \| 238 \| 0.8474 \| 0.805 \| 0.2807 \| 1.3510 \| 0.805 \| 0.7946 \| 0.1493 \| 0.0524 \| \| No log \| 35.0 \| 245 \| 0.9058 \| 0.79 \| 0.3035 \| 1.2005 \| 0.79 \| 0.7768 \| 0.1497 \| 0.0553 \| \| No log \| 36.0 \| 252 \| 0.8461 \| 0.805 \| 0.2897 \| 1.2770 \| 0.805 \| 0.7906 \| 0.1599 \| 0.0513 \| \| No log \| 37.0 \| 259 \| 0.8461 \| 0.805 \| 0.2962 \| 1.1989 \| 0.805 \| 0.7912 \| 0.1527 \| 0.0533 \| \| No log \| 38.0 \| 266 \| 0.8646 \| 0.815 \| 0.2817 \| 1.3653 \| 0.815 \| 0.8031 \| 0.1355 \| 0.0499 \| \| No log \| 39.0 \| 273 \| 0.8306 \| 0.8 \| 0.2905 \| 1.1852 \| 0.8000 \| 0.7862 \| 0.1528 \| 0.0549 \| \| No log \| 40.0 \| 280 \| 0.8561 \| 0.815 \| 0.2838 \| 1.2577 \| 0.815 \| 0.8005 \| 0.1431 \| 0.0544 \| \| No log \| 41.0 \| 287 \| 0.8236 \| 0.805 \| 0.2836 \| 1.2093 \| 0.805 \| 0.7925 \| 0.1376 \| 0.0490 \| \| No log \| 42.0 \| 294 \| 0.8221 \| 0.805 \| 0.2853 \| 1.1929 \| 0.805 \| 0.7805 \| 0.1397 \| 0.0524 \| \| No log \| 43.0 \| 301 \| 0.7834 \| 0.815 \| 0.2666 \| 1.2720 \| 0.815 \| 0.8006 \| 0.1316 \| 0.0496 \| \| No log \| 44.0 \| 308 \| 0.8022 \| 0.8 \| 0.2839 \| 1.2009 \| 0.8000 \| 0.7870 \| 0.1457 \| 0.0514 \| \| No log \| 45.0 \| 315 \| 0.8009 \| 0.81 \| 0.2735 \| 1.3505 \| 0.81 \| 0.7970 \| 0.1359 \| 0.0494 \| \| No log \| 46.0 \| 322 \| 0.8029 \| 0.81 \| 0.2775 \| 1.1956 \| 0.81 \| 0.7983 \| 0.1476 \| 0.0509 \| \| No log \| 47.0 \| 329 \| 0.7979 \| 0.82 \| 0.2818 \| 1.2005 \| 0.82 \| 0.8049 \| 0.1466 \| 0.0488 \| \| No log \| 48.0 \| 336 \| 0.7763 \| 0.815 \| 0.2784 \| 1.1905 \| 0.815 \| 0.7970 \| 0.1358 \| 0.0512 \| \| No log \| 49.0 \| 343 \| 0.7917 \| 0.81 \| 0.2802 \| 1.2136 \| 0.81 \| 0.7989 \| 0.1429 \| 0.0486 \| \| No log \| 50.0 \| 350 \| 0.8223 \| 0.825 \| 0.2809 \| 1.1860 \| 0.825 \| 0.8042 \| 0.1567 \| 0.0520 \| \| No log \| 51.0 \| 357 \| 0.7952 \| 0.82 \| 0.2747 \| 1.2074 \| 0.82 \| 0.8078 \| 0.1377 \| 0.0484 \| \| No log \| 52.0 \| 364 \| 0.7868 \| 0.825 \| 0.2714 \| 1.2850 \| 0.825 \| 0.8170 \| 0.1371 \| 0.0476 \| \| No log \| 53.0 \| 371 \| 0.8111 \| 0.805 \| 0.2869 \| 1.1892 \| 0.805 \| 0.7954 \| 0.1467 \| 0.0524 \| \| No log \| 54.0 \| 378 \| 0.7739 \| 0.81 \| 0.2755 \| 1.1946 \| 0.81 \| 0.7953 \| 0.1567 \| 0.0486 \| \| No log \| 55.0 \| 385 \| 0.7930 \| 0.825 \| 0.2825 \| 1.2000 \| 0.825 \| 0.8087 \| 0.1546 \| 0.0518 \| \| No log \| 56.0 \| 392 \| 0.7826 \| 0.815 \| 0.2789 \| 1.1953 \| 0.815 \| 0.8031 \| 0.1353 \| 0.0514 \| \| No log \| 57.0 \| 399 \| 0.7716 \| 0.82 \| 0.2714 \| 1.3115 \| 0.82 \| 0.8079 \| 0.1207 \| 0.0470 \| \| No log \| 58.0 \| 406 \| 0.8036 \| 0.815 \| 0.2878 \| 1.1875 \| 0.815 \| 0.7945 \| 0.1469 \| 0.0531 \| \| No log \| 59.0 \| 413 \| 0.7714 \| 0.82 \| 0.2722 \| 1.2787 \| 0.82 \| 0.8128 \| 0.1264 \| 0.0467 \| \| No log \| 60.0 \| 420 \| 0.7671 \| 0.825 \| 0.2720 \| 1.2722 \| 0.825 \| 0.8136 \| 0.1378 \| 0.0476 \| \| No log \| 61.0 \| 427 \| 0.7885 \| 0.815 \| 0.2834 \| 1.1798 \| 0.815 \| 0.8007 \| 0.1480 \| 0.0526 \| \| No log \| 62.0 \| 434 \| 0.7621 \| 0.82 \| 0.2706 \| 1.3459 \| 0.82 \| 0.8102 \| 0.1156 \| 0.0482 \| \| No log \| 63.0 \| 441 \| 0.7691 \| 0.81 \| 0.2797 \| 1.1379 \| 0.81 \| 0.7959 \| 0.1429 \| 0.0506 \| \| No log \| 64.0 \| 448 \| 0.7699 \| 0.81 \| 0.2776 \| 1.1964 \| 0.81 \| 0.7974 \| 0.1473 \| 0.0494 \| \| No log \| 65.0 \| 455 \| 0.7693 \| 0.82 \| 0.2739 \| 1.2089 \| 0.82 \| 0.8106 \| 0.1390 \| 0.0481 \| \| No log \| 66.0 \| 462 \| 0.7891 \| 0.81 \| 0.2805 \| 1.1989 \| 0.81 \| 0.7927 \| 0.1530 \| 0.0513 \| \| No log \| 67.0 \| 469 \| 0.7806 \| 0.82 \| 0.2798 \| 1.2033 \| 0.82 \| 0.8068 \| 0.1408 \| 0.0485 \| \| No log \| 68.0 \| 476 \| 0.7877 \| 0.82 \| 0.2815 \| 1.1896 \| 0.82 \| 0.8054 \| 0.1376 \| 0.0501 \| \| No log \| 69.0 \| 483 \| 0.7649 \| 0.825 \| 0.2731 \| 1.1567 \| 0.825 \| 0.8155 \| 0.1371 \| 0.0479 \| \| No log \| 70.0 \| 490 \| 0.7740 \| 0.82 \| 0.2764 \| 1.1929 \| 0.82 \| 0.8107 \| 0.1250 \| 0.0511 \| \| No log \| 71.0 \| 497 \| 0.7657 \| 0.82 \| 0.2744 \| 1.2762 \| 0.82 \| 0.8068 \| 0.1374 \| 0.0488 \| \| 0.4804 \| 72.0 \| 504 \| 0.7887 \| 0.805 \| 0.2839 \| 1.1851 \| 0.805 \| 0.7914 \| 0.1524 \| 0.0513 \| \| 0.4804 \| 73.0 \| 511 \| 0.7662 \| 0.815 \| 0.2759 \| 1.1973 \| 0.815 \| 0.8010 \| 0.1395 \| 0.0496 \| \| 0.4804 \| 74.0 \| 518 \| 0.7706 \| 0.825 \| 0.2742 \| 1.2020 \| 0.825 \| 0.8196 \| 0.1398 \| 0.0492 \| \| 0.4804 \| 75.0 \| 525 \| 0.7780 \| 0.815 \| 0.2802 \| 1.1881 \| 0.815 \| 0.7970 \| 0.1392 \| 0.0505 \| \| 0.4804 \| 76.0 \| 532 \| 0.7731 \| 0.825 \| 0.2745 \| 1.2695 \| 0.825 \| 0.8152 \| 0.1548 \| 0.0485 \| \| 0.4804 \| 77.0 \| 539 \| 0.7743 \| 0.825 \| 0.2762 \| 1.2039 \| 0.825 \| 0.8109 \| 0.1326 \| 0.0490 \| \| 0.4804 \| 78.0 \| 546 \| 0.7782 \| 0.805 \| 0.2792 \| 1.2001 \| 0.805 \| 0.7905 \| 0.1381 \| 0.0506 \| \| 0.4804 \| 79.0 \| 553 \| 0.7786 \| 0.81 \| 0.2807 \| 1.1929 \| 0.81 \| 0.7980 \| 0.1394 \| 0.0505 \| \| 0.4804 \| 80.0 \| 560 \| 0.7759 \| 0.82 \| 0.2772 \| 1.1973 \| 0.82 \| 0.8081 \| 0.1296 \| 0.0494 \| \| 0.4804 \| 81.0 \| 567 \| 0.7703 \| 0.82 \| 0.2758 \| 1.2069 \| 0.82 \| 0.8096 \| 0.1405 \| 0.0491 \| \| 0.4804 \| 82.0 \| 574 \| 0.7749 \| 0.81 \| 0.2777 \| 1.1996 \| 0.81 \| 0.7980 \| 0.1502 \| 0.0501 \| \| 0.4804 \| 83.0 \| 581 \| 0.7768 \| 0.815 \| 0.2777 \| 1.2009 \| 0.815 \| 0.8052 \| 0.1237 \| 0.0496 \| \| 0.4804 \| 84.0 \| 588 \| 0.7761 \| 0.815 \| 0.2778 \| 1.1986 \| 0.815 \| 0.8008 \| 0.1333 \| 0.0495 \| \| 0.4804 \| 85.0 \| 595 \| 0.7771 \| 0.815 \| 0.2780 \| 1.1984 \| 0.815 \| 0.8008 \| 0.1335 \| 0.0497 \| \| 0.4804 \| 86.0 \| 602 \| 0.7755 \| 0.81 \| 0.2777 \| 1.1987 \| 0.81 \| 0.7980 \| 0.1327 \| 0.0501 \| \| 0.4804 \| 87.0 \| 609 \| 0.7749 \| 0.81 \| 0.2776 \| 1.1974 \| 0.81 \| 0.7980 \| 0.1261 \| 0.0499 \| \| 0.4804 \| 88.0 \| 616 \| 0.7746 \| 0.815 \| 0.2776 \| 1.1981 \| 0.815 \| 0.8052 \| 0.1238 \| 0.0497 \| \| 0.4804 \| 89.0 \| 623 \| 0.7744 \| 0.81 \| 0.2776 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1283 \| 0.0500 \| \| 0.4804 \| 90.0 \| 630 \| 0.7743 \| 0.81 \| 0.2774 \| 1.1987 \| 0.81 \| 0.7980 \| 0.1346 \| 0.0499 \| \| 0.4804 \| 91.0 \| 637 \| 0.7741 \| 0.81 \| 0.2774 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1379 \| 0.0499 \| \| 0.4804 \| 92.0 \| 644 \| 0.7742 \| 0.81 \| 0.2774 \| 1.1982 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0499 \| \| 0.4804 \| 93.0 \| 651 \| 0.7745 \| 0.81 \| 0.2775 \| 1.1982 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0500 \| \| 0.4804 \| 94.0 \| 658 \| 0.7746 \| 0.81 \| 0.2776 \| 1.1978 \| 0.81 \| 0.7980 \| 0.1316 \| 0.0500 \| \| 0.4804 \| 95.0 \| 665 \| 0.7745 \| 0.81 \| 0.2775 \| 1.1982 \| 0.81 \| 0.7980 \| 0.1380 \| 0.0499 \| \| 0.4804 \| 96.0 \| 672 \| 0.7746 \| 0.81 \| 0.2775 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1315 \| 0.0500 \| \| 0.4804 \| 97.0 \| 679 \| 0.7746 \| 0.81 \| 0.2775 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0500 \| \| 0.4804 \| 98.0 \| 686 \| 0.7746 \| 0.81 \| 0.2775 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0500 \| \| 0.4804 \| 99.0 \| 693 \| 0.7746 \| 0.81 \| 0.2775 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0500 \| \| 0.4804 \| 100.0 \| 700 \| 0.7746 \| 0.81 \| 0.2775 \| 1.1981 \| 0.81 \| 0.7980 \| 0.1403 \| 0.0500 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_small_sgd_001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0586 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.487 \| 1.0 \| 28 \| 1.4172 \| 0.3095 \| \| 1.4655 \| 2.0 \| 56 \| 1.3829 \| 0.3333 \| \| 1.3519 \| 3.0 \| 84 \| 1.3645 \| 0.2619 \| \| 1.3235 \| 4.0 \| 112 \| 1.3524 \| 0.2619 \| \| 1.3019 \| 5.0 \| 140 \| 1.3421 \| 0.2857 \| \| 1.2683 \| 6.0 \| 168 \| 1.3287 \| 0.2857 \| \| 1.2448 \| 7.0 \| 196 \| 1.3147 \| 0.2857 \| \| 1.2154 \| 8.0 \| 224 \| 1.3011 \| 0.2619 \| \| 1.1886 \| 9.0 \| 252 \| 1.2876 \| 0.3571 \| \| 1.1547 \| 10.0 \| 280 \| 1.2739 \| 0.3810 \| \| 1.1374 \| 11.0 \| 308 \| 1.2618 \| 0.3810 \| \| 1.1111 \| 12.0 \| 336 \| 1.2488 \| 0.3810 \| \| 1.1298 \| 13.0 \| 364 \| 1.2398 \| 0.4048 \| \| 1.0797 \| 14.0 \| 392 \| 1.2302 \| 0.4048 \| \| 1.0414 \| 15.0 \| 420 \| 1.2217 \| 0.4286 \| \| 1.061 \| 16.0 \| 448 \| 1.2120 \| 0.4286 \| \| 1.0634 \| 17.0 \| 476 \| 1.2016 \| 0.4524 \| \| 1.0054 \| 18.0 \| 504 \| 1.1928 \| 0.4524 \| \| 0.9762 \| 19.0 \| 532 \| 1.1844 \| 0.4524 \| \| 1.0106 \| 20.0 \| 560 \| 1.1764 \| 0.4524 \| \| 0.9235 \| 21.0 \| 588 \| 1.1685 \| 0.4524 \| \| 0.9458 \| 22.0 \| 616 \| 1.1599 \| 0.4762 \| \| 0.9326 \| 23.0 \| 644 \| 1.1543 \| 0.5 \| \| 0.9222 \| 24.0 \| 672 \| 1.1465 \| 0.5 \| \| 0.8846 \| 25.0 \| 700 \| 1.1391 \| 0.5 \| \| 0.8795 \| 26.0 \| 728 \| 1.1307 \| 0.4762 \| \| 0.8711 \| 27.0 \| 756 \| 1.1242 \| 0.5238 \| \| 0.8921 \| 28.0 \| 784 \| 1.1184 \| 0.5238 \| \| 0.8796 \| 29.0 \| 812 \| 1.1133 \| 0.5238 \| \| 0.8567 \| 30.0 \| 840 \| 1.1054 \| 0.5238 \| \| 0.8632 \| 31.0 \| 868 \| 1.1011 \| 0.5238 \| \| 0.8179 \| 32.0 \| 896 \| 1.0965 \| 0.5238 \| \| 0.8418 \| 33.0 \| 924 \| 1.0917 \| 0.5238 \| \| 0.8097 \| 34.0 \| 952 \| 1.0866 \| 0.5238 \| \| 0.8474 \| 35.0 \| 980 \| 1.0818 \| 0.5238 \| \| 0.7989 \| 36.0 \| 1008 \| 1.0776 \| 0.5238 \| \| 0.7935 \| 37.0 \| 1036 \| 1.0750 \| 0.5238 \| \| 0.8104 \| 38.0 \| 1064 \| 1.0725 \| 0.5238 \| \| 0.8018 \| 39.0 \| 1092 \| 1.0698 \| 0.5238 \| \| 0.797 \| 40.0 \| 1120 \| 1.0673 \| 0.5238 \| \| 0.8004 \| 41.0 \| 1148 \| 1.0654 \| 0.5238 \| \| 0.775 \| 42.0 \| 1176 \| 1.0641 \| 0.5 \| \| 0.7606 \| 43.0 \| 1204 \| 1.0623 \| 0.5 \| \| 0.7649 \| 44.0 \| 1232 \| 1.0613 \| 0.5 \| \| 0.7627 \| 45.0 \| 1260 \| 1.0601 \| 0.5 \| \| 0.7807 \| 46.0 \| 1288 \| 1.0595 \| 0.5 \| \| 0.7697 \| 47.0 \| 1316 \| 1.0588 \| 0.5 \| \| 0.7683 \| 48.0 \| 1344 \| 1.0586 \| 0.5 \| \| 0.783 \| 49.0 \| 1372 \| 1.0586 \| 0.5 \| \| 0.7862 \| 50.0 \| 1400 \| 1.0586 \| 0.5 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_small_sgd_001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_small_sgd_001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0219 - Accuracy: 0.5854 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5389 \| 1.0 \| 28 \| 1.4343 \| 0.2439 \| \| 1.3921 \| 2.0 \| 56 \| 1.3847 \| 0.2683 \| \| 1.3749 \| 3.0 \| 84 \| 1.3585 \| 0.3659 \| \| 1.3126 \| 4.0 \| 112 \| 1.3409 \| 0.3659 \| \| 1.3117 \| 5.0 \| 140 \| 1.3251 \| 0.3659 \| \| 1.3121 \| 6.0 \| 168 \| 1.3119 \| 0.3415 \| \| 1.2628 \| 7.0 \| 196 \| 1.2980 \| 0.3415 \| \| 1.2308 \| 8.0 \| 224 \| 1.2843 \| 0.3659 \| \| 1.2428 \| 9.0 \| 252 \| 1.2711 \| 0.4146 \| \| 1.1961 \| 10.0 \| 280 \| 1.2591 \| 0.4146 \| \| 1.1795 \| 11.0 \| 308 \| 1.2486 \| 0.3902 \| \| 1.1594 \| 12.0 \| 336 \| 1.2381 \| 0.3902 \| \| 1.1371 \| 13.0 \| 364 \| 1.2260 \| 0.3902 \| \| 1.1217 \| 14.0 \| 392 \| 1.2140 \| 0.4390 \| \| 1.0975 \| 15.0 \| 420 \| 1.2018 \| 0.4634 \| \| 1.1139 \| 16.0 \| 448 \| 1.1910 \| 0.4878 \| \| 1.0797 \| 17.0 \| 476 \| 1.1802 \| 0.4878 \| \| 1.0813 \| 18.0 \| 504 \| 1.1682 \| 0.4878 \| \| 1.0619 \| 19.0 \| 532 \| 1.1572 \| 0.4878 \| \| 1.0398 \| 20.0 \| 560 \| 1.1467 \| 0.5122 \| \| 1.0215 \| 21.0 \| 588 \| 1.1362 \| 0.5122 \| \| 1.0014 \| 22.0 \| 616 \| 1.1280 \| 0.5366 \| \| 1.0047 \| 23.0 \| 644 \| 1.1216 \| 0.5610 \| \| 0.9823 \| 24.0 \| 672 \| 1.1144 \| 0.5610 \| \| 0.9814 \| 25.0 \| 700 \| 1.1058 \| 0.5610 \| \| 0.9822 \| 26.0 \| 728 \| 1.0976 \| 0.5610 \| \| 0.9448 \| 27.0 \| 756 \| 1.0916 \| 0.5366 \| \| 0.9805 \| 28.0 \| 784 \| 1.0839 \| 0.5366 \| \| 0.9187 \| 29.0 \| 812 \| 1.0780 \| 0.5366 \| \| 0.9659 \| 30.0 \| 840 \| 1.0725 \| 0.5366 \| \| 0.9135 \| 31.0 \| 868 \| 1.0663 \| 0.5610 \| \| 0.889 \| 32.0 \| 896 \| 1.0628 \| 0.5610 \| \| 0.9089 \| 33.0 \| 924 \| 1.0587 \| 0.5610 \| \| 0.9062 \| 34.0 \| 952 \| 1.0524 \| 0.5610 \| \| 0.9029 \| 35.0 \| 980 \| 1.0479 \| 0.5610 \| \| 0.8924 \| 36.0 \| 1008 \| 1.0439 \| 0.5854 \| \| 0.8694 \| 37.0 \| 1036 \| 1.0402 \| 0.5854 \| \| 0.8578 \| 38.0 \| 1064 \| 1.0365 \| 0.5610 \| \| 0.8992 \| 39.0 \| 1092 \| 1.0340 \| 0.5854 \| \| 0.8586 \| 40.0 \| 1120 \| 1.0317 \| 0.5854 \| \| 0.8737 \| 41.0 \| 1148 \| 1.0296 \| 0.5854 \| \| 0.8517 \| 42.0 \| 1176 \| 1.0278 \| 0.5854 \| \| 0.8537 \| 43.0 \| 1204 \| 1.0257 \| 0.5854 \| \| 0.8642 \| 44.0 \| 1232 \| 1.0243 \| 0.5854 \| \| 0.871 \| 45.0 \| 1260 \| 1.0234 \| 0.5854 \| \| 0.8594 \| 46.0 \| 1288 \| 1.0226 \| 0.5854 \| \| 0.8675 \| 47.0 \| 1316 \| 1.0221 \| 0.5854 \| \| 0.874 \| 48.0 \| 1344 \| 1.0219 \| 0.5854 \| \| 0.8459 \| 49.0 \| 1372 \| 1.0219 \| 0.5854 \| \| 0.8538 \| 50.0 \| 1400 \| 1.0219 \| 0.5854 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
piecurus/convnext-tiny-224-finetuned-eurosat-albumentations	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2257 - Accuracy: 0.9622 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2446 \| 1.0 \| 190 \| 0.2257 \| 0.9622 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
hkivancoral/hushem_5x_deit_base_adamax_0001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_0001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 - Accuracy: 0.7111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.238 \| 1.0 \| 27 \| 1.3398 \| 0.2667 \| \| 0.9269 \| 2.0 \| 54 \| 1.2685 \| 0.4444 \| \| 0.6591 \| 3.0 \| 81 \| 1.1740 \| 0.5333 \| \| 0.5112 \| 4.0 \| 108 \| 1.1289 \| 0.5556 \| \| 0.3169 \| 5.0 \| 135 \| 1.0720 \| 0.5778 \| \| 0.2415 \| 6.0 \| 162 \| 0.9458 \| 0.6 \| \| 0.1769 \| 7.0 \| 189 \| 0.9250 \| 0.6 \| \| 0.0983 \| 8.0 \| 216 \| 0.8893 \| 0.6667 \| \| 0.0567 \| 9.0 \| 243 \| 0.8959 \| 0.7111 \| \| 0.0295 \| 10.0 \| 270 \| 1.0130 \| 0.5778 \| \| 0.0202 \| 11.0 \| 297 \| 0.9509 \| 0.6889 \| \| 0.0113 \| 12.0 \| 324 \| 0.9586 \| 0.7111 \| \| 0.0094 \| 13.0 \| 351 \| 0.9844 \| 0.6889 \| \| 0.0072 \| 14.0 \| 378 \| 0.9965 \| 0.7333 \| \| 0.0063 \| 15.0 \| 405 \| 1.0087 \| 0.7111 \| \| 0.005 \| 16.0 \| 432 \| 1.0089 \| 0.6889 \| \| 0.0041 \| 17.0 \| 459 \| 1.0347 \| 0.6889 \| \| 0.004 \| 18.0 \| 486 \| 1.0569 \| 0.6889 \| \| 0.0034 \| 19.0 \| 513 \| 1.0522 \| 0.6889 \| \| 0.0031 \| 20.0 \| 540 \| 1.0681 \| 0.6889 \| \| 0.0027 \| 21.0 \| 567 \| 1.0686 \| 0.6889 \| \| 0.0026 \| 22.0 \| 594 \| 1.0745 \| 0.6889 \| \| 0.0023 \| 23.0 \| 621 \| 1.0948 \| 0.6889 \| \| 0.0022 \| 24.0 \| 648 \| 1.0979 \| 0.6889 \| \| 0.0021 \| 25.0 \| 675 \| 1.0958 \| 0.6889 \| \| 0.0021 \| 26.0 \| 702 \| 1.1008 \| 0.6889 \| \| 0.0018 \| 27.0 \| 729 \| 1.1079 \| 0.6889 \| \| 0.0017 \| 28.0 \| 756 \| 1.1114 \| 0.6889 \| \| 0.0019 \| 29.0 \| 783 \| 1.1187 \| 0.6889 \| \| 0.0017 \| 30.0 \| 810 \| 1.1246 \| 0.6889 \| \| 0.0016 \| 31.0 \| 837 \| 1.1229 \| 0.6889 \| \| 0.0016 \| 32.0 \| 864 \| 1.1290 \| 0.6889 \| \| 0.0014 \| 33.0 \| 891 \| 1.1312 \| 0.6889 \| \| 0.0015 \| 34.0 \| 918 \| 1.1349 \| 0.6889 \| \| 0.0014 \| 35.0 \| 945 \| 1.1402 \| 0.6889 \| \| 0.0013 \| 36.0 \| 972 \| 1.1442 \| 0.6889 \| \| 0.0013 \| 37.0 \| 999 \| 1.1434 \| 0.6889 \| \| 0.0012 \| 38.0 \| 1026 \| 1.1425 \| 0.7111 \| \| 0.0012 \| 39.0 \| 1053 \| 1.1512 \| 0.6889 \| \| 0.0011 \| 40.0 \| 1080 \| 1.1497 \| 0.6889 \| \| 0.0012 \| 41.0 \| 1107 \| 1.1525 \| 0.6889 \| \| 0.0012 \| 42.0 \| 1134 \| 1.1548 \| 0.6889 \| \| 0.0012 \| 43.0 \| 1161 \| 1.1577 \| 0.6889 \| \| 0.0011 \| 44.0 \| 1188 \| 1.1573 \| 0.6889 \| \| 0.0011 \| 45.0 \| 1215 \| 1.1575 \| 0.6889 \| \| 0.0011 \| 46.0 \| 1242 \| 1.1575 \| 0.7111 \| \| 0.0011 \| 47.0 \| 1269 \| 1.1575 \| 0.7111 \| \| 0.0011 \| 48.0 \| 1296 \| 1.1576 \| 0.7111 \| \| 0.0011 \| 49.0 \| 1323 \| 1.1576 \| 0.7111 \| \| 0.0011 \| 50.0 \| 1350 \| 1.1576 \| 0.7111 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_adamax_0001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_0001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3720 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2573 \| 1.0 \| 27 \| 1.3177 \| 0.3778 \| \| 0.9474 \| 2.0 \| 54 \| 1.2698 \| 0.4667 \| \| 0.6743 \| 3.0 \| 81 \| 1.1709 \| 0.5333 \| \| 0.53 \| 4.0 \| 108 \| 1.1238 \| 0.6 \| \| 0.3327 \| 5.0 \| 135 \| 1.1060 \| 0.6 \| \| 0.2187 \| 6.0 \| 162 \| 1.0991 \| 0.6444 \| \| 0.1497 \| 7.0 \| 189 \| 1.1072 \| 0.6444 \| \| 0.086 \| 8.0 \| 216 \| 1.1220 \| 0.6444 \| \| 0.0449 \| 9.0 \| 243 \| 1.1215 \| 0.6444 \| \| 0.0257 \| 10.0 \| 270 \| 1.1368 \| 0.6667 \| \| 0.0174 \| 11.0 \| 297 \| 1.1587 \| 0.6667 \| \| 0.0102 \| 12.0 \| 324 \| 1.1715 \| 0.6889 \| \| 0.0083 \| 13.0 \| 351 \| 1.2117 \| 0.6889 \| \| 0.0067 \| 14.0 \| 378 \| 1.2042 \| 0.6889 \| \| 0.0061 \| 15.0 \| 405 \| 1.2320 \| 0.6889 \| \| 0.0048 \| 16.0 \| 432 \| 1.2396 \| 0.6889 \| \| 0.0043 \| 17.0 \| 459 \| 1.2501 \| 0.6889 \| \| 0.0039 \| 18.0 \| 486 \| 1.2585 \| 0.6667 \| \| 0.0034 \| 19.0 \| 513 \| 1.2714 \| 0.6889 \| \| 0.0031 \| 20.0 \| 540 \| 1.2786 \| 0.6889 \| \| 0.0029 \| 21.0 \| 567 \| 1.2831 \| 0.6667 \| \| 0.0026 \| 22.0 \| 594 \| 1.2886 \| 0.6667 \| \| 0.0022 \| 23.0 \| 621 \| 1.2985 \| 0.6667 \| \| 0.0022 \| 24.0 \| 648 \| 1.3036 \| 0.6667 \| \| 0.002 \| 25.0 \| 675 \| 1.3071 \| 0.6667 \| \| 0.002 \| 26.0 \| 702 \| 1.3150 \| 0.6667 \| \| 0.0017 \| 27.0 \| 729 \| 1.3222 \| 0.6667 \| \| 0.0018 \| 28.0 \| 756 \| 1.3235 \| 0.6667 \| \| 0.0018 \| 29.0 \| 783 \| 1.3294 \| 0.6667 \| \| 0.0017 \| 30.0 \| 810 \| 1.3351 \| 0.6667 \| \| 0.0015 \| 31.0 \| 837 \| 1.3358 \| 0.6667 \| \| 0.0016 \| 32.0 \| 864 \| 1.3406 \| 0.6667 \| \| 0.0015 \| 33.0 \| 891 \| 1.3434 \| 0.6667 \| \| 0.0014 \| 34.0 \| 918 \| 1.3481 \| 0.6667 \| \| 0.0013 \| 35.0 \| 945 \| 1.3523 \| 0.6667 \| \| 0.0013 \| 36.0 \| 972 \| 1.3535 \| 0.6667 \| \| 0.0013 \| 37.0 \| 999 \| 1.3558 \| 0.6667 \| \| 0.0012 \| 38.0 \| 1026 \| 1.3590 \| 0.6667 \| \| 0.0012 \| 39.0 \| 1053 \| 1.3619 \| 0.6667 \| \| 0.0011 \| 40.0 \| 1080 \| 1.3634 \| 0.6667 \| \| 0.0012 \| 41.0 \| 1107 \| 1.3657 \| 0.6667 \| \| 0.0011 \| 42.0 \| 1134 \| 1.3669 \| 0.6667 \| \| 0.0011 \| 43.0 \| 1161 \| 1.3696 \| 0.6667 \| \| 0.0011 \| 44.0 \| 1188 \| 1.3699 \| 0.6667 \| \| 0.0011 \| 45.0 \| 1215 \| 1.3707 \| 0.6667 \| \| 0.0011 \| 46.0 \| 1242 \| 1.3712 \| 0.6667 \| \| 0.0011 \| 47.0 \| 1269 \| 1.3718 \| 0.6667 \| \| 0.0011 \| 48.0 \| 1296 \| 1.3720 \| 0.6667 \| \| 0.0011 \| 49.0 \| 1323 \| 1.3720 \| 0.6667 \| \| 0.0011 \| 50.0 \| 1350 \| 1.3720 \| 0.6667 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-small_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1084 - Accuracy: 0.825 - Brier Loss: 0.2907 - Nll: 1.2013 - F1 Micro: 0.825 - F1 Macro: 0.8171 - Ece: 0.1500 - Aurc: 0.0459 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 5.5631 \| 0.135 \| 0.9164 \| 5.3726 \| 0.135 \| 0.1126 \| 0.2543 \| 0.8397 \| \| No log \| 2.0 \| 14 \| 4.9048 \| 0.35 \| 0.8238 \| 3.0911 \| 0.35 \| 0.2637 \| 0.3348 \| 0.6709 \| \| No log \| 3.0 \| 21 \| 4.1439 \| 0.49 \| 0.6650 \| 1.8580 \| 0.49 \| 0.4532 \| 0.2990 \| 0.2902 \| \| No log \| 4.0 \| 28 \| 3.5518 \| 0.66 \| 0.4867 \| 1.6397 \| 0.66 \| 0.6303 \| 0.2902 \| 0.1489 \| \| No log \| 5.0 \| 35 \| 3.3371 \| 0.755 \| 0.3981 \| 1.6213 \| 0.755 \| 0.7261 \| 0.2670 \| 0.0984 \| \| No log \| 6.0 \| 42 \| 3.4978 \| 0.69 \| 0.4211 \| 1.5668 \| 0.69 \| 0.6792 \| 0.2240 \| 0.1170 \| \| No log \| 7.0 \| 49 \| 3.0945 \| 0.795 \| 0.3094 \| 1.5507 \| 0.795 \| 0.7653 \| 0.1765 \| 0.0622 \| \| No log \| 8.0 \| 56 \| 3.0882 \| 0.775 \| 0.3056 \| 1.5470 \| 0.775 \| 0.7500 \| 0.1826 \| 0.0634 \| \| No log \| 9.0 \| 63 \| 3.1861 \| 0.745 \| 0.3331 \| 1.6432 \| 0.745 \| 0.7362 \| 0.1822 \| 0.0754 \| \| No log \| 10.0 \| 70 \| 2.9849 \| 0.81 \| 0.2789 \| 1.5850 \| 0.81 \| 0.7802 \| 0.1559 \| 0.0548 \| \| No log \| 11.0 \| 77 \| 3.0131 \| 0.795 \| 0.3012 \| 1.4820 \| 0.795 \| 0.7720 \| 0.1627 \| 0.0567 \| \| No log \| 12.0 \| 84 \| 2.9054 \| 0.795 \| 0.2734 \| 1.4141 \| 0.795 \| 0.7843 \| 0.1501 \| 0.0535 \| \| No log \| 13.0 \| 91 \| 2.9704 \| 0.815 \| 0.2720 \| 1.4241 \| 0.815 \| 0.8144 \| 0.1584 \| 0.0536 \| \| No log \| 14.0 \| 98 \| 2.9393 \| 0.815 \| 0.2627 \| 1.4735 \| 0.815 \| 0.7902 \| 0.1582 \| 0.0504 \| \| No log \| 15.0 \| 105 \| 3.0346 \| 0.805 \| 0.2963 \| 1.3649 \| 0.805 \| 0.7973 \| 0.1617 \| 0.0564 \| \| No log \| 16.0 \| 112 \| 2.9648 \| 0.79 \| 0.2839 \| 1.6270 \| 0.79 \| 0.7722 \| 0.1418 \| 0.0525 \| \| No log \| 17.0 \| 119 \| 3.0458 \| 0.82 \| 0.2960 \| 1.3476 \| 0.82 \| 0.8048 \| 0.1575 \| 0.0622 \| \| No log \| 18.0 \| 126 \| 2.8571 \| 0.82 \| 0.2754 \| 1.3958 \| 0.82 \| 0.8081 \| 0.1482 \| 0.0493 \| \| No log \| 19.0 \| 133 \| 2.9429 \| 0.775 \| 0.2971 \| 1.4302 \| 0.775 \| 0.7617 \| 0.1616 \| 0.0575 \| \| No log \| 20.0 \| 140 \| 2.8274 \| 0.825 \| 0.2698 \| 1.3759 \| 0.825 \| 0.8081 \| 0.1520 \| 0.0449 \| \| No log \| 21.0 \| 147 \| 2.8769 \| 0.81 \| 0.2713 \| 1.3604 \| 0.81 \| 0.8086 \| 0.1390 \| 0.0466 \| \| No log \| 22.0 \| 154 \| 2.8787 \| 0.805 \| 0.2694 \| 1.3016 \| 0.805 \| 0.7975 \| 0.1522 \| 0.0435 \| \| No log \| 23.0 \| 161 \| 2.8771 \| 0.825 \| 0.2646 \| 1.4753 \| 0.825 \| 0.8215 \| 0.1414 \| 0.0485 \| \| No log \| 24.0 \| 168 \| 2.8950 \| 0.805 \| 0.2774 \| 1.2783 \| 0.805 \| 0.7754 \| 0.1406 \| 0.0495 \| \| No log \| 25.0 \| 175 \| 2.9780 \| 0.825 \| 0.2829 \| 1.3207 \| 0.825 \| 0.8332 \| 0.1402 \| 0.0496 \| \| No log \| 26.0 \| 182 \| 2.8906 \| 0.82 \| 0.2653 \| 1.3097 \| 0.82 \| 0.8007 \| 0.1380 \| 0.0454 \| \| No log \| 27.0 \| 189 \| 2.9385 \| 0.82 \| 0.2778 \| 1.3039 \| 0.82 \| 0.8211 \| 0.1489 \| 0.0469 \| \| No log \| 28.0 \| 196 \| 2.8644 \| 0.83 \| 0.2618 \| 1.4004 \| 0.83 \| 0.8325 \| 0.1358 \| 0.0494 \| \| No log \| 29.0 \| 203 \| 2.8761 \| 0.82 \| 0.2720 \| 1.2220 \| 0.82 \| 0.8192 \| 0.1411 \| 0.0463 \| \| No log \| 30.0 \| 210 \| 2.8594 \| 0.83 \| 0.2620 \| 1.3323 \| 0.83 \| 0.8130 \| 0.1257 \| 0.0448 \| \| No log \| 31.0 \| 217 \| 2.8946 \| 0.825 \| 0.2658 \| 1.3388 \| 0.825 \| 0.8236 \| 0.1322 \| 0.0427 \| \| No log \| 32.0 \| 224 \| 2.8698 \| 0.825 \| 0.2712 \| 1.3141 \| 0.825 \| 0.8107 \| 0.1467 \| 0.0473 \| \| No log \| 33.0 \| 231 \| 2.8106 \| 0.83 \| 0.2563 \| 1.3750 \| 0.83 \| 0.8178 \| 0.1126 \| 0.0422 \| \| No log \| 34.0 \| 238 \| 2.9752 \| 0.8 \| 0.2881 \| 1.3007 \| 0.8000 \| 0.7902 \| 0.1522 \| 0.0499 \| \| No log \| 35.0 \| 245 \| 2.8919 \| 0.815 \| 0.2886 \| 1.3057 \| 0.815 \| 0.8149 \| 0.1472 \| 0.0468 \| \| No log \| 36.0 \| 252 \| 2.8863 \| 0.81 \| 0.2833 \| 1.1973 \| 0.81 \| 0.8006 \| 0.1453 \| 0.0458 \| \| No log \| 37.0 \| 259 \| 2.8283 \| 0.845 \| 0.2685 \| 1.2743 \| 0.845 \| 0.8438 \| 0.1481 \| 0.0451 \| \| No log \| 38.0 \| 266 \| 2.9174 \| 0.815 \| 0.2825 \| 1.2658 \| 0.815 \| 0.7965 \| 0.1408 \| 0.0530 \| \| No log \| 39.0 \| 273 \| 2.8837 \| 0.82 \| 0.2775 \| 1.2946 \| 0.82 \| 0.8050 \| 0.1440 \| 0.0472 \| \| No log \| 40.0 \| 280 \| 2.8585 \| 0.835 \| 0.2654 \| 1.2830 \| 0.835 \| 0.8169 \| 0.1450 \| 0.0467 \| \| No log \| 41.0 \| 287 \| 2.9323 \| 0.82 \| 0.2809 \| 1.2833 \| 0.82 \| 0.8085 \| 0.1342 \| 0.0490 \| \| No log \| 42.0 \| 294 \| 2.9525 \| 0.82 \| 0.2847 \| 1.2331 \| 0.82 \| 0.8055 \| 0.1352 \| 0.0481 \| \| No log \| 43.0 \| 301 \| 2.9005 \| 0.83 \| 0.2819 \| 1.2643 \| 0.83 \| 0.8225 \| 0.1548 \| 0.0482 \| \| No log \| 44.0 \| 308 \| 2.8388 \| 0.83 \| 0.2634 \| 1.2662 \| 0.83 \| 0.8152 \| 0.1286 \| 0.0460 \| \| No log \| 45.0 \| 315 \| 2.8962 \| 0.82 \| 0.2752 \| 1.3291 \| 0.82 \| 0.8127 \| 0.1442 \| 0.0496 \| \| No log \| 46.0 \| 322 \| 2.9479 \| 0.815 \| 0.2883 \| 1.2433 \| 0.815 \| 0.7968 \| 0.1540 \| 0.0523 \| \| No log \| 47.0 \| 329 \| 2.8795 \| 0.825 \| 0.2737 \| 1.2477 \| 0.825 \| 0.8260 \| 0.1295 \| 0.0447 \| \| No log \| 48.0 \| 336 \| 2.9872 \| 0.815 \| 0.2992 \| 1.2556 \| 0.815 \| 0.8029 \| 0.1379 \| 0.0510 \| \| No log \| 49.0 \| 343 \| 2.8156 \| 0.84 \| 0.2536 \| 1.2715 \| 0.8400 \| 0.8263 \| 0.1240 \| 0.0422 \| \| No log \| 50.0 \| 350 \| 2.9534 \| 0.81 \| 0.2924 \| 1.3383 \| 0.81 \| 0.7937 \| 0.1471 \| 0.0478 \| \| No log \| 51.0 \| 357 \| 2.8604 \| 0.855 \| 0.2549 \| 1.2566 \| 0.855 \| 0.8547 \| 0.1318 \| 0.0411 \| \| No log \| 52.0 \| 364 \| 2.9769 \| 0.825 \| 0.2828 \| 1.2325 \| 0.825 \| 0.8160 \| 0.1407 \| 0.0480 \| \| No log \| 53.0 \| 371 \| 2.8717 \| 0.84 \| 0.2635 \| 1.2511 \| 0.8400 \| 0.8342 \| 0.1254 \| 0.0434 \| \| No log \| 54.0 \| 378 \| 2.9313 \| 0.825 \| 0.2704 \| 1.2676 \| 0.825 \| 0.8159 \| 0.1310 \| 0.0477 \| \| No log \| 55.0 \| 385 \| 2.8552 \| 0.82 \| 0.2638 \| 1.2417 \| 0.82 \| 0.8031 \| 0.1490 \| 0.0435 \| \| No log \| 56.0 \| 392 \| 2.9680 \| 0.845 \| 0.2729 \| 1.2530 \| 0.845 \| 0.8414 \| 0.1349 \| 0.0452 \| \| No log \| 57.0 \| 399 \| 2.9440 \| 0.83 \| 0.2796 \| 1.2344 \| 0.83 \| 0.8222 \| 0.1367 \| 0.0450 \| \| No log \| 58.0 \| 406 \| 3.0577 \| 0.815 \| 0.2913 \| 1.2232 \| 0.815 \| 0.8068 \| 0.1447 \| 0.0488 \| \| No log \| 59.0 \| 413 \| 2.8861 \| 0.835 \| 0.2643 \| 1.2618 \| 0.835 \| 0.8280 \| 0.1354 \| 0.0422 \| \| No log \| 60.0 \| 420 \| 3.0007 \| 0.825 \| 0.2822 \| 1.2352 \| 0.825 \| 0.8136 \| 0.1342 \| 0.0449 \| \| No log \| 61.0 \| 427 \| 2.9368 \| 0.835 \| 0.2746 \| 1.2437 \| 0.835 \| 0.8258 \| 0.1402 \| 0.0437 \| \| No log \| 62.0 \| 434 \| 2.9202 \| 0.835 \| 0.2709 \| 1.2281 \| 0.835 \| 0.8258 \| 0.1435 \| 0.0435 \| \| No log \| 63.0 \| 441 \| 2.9720 \| 0.835 \| 0.2768 \| 1.2129 \| 0.835 \| 0.8354 \| 0.1444 \| 0.0460 \| \| No log \| 64.0 \| 448 \| 2.9993 \| 0.835 \| 0.2815 \| 1.2250 \| 0.835 \| 0.8245 \| 0.1526 \| 0.0451 \| \| No log \| 65.0 \| 455 \| 2.9628 \| 0.83 \| 0.2725 \| 1.2477 \| 0.83 \| 0.8190 \| 0.1405 \| 0.0439 \| \| No log \| 66.0 \| 462 \| 3.0418 \| 0.825 \| 0.2863 \| 1.2244 \| 0.825 \| 0.8142 \| 0.1447 \| 0.0473 \| \| No log \| 67.0 \| 469 \| 3.0196 \| 0.83 \| 0.2797 \| 1.2317 \| 0.83 \| 0.8223 \| 0.1450 \| 0.0463 \| \| No log \| 68.0 \| 476 \| 3.0227 \| 0.835 \| 0.2834 \| 1.2362 \| 0.835 \| 0.8270 \| 0.1416 \| 0.0446 \| \| No log \| 69.0 \| 483 \| 3.0343 \| 0.835 \| 0.2837 \| 1.2377 \| 0.835 \| 0.8310 \| 0.1423 \| 0.0455 \| \| No log \| 70.0 \| 490 \| 2.9982 \| 0.835 \| 0.2755 \| 1.2247 \| 0.835 \| 0.8245 \| 0.1306 \| 0.0443 \| \| No log \| 71.0 \| 497 \| 3.0230 \| 0.825 \| 0.2860 \| 1.2302 \| 0.825 \| 0.8171 \| 0.1376 \| 0.0464 \| \| 2.5595 \| 72.0 \| 504 \| 3.0254 \| 0.83 \| 0.2843 \| 1.2190 \| 0.83 \| 0.8222 \| 0.1386 \| 0.0463 \| \| 2.5595 \| 73.0 \| 511 \| 3.0295 \| 0.825 \| 0.2851 \| 1.2206 \| 0.825 \| 0.8192 \| 0.1417 \| 0.0462 \| \| 2.5595 \| 74.0 \| 518 \| 3.0381 \| 0.83 \| 0.2845 \| 1.2130 \| 0.83 \| 0.8243 \| 0.1423 \| 0.0457 \| \| 2.5595 \| 75.0 \| 525 \| 3.0258 \| 0.825 \| 0.2837 \| 1.2210 \| 0.825 \| 0.8171 \| 0.1431 \| 0.0460 \| \| 2.5595 \| 76.0 \| 532 \| 3.0694 \| 0.825 \| 0.2886 \| 1.2091 \| 0.825 \| 0.8171 \| 0.1533 \| 0.0476 \| \| 2.5595 \| 77.0 \| 539 \| 3.0924 \| 0.825 \| 0.2939 \| 1.2130 \| 0.825 \| 0.8171 \| 0.1515 \| 0.0473 \| \| 2.5595 \| 78.0 \| 546 \| 3.0956 \| 0.82 \| 0.2921 \| 1.2081 \| 0.82 \| 0.8140 \| 0.1539 \| 0.0482 \| \| 2.5595 \| 79.0 \| 553 \| 3.0859 \| 0.825 \| 0.2884 \| 1.2109 \| 0.825 \| 0.8220 \| 0.1480 \| 0.0468 \| \| 2.5595 \| 80.0 \| 560 \| 3.0740 \| 0.825 \| 0.2894 \| 1.2081 \| 0.825 \| 0.8136 \| 0.1399 \| 0.0459 \| \| 2.5595 \| 81.0 \| 567 \| 3.0776 \| 0.825 \| 0.2901 \| 1.2066 \| 0.825 \| 0.8171 \| 0.1502 \| 0.0462 \| \| 2.5595 \| 82.0 \| 574 \| 3.0736 \| 0.83 \| 0.2869 \| 1.2100 \| 0.83 \| 0.8251 \| 0.1405 \| 0.0462 \| \| 2.5595 \| 83.0 \| 581 \| 3.0943 \| 0.825 \| 0.2919 \| 1.2065 \| 0.825 \| 0.8171 \| 0.1503 \| 0.0464 \| \| 2.5595 \| 84.0 \| 588 \| 3.0857 \| 0.825 \| 0.2908 \| 1.2080 \| 0.825 \| 0.8171 \| 0.1456 \| 0.0461 \| \| 2.5595 \| 85.0 \| 595 \| 3.0874 \| 0.825 \| 0.2890 \| 1.2063 \| 0.825 \| 0.8171 \| 0.1457 \| 0.0461 \| \| 2.5595 \| 86.0 \| 602 \| 3.0863 \| 0.825 \| 0.2880 \| 1.2069 \| 0.825 \| 0.8171 \| 0.1453 \| 0.0459 \| \| 2.5595 \| 87.0 \| 609 \| 3.0844 \| 0.825 \| 0.2882 \| 1.2059 \| 0.825 \| 0.8171 \| 0.1457 \| 0.0456 \| \| 2.5595 \| 88.0 \| 616 \| 3.1011 \| 0.825 \| 0.2909 \| 1.2034 \| 0.825 \| 0.8171 \| 0.1557 \| 0.0462 \| \| 2.5595 \| 89.0 \| 623 \| 3.1033 \| 0.825 \| 0.2912 \| 1.2033 \| 0.825 \| 0.8171 \| 0.1528 \| 0.0463 \| \| 2.5595 \| 90.0 \| 630 \| 3.1004 \| 0.825 \| 0.2903 \| 1.2029 \| 0.825 \| 0.8171 \| 0.1541 \| 0.0461 \| \| 2.5595 \| 91.0 \| 637 \| 3.0998 \| 0.825 \| 0.2900 \| 1.2033 \| 0.825 \| 0.8171 \| 0.1499 \| 0.0459 \| \| 2.5595 \| 92.0 \| 644 \| 3.1039 \| 0.825 \| 0.2904 \| 1.2023 \| 0.825 \| 0.8171 \| 0.1535 \| 0.0460 \| \| 2.5595 \| 93.0 \| 651 \| 3.1058 \| 0.825 \| 0.2906 \| 1.2020 \| 0.825 \| 0.8171 \| 0.1498 \| 0.0460 \| \| 2.5595 \| 94.0 \| 658 \| 3.1057 \| 0.825 \| 0.2906 \| 1.2022 \| 0.825 \| 0.8171 \| 0.1504 \| 0.0459 \| \| 2.5595 \| 95.0 \| 665 \| 3.1066 \| 0.825 \| 0.2908 \| 1.2018 \| 0.825 \| 0.8171 \| 0.1509 \| 0.0460 \| \| 2.5595 \| 96.0 \| 672 \| 3.1069 \| 0.825 \| 0.2906 \| 1.2018 \| 0.825 \| 0.8171 \| 0.1506 \| 0.0459 \| \| 2.5595 \| 97.0 \| 679 \| 3.1079 \| 0.825 \| 0.2906 \| 1.2013 \| 0.825 \| 0.8171 \| 0.1497 \| 0.0459 \| \| 2.5595 \| 98.0 \| 686 \| 3.1085 \| 0.825 \| 0.2907 \| 1.2013 \| 0.825 \| 0.8171 \| 0.1500 \| 0.0459 \| \| 2.5595 \| 99.0 \| 693 \| 3.1083 \| 0.825 \| 0.2907 \| 1.2013 \| 0.825 \| 0.8171 \| 0.1499 \| 0.0460 \| \| 2.5595 \| 100.0 \| 700 \| 3.1084 \| 0.825 \| 0.2907 \| 1.2013 \| 0.825 \| 0.8171 \| 0.1500 \| 0.0459 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_base_adamax_0001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_0001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4419 - Accuracy: 0.8372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3275 \| 1.0 \| 28 \| 1.2372 \| 0.5814 \| \| 1.0641 \| 2.0 \| 56 \| 1.0484 \| 0.6977 \| \| 0.7591 \| 3.0 \| 84 \| 0.8760 \| 0.7442 \| \| 0.5652 \| 4.0 \| 112 \| 0.7360 \| 0.8140 \| \| 0.3906 \| 5.0 \| 140 \| 0.6489 \| 0.8372 \| \| 0.3059 \| 6.0 \| 168 \| 0.5954 \| 0.8605 \| \| 0.1994 \| 7.0 \| 196 \| 0.5269 \| 0.8372 \| \| 0.134 \| 8.0 \| 224 \| 0.5174 \| 0.8605 \| \| 0.0783 \| 9.0 \| 252 \| 0.4602 \| 0.8605 \| \| 0.0454 \| 10.0 \| 280 \| 0.4569 \| 0.8372 \| \| 0.0318 \| 11.0 \| 308 \| 0.4393 \| 0.8837 \| \| 0.018 \| 12.0 \| 336 \| 0.4222 \| 0.8605 \| \| 0.0132 \| 13.0 \| 364 \| 0.4453 \| 0.8837 \| \| 0.0088 \| 14.0 \| 392 \| 0.4098 \| 0.8837 \| \| 0.0068 \| 15.0 \| 420 \| 0.4226 \| 0.8605 \| \| 0.0058 \| 16.0 \| 448 \| 0.4268 \| 0.8605 \| \| 0.0055 \| 17.0 \| 476 \| 0.4132 \| 0.8605 \| \| 0.0045 \| 18.0 \| 504 \| 0.4342 \| 0.8605 \| \| 0.004 \| 19.0 \| 532 \| 0.4228 \| 0.8605 \| \| 0.0033 \| 20.0 \| 560 \| 0.4271 \| 0.8372 \| \| 0.0033 \| 21.0 \| 588 \| 0.4254 \| 0.8372 \| \| 0.0029 \| 22.0 \| 616 \| 0.4205 \| 0.8372 \| \| 0.0027 \| 23.0 \| 644 \| 0.4207 \| 0.8372 \| \| 0.0024 \| 24.0 \| 672 \| 0.4248 \| 0.8605 \| \| 0.0022 \| 25.0 \| 700 \| 0.4229 \| 0.8372 \| \| 0.0021 \| 26.0 \| 728 \| 0.4293 \| 0.8372 \| \| 0.002 \| 27.0 \| 756 \| 0.4267 \| 0.8372 \| \| 0.002 \| 28.0 \| 784 \| 0.4239 \| 0.8605 \| \| 0.0018 \| 29.0 \| 812 \| 0.4273 \| 0.8372 \| \| 0.0018 \| 30.0 \| 840 \| 0.4313 \| 0.8372 \| \| 0.0016 \| 31.0 \| 868 \| 0.4289 \| 0.8372 \| \| 0.0016 \| 32.0 \| 896 \| 0.4329 \| 0.8372 \| \| 0.0016 \| 33.0 \| 924 \| 0.4313 \| 0.8372 \| \| 0.0014 \| 34.0 \| 952 \| 0.4362 \| 0.8372 \| \| 0.0016 \| 35.0 \| 980 \| 0.4336 \| 0.8372 \| \| 0.0014 \| 36.0 \| 1008 \| 0.4353 \| 0.8372 \| \| 0.0014 \| 37.0 \| 1036 \| 0.4446 \| 0.8372 \| \| 0.0013 \| 38.0 \| 1064 \| 0.4482 \| 0.8372 \| \| 0.0013 \| 39.0 \| 1092 \| 0.4496 \| 0.8372 \| \| 0.0012 \| 40.0 \| 1120 \| 0.4442 \| 0.8372 \| \| 0.0013 \| 41.0 \| 1148 \| 0.4456 \| 0.8372 \| \| 0.0013 \| 42.0 \| 1176 \| 0.4450 \| 0.8372 \| \| 0.0012 \| 43.0 \| 1204 \| 0.4433 \| 0.8372 \| \| 0.0012 \| 44.0 \| 1232 \| 0.4424 \| 0.8372 \| \| 0.0011 \| 45.0 \| 1260 \| 0.4418 \| 0.8372 \| \| 0.0011 \| 46.0 \| 1288 \| 0.4417 \| 0.8372 \| \| 0.0011 \| 47.0 \| 1316 \| 0.4421 \| 0.8372 \| \| 0.0011 \| 48.0 \| 1344 \| 0.4419 \| 0.8372 \| \| 0.0011 \| 49.0 \| 1372 \| 0.4419 \| 0.8372 \| \| 0.0011 \| 50.0 \| 1400 \| 0.4419 \| 0.8372 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_adamax_0001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_0001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7295 \| 1.0 \| 28 \| 0.4220 \| 0.8095 \| \| 0.0888 \| 2.0 \| 56 \| 0.1615 \| 0.9286 \| \| 0.0158 \| 3.0 \| 84 \| 0.0867 \| 0.9524 \| \| 0.0047 \| 4.0 \| 112 \| 0.1052 \| 0.9524 \| \| 0.001 \| 5.0 \| 140 \| 0.1157 \| 0.9524 \| \| 0.0007 \| 6.0 \| 168 \| 0.0506 \| 0.9762 \| \| 0.0004 \| 7.0 \| 196 \| 0.0523 \| 0.9762 \| \| 0.0003 \| 8.0 \| 224 \| 0.0521 \| 1.0 \| \| 0.0003 \| 9.0 \| 252 \| 0.0486 \| 1.0 \| \| 0.0002 \| 10.0 \| 280 \| 0.0478 \| 1.0 \| \| 0.0002 \| 11.0 \| 308 \| 0.0461 \| 1.0 \| \| 0.0002 \| 12.0 \| 336 \| 0.0436 \| 1.0 \| \| 0.0002 \| 13.0 \| 364 \| 0.0422 \| 1.0 \| \| 0.0002 \| 14.0 \| 392 \| 0.0427 \| 1.0 \| \| 0.0002 \| 15.0 \| 420 \| 0.0410 \| 1.0 \| \| 0.0001 \| 16.0 \| 448 \| 0.0408 \| 1.0 \| \| 0.0001 \| 17.0 \| 476 \| 0.0398 \| 1.0 \| \| 0.0001 \| 18.0 \| 504 \| 0.0389 \| 1.0 \| \| 0.0001 \| 19.0 \| 532 \| 0.0389 \| 1.0 \| \| 0.0001 \| 20.0 \| 560 \| 0.0386 \| 1.0 \| \| 0.0001 \| 21.0 \| 588 \| 0.0377 \| 1.0 \| \| 0.0001 \| 22.0 \| 616 \| 0.0375 \| 1.0 \| \| 0.0001 \| 23.0 \| 644 \| 0.0368 \| 1.0 \| \| 0.0001 \| 24.0 \| 672 \| 0.0368 \| 1.0 \| \| 0.0001 \| 25.0 \| 700 \| 0.0364 \| 1.0 \| \| 0.0001 \| 26.0 \| 728 \| 0.0360 \| 1.0 \| \| 0.0001 \| 27.0 \| 756 \| 0.0354 \| 1.0 \| \| 0.0001 \| 28.0 \| 784 \| 0.0352 \| 1.0 \| \| 0.0001 \| 29.0 \| 812 \| 0.0345 \| 1.0 \| \| 0.0001 \| 30.0 \| 840 \| 0.0346 \| 1.0 \| \| 0.0001 \| 31.0 \| 868 \| 0.0344 \| 1.0 \| \| 0.0001 \| 32.0 \| 896 \| 0.0342 \| 1.0 \| \| 0.0001 \| 33.0 \| 924 \| 0.0343 \| 1.0 \| \| 0.0001 \| 34.0 \| 952 \| 0.0340 \| 1.0 \| \| 0.0001 \| 35.0 \| 980 \| 0.0336 \| 1.0 \| \| 0.0001 \| 36.0 \| 1008 \| 0.0333 \| 1.0 \| \| 0.0001 \| 37.0 \| 1036 \| 0.0331 \| 1.0 \| \| 0.0001 \| 38.0 \| 1064 \| 0.0333 \| 1.0 \| \| 0.0001 \| 39.0 \| 1092 \| 0.0331 \| 1.0 \| \| 0.0001 \| 40.0 \| 1120 \| 0.0332 \| 1.0 \| \| 0.0001 \| 41.0 \| 1148 \| 0.0332 \| 1.0 \| \| 0.0001 \| 42.0 \| 1176 \| 0.0331 \| 1.0 \| \| 0.0001 \| 43.0 \| 1204 \| 0.0330 \| 1.0 \| \| 0.0001 \| 44.0 \| 1232 \| 0.0331 \| 1.0 \| \| 0.0001 \| 45.0 \| 1260 \| 0.0330 \| 1.0 \| \| 0.0001 \| 46.0 \| 1288 \| 0.0331 \| 1.0 \| \| 0.0001 \| 47.0 \| 1316 \| 0.0332 \| 1.0 \| \| 0.0001 \| 48.0 \| 1344 \| 0.0332 \| 1.0 \| \| 0.0001 \| 49.0 \| 1372 \| 0.0332 \| 1.0 \| \| 0.0001 \| 50.0 \| 1400 \| 0.0332 \| 1.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/dit-base_tobacco-small_tobacco3482_hint	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_hint This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9099 - Accuracy: 0.85 - Brier Loss: 0.2772 - Nll: 1.4757 - F1 Micro: 0.85 - F1 Macro: 0.8366 - Ece: 0.1392 - Aurc: 0.0460 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 3.2233 \| 0.44 \| 0.7001 \| 2.8339 \| 0.44 \| 0.3067 \| 0.2724 \| 0.3661 \| \| No log \| 2.0 \| 100 \| 2.3954 \| 0.705 \| 0.4016 \| 1.5814 \| 0.705 \| 0.6657 \| 0.2046 \| 0.1093 \| \| No log \| 3.0 \| 150 \| 2.1938 \| 0.735 \| 0.3560 \| 1.5685 \| 0.735 \| 0.7026 \| 0.1879 \| 0.0858 \| \| No log \| 4.0 \| 200 \| 2.0989 \| 0.74 \| 0.3533 \| 1.5416 \| 0.74 \| 0.7058 \| 0.2015 \| 0.0896 \| \| No log \| 5.0 \| 250 \| 2.0203 \| 0.795 \| 0.3169 \| 1.5407 \| 0.795 \| 0.7861 \| 0.1773 \| 0.0919 \| \| No log \| 6.0 \| 300 \| 2.1849 \| 0.675 \| 0.4531 \| 1.6333 \| 0.675 \| 0.6701 \| 0.2207 \| 0.1166 \| \| No log \| 7.0 \| 350 \| 2.2223 \| 0.745 \| 0.4113 \| 1.4333 \| 0.745 \| 0.7293 \| 0.2045 \| 0.0980 \| \| No log \| 8.0 \| 400 \| 2.1696 \| 0.715 \| 0.4221 \| 1.6537 \| 0.715 \| 0.6723 \| 0.2069 \| 0.1040 \| \| No log \| 9.0 \| 450 \| 2.4443 \| 0.735 \| 0.4291 \| 1.5392 \| 0.735 \| 0.7458 \| 0.2236 \| 0.1323 \| \| 1.8536 \| 10.0 \| 500 \| 2.0474 \| 0.775 \| 0.3649 \| 1.6156 \| 0.775 \| 0.7528 \| 0.1915 \| 0.0844 \| \| 1.8536 \| 11.0 \| 550 \| 2.0046 \| 0.81 \| 0.3170 \| 1.6225 \| 0.81 \| 0.7920 \| 0.1547 \| 0.0639 \| \| 1.8536 \| 12.0 \| 600 \| 2.4864 \| 0.725 \| 0.4602 \| 1.5678 \| 0.7250 \| 0.7308 \| 0.2415 \| 0.1218 \| \| 1.8536 \| 13.0 \| 650 \| 1.8413 \| 0.83 \| 0.2698 \| 1.6361 \| 0.83 \| 0.8117 \| 0.1349 \| 0.0674 \| \| 1.8536 \| 14.0 \| 700 \| 2.1304 \| 0.815 \| 0.3281 \| 1.5685 \| 0.815 \| 0.7936 \| 0.1715 \| 0.0703 \| \| 1.8536 \| 15.0 \| 750 \| 2.5075 \| 0.71 \| 0.4652 \| 1.9297 \| 0.7100 \| 0.6877 \| 0.2281 \| 0.1099 \| \| 1.8536 \| 16.0 \| 800 \| 2.4854 \| 0.73 \| 0.4462 \| 1.5241 \| 0.7300 \| 0.7176 \| 0.2282 \| 0.1097 \| \| 1.8536 \| 17.0 \| 850 \| 2.1252 \| 0.805 \| 0.3210 \| 1.5685 \| 0.805 \| 0.7907 \| 0.1650 \| 0.0804 \| \| 1.8536 \| 18.0 \| 900 \| 1.9249 \| 0.86 \| 0.2473 \| 1.7031 \| 0.8600 \| 0.8689 \| 0.1244 \| 0.0528 \| \| 1.8536 \| 19.0 \| 950 \| 2.0943 \| 0.835 \| 0.2840 \| 1.4696 \| 0.835 \| 0.8267 \| 0.1439 \| 0.0652 \| \| 1.0941 \| 20.0 \| 1000 \| 1.8548 \| 0.845 \| 0.2566 \| 1.3059 \| 0.845 \| 0.8403 \| 0.1333 \| 0.0558 \| \| 1.0941 \| 21.0 \| 1050 \| 2.1487 \| 0.805 \| 0.3362 \| 1.4556 \| 0.805 \| 0.8051 \| 0.1665 \| 0.0764 \| \| 1.0941 \| 22.0 \| 1100 \| 2.2147 \| 0.81 \| 0.3149 \| 1.4884 \| 0.81 \| 0.8081 \| 0.1710 \| 0.0984 \| \| 1.0941 \| 23.0 \| 1150 \| 2.1111 \| 0.84 \| 0.2898 \| 1.5426 \| 0.8400 \| 0.8410 \| 0.1489 \| 0.0848 \| \| 1.0941 \| 24.0 \| 1200 \| 2.2432 \| 0.85 \| 0.2884 \| 1.7273 \| 0.85 \| 0.8482 \| 0.1532 \| 0.0765 \| \| 1.0941 \| 25.0 \| 1250 \| 2.3105 \| 0.75 \| 0.4190 \| 1.4648 \| 0.75 \| 0.7396 \| 0.2177 \| 0.1074 \| \| 1.0941 \| 26.0 \| 1300 \| 2.0587 \| 0.795 \| 0.3444 \| 1.6181 \| 0.795 \| 0.7960 \| 0.1641 \| 0.0799 \| \| 1.0941 \| 27.0 \| 1350 \| 2.4465 \| 0.8 \| 0.3517 \| 2.0076 \| 0.8000 \| 0.7770 \| 0.1731 \| 0.0849 \| \| 1.0941 \| 28.0 \| 1400 \| 2.1351 \| 0.825 \| 0.3132 \| 1.5650 \| 0.825 \| 0.8315 \| 0.1631 \| 0.0553 \| \| 1.0941 \| 29.0 \| 1450 \| 1.9746 \| 0.86 \| 0.2451 \| 1.5908 \| 0.8600 \| 0.8374 \| 0.1267 \| 0.0537 \| \| 0.9575 \| 30.0 \| 1500 \| 2.0257 \| 0.855 \| 0.2737 \| 1.6541 \| 0.855 \| 0.8121 \| 0.1352 \| 0.0480 \| \| 0.9575 \| 31.0 \| 1550 \| 1.9631 \| 0.84 \| 0.3037 \| 1.7341 \| 0.8400 \| 0.8201 \| 0.1515 \| 0.0423 \| \| 0.9575 \| 32.0 \| 1600 \| 2.4215 \| 0.785 \| 0.3909 \| 1.4042 \| 0.785 \| 0.7740 \| 0.2018 \| 0.0708 \| \| 0.9575 \| 33.0 \| 1650 \| 2.2159 \| 0.795 \| 0.3492 \| 1.7639 \| 0.795 \| 0.7716 \| 0.1721 \| 0.0537 \| \| 0.9575 \| 34.0 \| 1700 \| 2.3363 \| 0.82 \| 0.3132 \| 1.9858 \| 0.82 \| 0.7993 \| 0.1610 \| 0.0845 \| \| 0.9575 \| 35.0 \| 1750 \| 2.2187 \| 0.84 \| 0.2884 \| 1.5376 \| 0.8400 \| 0.8182 \| 0.1523 \| 0.0803 \| \| 0.9575 \| 36.0 \| 1800 \| 2.3407 \| 0.825 \| 0.3206 \| 1.8292 \| 0.825 \| 0.8028 \| 0.1588 \| 0.0719 \| \| 0.9575 \| 37.0 \| 1850 \| 2.4302 \| 0.815 \| 0.3353 \| 1.7611 \| 0.815 \| 0.8091 \| 0.1654 \| 0.0920 \| \| 0.9575 \| 38.0 \| 1900 \| 2.3307 \| 0.815 \| 0.3269 \| 1.8263 \| 0.815 \| 0.8043 \| 0.1675 \| 0.0876 \| \| 0.9575 \| 39.0 \| 1950 \| 2.2905 \| 0.825 \| 0.3217 \| 1.7612 \| 0.825 \| 0.8116 \| 0.1639 \| 0.0841 \| \| 0.8923 \| 40.0 \| 2000 \| 2.2699 \| 0.83 \| 0.3225 \| 1.7537 \| 0.83 \| 0.8186 \| 0.1655 \| 0.0792 \| \| 0.8923 \| 41.0 \| 2050 \| 2.2327 \| 0.83 \| 0.3179 \| 1.7534 \| 0.83 \| 0.8186 \| 0.1559 \| 0.0764 \| \| 0.8923 \| 42.0 \| 2100 \| 2.2852 \| 0.825 \| 0.3230 \| 1.6737 \| 0.825 \| 0.8150 \| 0.1611 \| 0.0760 \| \| 0.8923 \| 43.0 \| 2150 \| 2.2597 \| 0.825 \| 0.3221 \| 1.6727 \| 0.825 \| 0.8147 \| 0.1610 \| 0.0734 \| \| 0.8923 \| 44.0 \| 2200 \| 2.2492 \| 0.83 \| 0.3176 \| 1.6692 \| 0.83 \| 0.8169 \| 0.1619 \| 0.0720 \| \| 0.8923 \| 45.0 \| 2250 \| 2.2208 \| 0.825 \| 0.3182 \| 1.6737 \| 0.825 \| 0.8124 \| 0.1627 \| 0.0707 \| \| 0.8923 \| 46.0 \| 2300 \| 2.2192 \| 0.825 \| 0.3209 \| 1.6771 \| 0.825 \| 0.8121 \| 0.1650 \| 0.0712 \| \| 0.8923 \| 47.0 \| 2350 \| 2.2127 \| 0.825 \| 0.3198 \| 1.6187 \| 0.825 \| 0.8124 \| 0.1636 \| 0.0684 \| \| 0.8923 \| 48.0 \| 2400 \| 2.2079 \| 0.825 \| 0.3208 \| 1.6760 \| 0.825 \| 0.8121 \| 0.1632 \| 0.0707 \| \| 0.8923 \| 49.0 \| 2450 \| 2.1995 \| 0.825 \| 0.3187 \| 1.5377 \| 0.825 \| 0.8124 \| 0.1656 \| 0.0702 \| \| 0.8511 \| 50.0 \| 2500 \| 2.1877 \| 0.825 \| 0.3158 \| 1.6098 \| 0.825 \| 0.8124 \| 0.1600 \| 0.0690 \| \| 0.8511 \| 51.0 \| 2550 \| 2.1698 \| 0.825 \| 0.3167 \| 1.5353 \| 0.825 \| 0.8124 \| 0.1607 \| 0.0695 \| \| 0.8511 \| 52.0 \| 2600 \| 2.1667 \| 0.825 \| 0.3133 \| 1.5303 \| 0.825 \| 0.8121 \| 0.1596 \| 0.0680 \| \| 0.8511 \| 53.0 \| 2650 \| 2.1791 \| 0.83 \| 0.3170 \| 1.5332 \| 0.83 \| 0.8149 \| 0.1608 \| 0.0690 \| \| 0.8511 \| 54.0 \| 2700 \| 2.1621 \| 0.83 \| 0.3148 \| 1.5274 \| 0.83 \| 0.8146 \| 0.1551 \| 0.0693 \| \| 0.8511 \| 55.0 \| 2750 \| 2.1572 \| 0.83 \| 0.3119 \| 1.5318 \| 0.83 \| 0.8149 \| 0.1532 \| 0.0680 \| \| 0.8511 \| 56.0 \| 2800 \| 2.1587 \| 0.83 \| 0.3100 \| 1.5232 \| 0.83 \| 0.8148 \| 0.1524 \| 0.0712 \| \| 0.8511 \| 57.0 \| 2850 \| 2.1596 \| 0.83 \| 0.3101 \| 1.5234 \| 0.83 \| 0.8146 \| 0.1560 \| 0.0696 \| \| 0.8511 \| 58.0 \| 2900 \| 2.1048 \| 0.835 \| 0.3047 \| 1.5231 \| 0.835 \| 0.8189 \| 0.1442 \| 0.0676 \| \| 0.8511 \| 59.0 \| 2950 \| 2.4279 \| 0.76 \| 0.4096 \| 1.4535 \| 0.76 \| 0.7538 \| 0.2078 \| 0.0731 \| \| 0.8335 \| 60.0 \| 3000 \| 2.2098 \| 0.775 \| 0.4036 \| 1.4180 \| 0.775 \| 0.7565 \| 0.2010 \| 0.0870 \| \| 0.8335 \| 61.0 \| 3050 \| 2.0122 \| 0.85 \| 0.2596 \| 1.5903 \| 0.85 \| 0.8272 \| 0.1349 \| 0.0779 \| \| 0.8335 \| 62.0 \| 3100 \| 2.2465 \| 0.815 \| 0.3311 \| 1.6852 \| 0.815 \| 0.7899 \| 0.1672 \| 0.0658 \| \| 0.8335 \| 63.0 \| 3150 \| 2.1239 \| 0.84 \| 0.2963 \| 1.6390 \| 0.8400 \| 0.8305 \| 0.1458 \| 0.0878 \| \| 0.8335 \| 64.0 \| 3200 \| 2.1931 \| 0.82 \| 0.3181 \| 1.7037 \| 0.82 \| 0.8199 \| 0.1654 \| 0.0719 \| \| 0.8335 \| 65.0 \| 3250 \| 1.8262 \| 0.855 \| 0.2493 \| 1.4845 \| 0.855 \| 0.8335 \| 0.1297 \| 0.0456 \| \| 0.8335 \| 66.0 \| 3300 \| 1.9467 \| 0.845 \| 0.2657 \| 1.4217 \| 0.845 \| 0.8326 \| 0.1361 \| 0.0498 \| \| 0.8335 \| 67.0 \| 3350 \| 1.9371 \| 0.85 \| 0.2680 \| 1.4175 \| 0.85 \| 0.8405 \| 0.1293 \| 0.0506 \| \| 0.8335 \| 68.0 \| 3400 \| 1.9172 \| 0.85 \| 0.2656 \| 1.4203 \| 0.85 \| 0.8405 \| 0.1331 \| 0.0503 \| \| 0.8335 \| 69.0 \| 3450 \| 1.8872 \| 0.845 \| 0.2664 \| 1.4327 \| 0.845 \| 0.8324 \| 0.1360 \| 0.0493 \| \| 0.8281 \| 70.0 \| 3500 \| 1.9045 \| 0.845 \| 0.2715 \| 1.4920 \| 0.845 \| 0.8324 \| 0.1377 \| 0.0496 \| \| 0.8281 \| 71.0 \| 3550 \| 1.8954 \| 0.845 \| 0.2684 \| 1.4919 \| 0.845 \| 0.8338 \| 0.1385 \| 0.0499 \| \| 0.8281 \| 72.0 \| 3600 \| 1.9222 \| 0.85 \| 0.2698 \| 1.4870 \| 0.85 \| 0.8375 \| 0.1356 \| 0.0499 \| \| 0.8281 \| 73.0 \| 3650 \| 1.9004 \| 0.845 \| 0.2691 \| 1.4912 \| 0.845 \| 0.8335 \| 0.1377 \| 0.0484 \| \| 0.8281 \| 74.0 \| 3700 \| 1.9168 \| 0.85 \| 0.2693 \| 1.4903 \| 0.85 \| 0.8375 \| 0.1338 \| 0.0495 \| \| 0.8281 \| 75.0 \| 3750 \| 1.8970 \| 0.85 \| 0.2700 \| 1.4908 \| 0.85 \| 0.8366 \| 0.1416 \| 0.0477 \| \| 0.8281 \| 76.0 \| 3800 \| 1.9089 \| 0.85 \| 0.2705 \| 1.4867 \| 0.85 \| 0.8366 \| 0.1373 \| 0.0480 \| \| 0.8281 \| 77.0 \| 3850 \| 1.8902 \| 0.85 \| 0.2697 \| 1.4896 \| 0.85 \| 0.8366 \| 0.1407 \| 0.0464 \| \| 0.8281 \| 78.0 \| 3900 \| 1.8889 \| 0.85 \| 0.2710 \| 1.4882 \| 0.85 \| 0.8366 \| 0.1421 \| 0.0472 \| \| 0.8281 \| 79.0 \| 3950 \| 1.9080 \| 0.85 \| 0.2712 \| 1.4876 \| 0.85 \| 0.8366 \| 0.1345 \| 0.0476 \| \| 0.8047 \| 80.0 \| 4000 \| 1.9011 \| 0.85 \| 0.2703 \| 1.4864 \| 0.85 \| 0.8366 \| 0.1373 \| 0.0472 \| \| 0.8047 \| 81.0 \| 4050 \| 1.9112 \| 0.85 \| 0.2735 \| 1.4867 \| 0.85 \| 0.8366 \| 0.1379 \| 0.0465 \| \| 0.8047 \| 82.0 \| 4100 \| 1.8850 \| 0.85 \| 0.2728 \| 1.4872 \| 0.85 \| 0.8366 \| 0.1419 \| 0.0462 \| \| 0.8047 \| 83.0 \| 4150 \| 1.9074 \| 0.85 \| 0.2740 \| 1.4862 \| 0.85 \| 0.8366 \| 0.1369 \| 0.0463 \| \| 0.8047 \| 84.0 \| 4200 \| 1.8804 \| 0.85 \| 0.2714 \| 1.4818 \| 0.85 \| 0.8366 \| 0.1376 \| 0.0461 \| \| 0.8047 \| 85.0 \| 4250 \| 1.9092 \| 0.85 \| 0.2757 \| 1.4825 \| 0.85 \| 0.8366 \| 0.1437 \| 0.0463 \| \| 0.8047 \| 86.0 \| 4300 \| 1.8985 \| 0.85 \| 0.2745 \| 1.4827 \| 0.85 \| 0.8366 \| 0.1390 \| 0.0460 \| \| 0.8047 \| 87.0 \| 4350 \| 1.9091 \| 0.85 \| 0.2731 \| 1.4808 \| 0.85 \| 0.8366 \| 0.1403 \| 0.0466 \| \| 0.8047 \| 88.0 \| 4400 \| 1.9037 \| 0.85 \| 0.2754 \| 1.4836 \| 0.85 \| 0.8366 \| 0.1383 \| 0.0459 \| \| 0.8047 \| 89.0 \| 4450 \| 1.8950 \| 0.85 \| 0.2750 \| 1.4798 \| 0.85 \| 0.8366 \| 0.1386 \| 0.0452 \| \| 0.7971 \| 90.0 \| 4500 \| 1.9115 \| 0.85 \| 0.2755 \| 1.4785 \| 0.85 \| 0.8366 \| 0.1387 \| 0.0461 \| \| 0.7971 \| 91.0 \| 4550 \| 1.9061 \| 0.85 \| 0.2757 \| 1.4791 \| 0.85 \| 0.8366 \| 0.1451 \| 0.0460 \| \| 0.7971 \| 92.0 \| 4600 \| 1.9058 \| 0.85 \| 0.2757 \| 1.4785 \| 0.85 \| 0.8366 \| 0.1392 \| 0.0464 \| \| 0.7971 \| 93.0 \| 4650 \| 1.9128 \| 0.85 \| 0.2724 \| 1.4769 \| 0.85 \| 0.8366 \| 0.1341 \| 0.0468 \| \| 0.7971 \| 94.0 \| 4700 \| 1.9115 \| 0.85 \| 0.2770 \| 1.4771 \| 0.85 \| 0.8366 \| 0.1388 \| 0.0463 \| \| 0.7971 \| 95.0 \| 4750 \| 1.9097 \| 0.85 \| 0.2761 \| 1.4761 \| 0.85 \| 0.8366 \| 0.1382 \| 0.0462 \| \| 0.7971 \| 96.0 \| 4800 \| 1.9025 \| 0.85 \| 0.2761 \| 1.4759 \| 0.85 \| 0.8366 \| 0.1385 \| 0.0460 \| \| 0.7971 \| 97.0 \| 4850 \| 1.9153 \| 0.85 \| 0.2775 \| 1.4757 \| 0.85 \| 0.8366 \| 0.1394 \| 0.0463 \| \| 0.7971 \| 98.0 \| 4900 \| 1.9084 \| 0.85 \| 0.2765 \| 1.4755 \| 0.85 \| 0.8366 \| 0.1388 \| 0.0460 \| \| 0.7971 \| 99.0 \| 4950 \| 1.9087 \| 0.85 \| 0.2772 \| 1.4757 \| 0.85 \| 0.8366 \| 0.1392 \| 0.0460 \| \| 0.7931 \| 100.0 \| 5000 \| 1.9099 \| 0.85 \| 0.2772 \| 1.4757 \| 0.85 \| 0.8366 \| 0.1392 \| 0.0460 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-small_tobacco3482_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_simkd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6962 - Accuracy: 0.85 - Brier Loss: 0.2700 - Nll: 0.9667 - F1 Micro: 0.85 - F1 Macro: 0.8241 - Ece: 0.2479 - Aurc: 0.0379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 1.0013 \| 0.18 \| 0.8965 \| 4.5407 \| 0.18 \| 0.1379 \| 0.2160 \| 0.6680 \| \| No log \| 2.0 \| 100 \| 0.9916 \| 0.3 \| 0.8871 \| 3.1090 \| 0.3 \| 0.1526 \| 0.3057 \| 0.4735 \| \| No log \| 3.0 \| 150 \| 0.9644 \| 0.51 \| 0.8433 \| 2.4502 \| 0.51 \| 0.3257 \| 0.4499 \| 0.2544 \| \| No log \| 4.0 \| 200 \| 0.9207 \| 0.575 \| 0.7585 \| 2.1964 \| 0.575 \| 0.3958 \| 0.4563 \| 0.2193 \| \| No log \| 5.0 \| 250 \| 0.8726 \| 0.635 \| 0.6620 \| 2.3923 \| 0.635 \| 0.5105 \| 0.4321 \| 0.1730 \| \| No log \| 6.0 \| 300 \| 0.8303 \| 0.665 \| 0.5604 \| 1.4922 \| 0.665 \| 0.5869 \| 0.3717 \| 0.1305 \| \| No log \| 7.0 \| 350 \| 0.7994 \| 0.745 \| 0.4490 \| 1.3772 \| 0.745 \| 0.6541 \| 0.3557 \| 0.0853 \| \| No log \| 8.0 \| 400 \| 0.7822 \| 0.79 \| 0.4124 \| 1.2076 \| 0.79 \| 0.7109 \| 0.3035 \| 0.0873 \| \| No log \| 9.0 \| 450 \| 0.7808 \| 0.78 \| 0.3955 \| 1.5529 \| 0.78 \| 0.7041 \| 0.3123 \| 0.0763 \| \| 0.8704 \| 10.0 \| 500 \| 0.7923 \| 0.695 \| 0.4296 \| 1.7171 \| 0.695 \| 0.6150 \| 0.3012 \| 0.1039 \| \| 0.8704 \| 11.0 \| 550 \| 0.7848 \| 0.745 \| 0.4327 \| 1.6327 \| 0.745 \| 0.6972 \| 0.2800 \| 0.1321 \| \| 0.8704 \| 12.0 \| 600 \| 0.7600 \| 0.825 \| 0.3579 \| 1.2569 \| 0.825 \| 0.7621 \| 0.3015 \| 0.0624 \| \| 0.8704 \| 13.0 \| 650 \| 0.7570 \| 0.79 \| 0.3554 \| 1.4638 \| 0.79 \| 0.7706 \| 0.2964 \| 0.0621 \| \| 0.8704 \| 14.0 \| 700 \| 0.7504 \| 0.81 \| 0.3434 \| 1.5597 \| 0.81 \| 0.7714 \| 0.2930 \| 0.0589 \| \| 0.8704 \| 15.0 \| 750 \| 0.7481 \| 0.8 \| 0.3439 \| 1.3827 \| 0.8000 \| 0.7641 \| 0.2805 \| 0.0675 \| \| 0.8704 \| 16.0 \| 800 \| 0.7358 \| 0.81 \| 0.3357 \| 1.4522 \| 0.81 \| 0.7889 \| 0.3077 \| 0.0610 \| \| 0.8704 \| 17.0 \| 850 \| 0.7294 \| 0.82 \| 0.3179 \| 1.0458 \| 0.82 \| 0.7820 \| 0.2909 \| 0.0564 \| \| 0.8704 \| 18.0 \| 900 \| 0.7229 \| 0.815 \| 0.3092 \| 1.2562 \| 0.815 \| 0.7862 \| 0.2719 \| 0.0496 \| \| 0.8704 \| 19.0 \| 950 \| 0.7186 \| 0.825 \| 0.3069 \| 1.0425 \| 0.825 \| 0.7977 \| 0.2824 \| 0.0558 \| \| 0.6968 \| 20.0 \| 1000 \| 0.7156 \| 0.83 \| 0.3031 \| 0.9897 \| 0.83 \| 0.8039 \| 0.2660 \| 0.0490 \| \| 0.6968 \| 21.0 \| 1050 \| 0.7135 \| 0.82 \| 0.3014 \| 1.0562 \| 0.82 \| 0.7887 \| 0.2745 \| 0.0462 \| \| 0.6968 \| 22.0 \| 1100 \| 0.7116 \| 0.835 \| 0.2997 \| 0.9822 \| 0.835 \| 0.8102 \| 0.2817 \| 0.0452 \| \| 0.6968 \| 23.0 \| 1150 \| 0.7114 \| 0.82 \| 0.3047 \| 0.9197 \| 0.82 \| 0.7937 \| 0.2669 \| 0.0484 \| \| 0.6968 \| 24.0 \| 1200 \| 0.7111 \| 0.8 \| 0.3032 \| 0.9744 \| 0.8000 \| 0.7690 \| 0.2624 \| 0.0504 \| \| 0.6968 \| 25.0 \| 1250 \| 0.7076 \| 0.805 \| 0.3025 \| 0.9884 \| 0.805 \| 0.7677 \| 0.2538 \| 0.0478 \| \| 0.6968 \| 26.0 \| 1300 \| 0.7074 \| 0.82 \| 0.3037 \| 0.9954 \| 0.82 \| 0.7877 \| 0.2592 \| 0.0496 \| \| 0.6968 \| 27.0 \| 1350 \| 0.7053 \| 0.825 \| 0.2998 \| 0.9712 \| 0.825 \| 0.7885 \| 0.2628 \| 0.0454 \| \| 0.6968 \| 28.0 \| 1400 \| 0.7046 \| 0.82 \| 0.2936 \| 0.9780 \| 0.82 \| 0.7886 \| 0.2573 \| 0.0438 \| \| 0.6968 \| 29.0 \| 1450 \| 0.7068 \| 0.82 \| 0.3000 \| 0.9943 \| 0.82 \| 0.7895 \| 0.2382 \| 0.0447 \| \| 0.6551 \| 30.0 \| 1500 \| 0.7045 \| 0.83 \| 0.2881 \| 0.9107 \| 0.83 \| 0.8010 \| 0.2363 \| 0.0439 \| \| 0.6551 \| 31.0 \| 1550 \| 0.7033 \| 0.825 \| 0.2936 \| 0.9794 \| 0.825 \| 0.7858 \| 0.2556 \| 0.0433 \| \| 0.6551 \| 32.0 \| 1600 \| 0.7014 \| 0.82 \| 0.2890 \| 0.9799 \| 0.82 \| 0.7895 \| 0.2495 \| 0.0418 \| \| 0.6551 \| 33.0 \| 1650 \| 0.7020 \| 0.815 \| 0.2921 \| 0.9658 \| 0.815 \| 0.7820 \| 0.2556 \| 0.0449 \| \| 0.6551 \| 34.0 \| 1700 \| 0.7012 \| 0.835 \| 0.2885 \| 1.0419 \| 0.835 \| 0.8042 \| 0.2581 \| 0.0417 \| \| 0.6551 \| 35.0 \| 1750 \| 0.7013 \| 0.835 \| 0.2902 \| 0.9773 \| 0.835 \| 0.8035 \| 0.2522 \| 0.0435 \| \| 0.6551 \| 36.0 \| 1800 \| 0.7016 \| 0.825 \| 0.2884 \| 0.9815 \| 0.825 \| 0.7851 \| 0.2518 \| 0.0432 \| \| 0.6551 \| 37.0 \| 1850 \| 0.7007 \| 0.835 \| 0.2888 \| 0.9724 \| 0.835 \| 0.8133 \| 0.2486 \| 0.0438 \| \| 0.6551 \| 38.0 \| 1900 \| 0.6984 \| 0.825 \| 0.2847 \| 0.9650 \| 0.825 \| 0.7897 \| 0.2487 \| 0.0415 \| \| 0.6551 \| 39.0 \| 1950 \| 0.7001 \| 0.84 \| 0.2843 \| 1.0535 \| 0.8400 \| 0.8104 \| 0.2566 \| 0.0418 \| \| 0.6381 \| 40.0 \| 2000 \| 0.6990 \| 0.825 \| 0.2843 \| 0.9673 \| 0.825 \| 0.7963 \| 0.2396 \| 0.0429 \| \| 0.6381 \| 41.0 \| 2050 \| 0.7002 \| 0.84 \| 0.2875 \| 1.0599 \| 0.8400 \| 0.8098 \| 0.2618 \| 0.0413 \| \| 0.6381 \| 42.0 \| 2100 \| 0.6967 \| 0.83 \| 0.2791 \| 0.9676 \| 0.83 \| 0.7929 \| 0.2441 \| 0.0403 \| \| 0.6381 \| 43.0 \| 2150 \| 0.6978 \| 0.835 \| 0.2802 \| 0.9771 \| 0.835 \| 0.8071 \| 0.2526 \| 0.0416 \| \| 0.6381 \| 44.0 \| 2200 \| 0.6969 \| 0.84 \| 0.2795 \| 0.9478 \| 0.8400 \| 0.8164 \| 0.2464 \| 0.0418 \| \| 0.6381 \| 45.0 \| 2250 \| 0.6971 \| 0.835 \| 0.2760 \| 0.9712 \| 0.835 \| 0.8030 \| 0.2333 \| 0.0392 \| \| 0.6381 \| 46.0 \| 2300 \| 0.6985 \| 0.84 \| 0.2813 \| 0.9692 \| 0.8400 \| 0.8072 \| 0.2403 \| 0.0404 \| \| 0.6381 \| 47.0 \| 2350 \| 0.6976 \| 0.835 \| 0.2796 \| 1.0420 \| 0.835 \| 0.8042 \| 0.2374 \| 0.0406 \| \| 0.6381 \| 48.0 \| 2400 \| 0.6965 \| 0.85 \| 0.2778 \| 0.9753 \| 0.85 \| 0.8205 \| 0.2653 \| 0.0403 \| \| 0.6381 \| 49.0 \| 2450 \| 0.6969 \| 0.825 \| 0.2747 \| 0.9606 \| 0.825 \| 0.7871 \| 0.2478 \| 0.0394 \| \| 0.6274 \| 50.0 \| 2500 \| 0.6954 \| 0.835 \| 0.2746 \| 0.9572 \| 0.835 \| 0.8070 \| 0.2395 \| 0.0406 \| \| 0.6274 \| 51.0 \| 2550 \| 0.6972 \| 0.835 \| 0.2755 \| 1.0383 \| 0.835 \| 0.8070 \| 0.2484 \| 0.0391 \| \| 0.6274 \| 52.0 \| 2600 \| 0.6955 \| 0.83 \| 0.2752 \| 0.9699 \| 0.83 \| 0.7998 \| 0.2562 \| 0.0406 \| \| 0.6274 \| 53.0 \| 2650 \| 0.6950 \| 0.835 \| 0.2693 \| 0.9563 \| 0.835 \| 0.8030 \| 0.2300 \| 0.0373 \| \| 0.6274 \| 54.0 \| 2700 \| 0.6960 \| 0.83 \| 0.2727 \| 0.9646 \| 0.83 \| 0.7977 \| 0.2347 \| 0.0399 \| \| 0.6274 \| 55.0 \| 2750 \| 0.6946 \| 0.83 \| 0.2711 \| 0.9603 \| 0.83 \| 0.8058 \| 0.2279 \| 0.0384 \| \| 0.6274 \| 56.0 \| 2800 \| 0.6940 \| 0.835 \| 0.2726 \| 0.9579 \| 0.835 \| 0.8088 \| 0.2478 \| 0.0380 \| \| 0.6274 \| 57.0 \| 2850 \| 0.6951 \| 0.835 \| 0.2732 \| 0.9594 \| 0.835 \| 0.8090 \| 0.2336 \| 0.0418 \| \| 0.6274 \| 58.0 \| 2900 \| 0.6936 \| 0.84 \| 0.2684 \| 0.9575 \| 0.8400 \| 0.8079 \| 0.2490 \| 0.0373 \| \| 0.6274 \| 59.0 \| 2950 \| 0.6949 \| 0.835 \| 0.2701 \| 0.9543 \| 0.835 \| 0.8088 \| 0.2261 \| 0.0389 \| \| 0.6207 \| 60.0 \| 3000 \| 0.6939 \| 0.84 \| 0.2697 \| 0.9574 \| 0.8400 \| 0.8161 \| 0.2339 \| 0.0378 \| \| 0.6207 \| 61.0 \| 3050 \| 0.6952 \| 0.84 \| 0.2706 \| 0.9611 \| 0.8400 \| 0.8080 \| 0.2306 \| 0.0379 \| \| 0.6207 \| 62.0 \| 3100 \| 0.6940 \| 0.835 \| 0.2691 \| 0.9523 \| 0.835 \| 0.8086 \| 0.2451 \| 0.0382 \| \| 0.6207 \| 63.0 \| 3150 \| 0.6946 \| 0.835 \| 0.2672 \| 0.9627 \| 0.835 \| 0.8088 \| 0.2347 \| 0.0374 \| \| 0.6207 \| 64.0 \| 3200 \| 0.6949 \| 0.84 \| 0.2713 \| 0.9602 \| 0.8400 \| 0.8139 \| 0.2404 \| 0.0384 \| \| 0.6207 \| 65.0 \| 3250 \| 0.6944 \| 0.835 \| 0.2662 \| 0.9603 \| 0.835 \| 0.8079 \| 0.2308 \| 0.0377 \| \| 0.6207 \| 66.0 \| 3300 \| 0.6946 \| 0.835 \| 0.2698 \| 0.9593 \| 0.835 \| 0.8088 \| 0.2352 \| 0.0390 \| \| 0.6207 \| 67.0 \| 3350 \| 0.6934 \| 0.83 \| 0.2658 \| 0.9558 \| 0.83 \| 0.8060 \| 0.2260 \| 0.0384 \| \| 0.6207 \| 68.0 \| 3400 \| 0.6944 \| 0.83 \| 0.2689 \| 0.9517 \| 0.83 \| 0.8058 \| 0.2208 \| 0.0399 \| \| 0.6207 \| 69.0 \| 3450 \| 0.6946 \| 0.835 \| 0.2698 \| 0.9553 \| 0.835 \| 0.8042 \| 0.2331 \| 0.0383 \| \| 0.6156 \| 70.0 \| 3500 \| 0.6948 \| 0.83 \| 0.2690 \| 0.9549 \| 0.83 \| 0.8058 \| 0.2280 \| 0.0391 \| \| 0.6156 \| 71.0 \| 3550 \| 0.6936 \| 0.84 \| 0.2676 \| 0.9532 \| 0.8400 \| 0.8122 \| 0.2346 \| 0.0383 \| \| 0.6156 \| 72.0 \| 3600 \| 0.6946 \| 0.835 \| 0.2667 \| 0.9545 \| 0.835 \| 0.8088 \| 0.2492 \| 0.0379 \| \| 0.6156 \| 73.0 \| 3650 \| 0.6939 \| 0.84 \| 0.2670 \| 0.9534 \| 0.8400 \| 0.8139 \| 0.2466 \| 0.0377 \| \| 0.6156 \| 74.0 \| 3700 \| 0.6948 \| 0.835 \| 0.2695 \| 0.9522 \| 0.835 \| 0.8086 \| 0.2312 \| 0.0390 \| \| 0.6156 \| 75.0 \| 3750 \| 0.6951 \| 0.835 \| 0.2701 \| 0.9622 \| 0.835 \| 0.8111 \| 0.2158 \| 0.0397 \| \| 0.6156 \| 76.0 \| 3800 \| 0.6949 \| 0.84 \| 0.2682 \| 0.9606 \| 0.8400 \| 0.8139 \| 0.2415 \| 0.0382 \| \| 0.6156 \| 77.0 \| 3850 \| 0.6950 \| 0.84 \| 0.2684 \| 0.9629 \| 0.8400 \| 0.8118 \| 0.2493 \| 0.0381 \| \| 0.6156 \| 78.0 \| 3900 \| 0.6946 \| 0.835 \| 0.2685 \| 0.9522 \| 0.835 \| 0.8111 \| 0.2360 \| 0.0390 \| \| 0.6156 \| 79.0 \| 3950 \| 0.6944 \| 0.84 \| 0.2668 \| 0.9544 \| 0.8400 \| 0.8118 \| 0.2377 \| 0.0372 \| \| 0.612 \| 80.0 \| 4000 \| 0.6954 \| 0.84 \| 0.2692 \| 0.9579 \| 0.8400 \| 0.8139 \| 0.2321 \| 0.0381 \| \| 0.612 \| 81.0 \| 4050 \| 0.6956 \| 0.84 \| 0.2701 \| 0.9606 \| 0.8400 \| 0.8139 \| 0.2354 \| 0.0382 \| \| 0.612 \| 82.0 \| 4100 \| 0.6952 \| 0.835 \| 0.2686 \| 0.9600 \| 0.835 \| 0.8086 \| 0.2540 \| 0.0381 \| \| 0.612 \| 83.0 \| 4150 \| 0.6955 \| 0.835 \| 0.2689 \| 0.9571 \| 0.835 \| 0.8086 \| 0.2465 \| 0.0383 \| \| 0.612 \| 84.0 \| 4200 \| 0.6952 \| 0.84 \| 0.2689 \| 0.9583 \| 0.8400 \| 0.8159 \| 0.2308 \| 0.0387 \| \| 0.612 \| 85.0 \| 4250 \| 0.6956 \| 0.835 \| 0.2702 \| 0.9618 \| 0.835 \| 0.8042 \| 0.2365 \| 0.0386 \| \| 0.612 \| 86.0 \| 4300 \| 0.6950 \| 0.835 \| 0.2683 \| 0.9572 \| 0.835 \| 0.8086 \| 0.2228 \| 0.0382 \| \| 0.612 \| 87.0 \| 4350 \| 0.6949 \| 0.84 \| 0.2692 \| 0.9583 \| 0.8400 \| 0.8118 \| 0.2497 \| 0.0381 \| \| 0.612 \| 88.0 \| 4400 \| 0.6953 \| 0.845 \| 0.2695 \| 0.9617 \| 0.845 \| 0.8209 \| 0.2558 \| 0.0386 \| \| 0.612 \| 89.0 \| 4450 \| 0.6952 \| 0.845 \| 0.2689 \| 0.9611 \| 0.845 \| 0.8209 \| 0.2251 \| 0.0383 \| \| 0.6097 \| 90.0 \| 4500 \| 0.6961 \| 0.835 \| 0.2701 \| 0.9645 \| 0.835 \| 0.8042 \| 0.2444 \| 0.0386 \| \| 0.6097 \| 91.0 \| 4550 \| 0.6954 \| 0.845 \| 0.2689 \| 0.9619 \| 0.845 \| 0.8209 \| 0.2324 \| 0.0383 \| \| 0.6097 \| 92.0 \| 4600 \| 0.6959 \| 0.845 \| 0.2700 \| 0.9636 \| 0.845 \| 0.8209 \| 0.2277 \| 0.0388 \| \| 0.6097 \| 93.0 \| 4650 \| 0.6959 \| 0.85 \| 0.2694 \| 0.9654 \| 0.85 \| 0.8241 \| 0.2396 \| 0.0379 \| \| 0.6097 \| 94.0 \| 4700 \| 0.6960 \| 0.85 \| 0.2696 \| 0.9643 \| 0.85 \| 0.8241 \| 0.2471 \| 0.0379 \| \| 0.6097 \| 95.0 \| 4750 \| 0.6959 \| 0.85 \| 0.2694 \| 0.9650 \| 0.85 \| 0.8241 \| 0.2233 \| 0.0378 \| \| 0.6097 \| 96.0 \| 4800 \| 0.6962 \| 0.845 \| 0.2700 \| 0.9666 \| 0.845 \| 0.8144 \| 0.2558 \| 0.0382 \| \| 0.6097 \| 97.0 \| 4850 \| 0.6962 \| 0.85 \| 0.2699 \| 0.9662 \| 0.85 \| 0.8241 \| 0.2400 \| 0.0381 \| \| 0.6097 \| 98.0 \| 4900 \| 0.6962 \| 0.85 \| 0.2700 \| 0.9662 \| 0.85 \| 0.8241 \| 0.2396 \| 0.0380 \| \| 0.6097 \| 99.0 \| 4950 \| 0.6963 \| 0.85 \| 0.2700 \| 0.9667 \| 0.85 \| 0.8241 \| 0.2478 \| 0.0379 \| \| 0.6083 \| 100.0 \| 5000 \| 0.6962 \| 0.85 \| 0.2700 \| 0.9667 \| 0.85 \| 0.8241 \| 0.2479 \| 0.0379 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/dit-base_tobacco-small_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-base_tobacco-small_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 309.8690 - Accuracy: 0.815 - Brier Loss: 0.3313 - Nll: 1.1190 - F1 Micro: 0.815 - F1 Macro: 0.7825 - Ece: 0.2569 - Aurc: 0.0659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 328.2257 \| 0.365 \| 0.8441 \| 5.5835 \| 0.3650 \| 0.2447 \| 0.3371 \| 0.4390 \| \| No log \| 2.0 \| 100 \| 325.5961 \| 0.58 \| 0.6442 \| 1.8499 \| 0.58 \| 0.4727 \| 0.3233 \| 0.2414 \| \| No log \| 3.0 \| 150 \| 323.3813 \| 0.66 \| 0.4759 \| 1.5815 \| 0.66 \| 0.5348 \| 0.2704 \| 0.1489 \| \| No log \| 4.0 \| 200 \| 322.5013 \| 0.715 \| 0.4234 \| 1.6240 \| 0.715 \| 0.6009 \| 0.2382 \| 0.1142 \| \| No log \| 5.0 \| 250 \| 321.7315 \| 0.755 \| 0.3532 \| 1.2868 \| 0.755 \| 0.6596 \| 0.2141 \| 0.0687 \| \| No log \| 6.0 \| 300 \| 320.5884 \| 0.775 \| 0.3668 \| 1.4106 \| 0.775 \| 0.7233 \| 0.2284 \| 0.0922 \| \| No log \| 7.0 \| 350 \| 320.8456 \| 0.775 \| 0.3638 \| 1.4833 \| 0.775 \| 0.7172 \| 0.2487 \| 0.0666 \| \| No log \| 8.0 \| 400 \| 319.6829 \| 0.785 \| 0.3308 \| 1.3914 \| 0.785 \| 0.7203 \| 0.1959 \| 0.0674 \| \| No log \| 9.0 \| 450 \| 319.7741 \| 0.815 \| 0.3459 \| 1.3920 \| 0.815 \| 0.7832 \| 0.2541 \| 0.0681 \| \| 325.907 \| 10.0 \| 500 \| 319.4605 \| 0.775 \| 0.3162 \| 1.2997 \| 0.775 \| 0.6987 \| 0.2140 \| 0.0575 \| \| 325.907 \| 11.0 \| 550 \| 318.6996 \| 0.81 \| 0.3190 \| 1.2271 \| 0.81 \| 0.7670 \| 0.2110 \| 0.0614 \| \| 325.907 \| 12.0 \| 600 \| 318.0233 \| 0.81 \| 0.3183 \| 1.2432 \| 0.81 \| 0.7673 \| 0.2134 \| 0.0624 \| \| 325.907 \| 13.0 \| 650 \| 318.2606 \| 0.79 \| 0.3259 \| 1.2187 \| 0.79 \| 0.7457 \| 0.2299 \| 0.0591 \| \| 325.907 \| 14.0 \| 700 \| 317.7428 \| 0.83 \| 0.3183 \| 1.3279 \| 0.83 \| 0.8035 \| 0.2449 \| 0.0512 \| \| 325.907 \| 15.0 \| 750 \| 317.7053 \| 0.81 \| 0.3251 \| 1.2097 \| 0.81 \| 0.7604 \| 0.2193 \| 0.0566 \| \| 325.907 \| 16.0 \| 800 \| 317.3470 \| 0.84 \| 0.3142 \| 1.2606 \| 0.8400 \| 0.8132 \| 0.2272 \| 0.0484 \| \| 325.907 \| 17.0 \| 850 \| 316.8029 \| 0.815 \| 0.3202 \| 1.1571 \| 0.815 \| 0.7748 \| 0.2316 \| 0.0563 \| \| 325.907 \| 18.0 \| 900 \| 316.9777 \| 0.805 \| 0.3442 \| 1.1453 \| 0.805 \| 0.7645 \| 0.2432 \| 0.0625 \| \| 325.907 \| 19.0 \| 950 \| 316.2359 \| 0.815 \| 0.3219 \| 1.1399 \| 0.815 \| 0.7717 \| 0.2404 \| 0.0603 \| \| 320.1296 \| 20.0 \| 1000 \| 316.1051 \| 0.8 \| 0.3220 \| 1.1807 \| 0.8000 \| 0.7500 \| 0.2412 \| 0.0576 \| \| 320.1296 \| 21.0 \| 1050 \| 315.8117 \| 0.845 \| 0.3099 \| 1.0976 \| 0.845 \| 0.8084 \| 0.2530 \| 0.0547 \| \| 320.1296 \| 22.0 \| 1100 \| 315.7457 \| 0.82 \| 0.3238 \| 1.1904 \| 0.82 \| 0.7663 \| 0.2507 \| 0.0548 \| \| 320.1296 \| 23.0 \| 1150 \| 315.6591 \| 0.82 \| 0.3357 \| 1.4044 \| 0.82 \| 0.7925 \| 0.2639 \| 0.0586 \| \| 320.1296 \| 24.0 \| 1200 \| 315.4048 \| 0.82 \| 0.3270 \| 1.0817 \| 0.82 \| 0.7681 \| 0.2575 \| 0.0629 \| \| 320.1296 \| 25.0 \| 1250 \| 314.9790 \| 0.81 \| 0.3309 \| 1.2002 \| 0.81 \| 0.7732 \| 0.2279 \| 0.0656 \| \| 320.1296 \| 26.0 \| 1300 \| 314.6778 \| 0.79 \| 0.3189 \| 1.1219 \| 0.79 \| 0.7464 \| 0.2014 \| 0.0576 \| \| 320.1296 \| 27.0 \| 1350 \| 314.7844 \| 0.8 \| 0.3345 \| 1.0655 \| 0.8000 \| 0.7555 \| 0.2398 \| 0.0661 \| \| 320.1296 \| 28.0 \| 1400 \| 314.4464 \| 0.815 \| 0.3175 \| 1.1116 \| 0.815 \| 0.7636 \| 0.2426 \| 0.0532 \| \| 320.1296 \| 29.0 \| 1450 \| 314.3737 \| 0.845 \| 0.3271 \| 1.1042 \| 0.845 \| 0.8072 \| 0.2595 \| 0.0531 \| \| 317.1926 \| 30.0 \| 1500 \| 313.9464 \| 0.82 \| 0.3225 \| 1.1270 \| 0.82 \| 0.7841 \| 0.2087 \| 0.0609 \| \| 317.1926 \| 31.0 \| 1550 \| 314.0068 \| 0.835 \| 0.3187 \| 1.1834 \| 0.835 \| 0.8070 \| 0.2470 \| 0.0522 \| \| 317.1926 \| 32.0 \| 1600 \| 313.8198 \| 0.81 \| 0.3271 \| 1.0324 \| 0.81 \| 0.7642 \| 0.2484 \| 0.0609 \| \| 317.1926 \| 33.0 \| 1650 \| 313.7599 \| 0.83 \| 0.3193 \| 1.0993 \| 0.83 \| 0.7910 \| 0.2382 \| 0.0536 \| \| 317.1926 \| 34.0 \| 1700 \| 313.4889 \| 0.82 \| 0.3224 \| 1.1743 \| 0.82 \| 0.7823 \| 0.2587 \| 0.0546 \| \| 317.1926 \| 35.0 \| 1750 \| 313.2496 \| 0.825 \| 0.3324 \| 1.1041 \| 0.825 \| 0.7988 \| 0.2404 \| 0.0652 \| \| 317.1926 \| 36.0 \| 1800 \| 313.1823 \| 0.83 \| 0.3207 \| 1.0900 \| 0.83 \| 0.8007 \| 0.2505 \| 0.0581 \| \| 317.1926 \| 37.0 \| 1850 \| 313.1304 \| 0.83 \| 0.3367 \| 1.2073 \| 0.83 \| 0.7973 \| 0.2615 \| 0.0571 \| \| 317.1926 \| 38.0 \| 1900 \| 313.2971 \| 0.815 \| 0.3398 \| 1.1045 \| 0.815 \| 0.7709 \| 0.2411 \| 0.0641 \| \| 317.1926 \| 39.0 \| 1950 \| 313.0526 \| 0.815 \| 0.3352 \| 1.1023 \| 0.815 \| 0.7744 \| 0.2455 \| 0.0616 \| \| 315.1897 \| 40.0 \| 2000 \| 312.7858 \| 0.84 \| 0.3231 \| 1.0983 \| 0.8400 \| 0.8096 \| 0.2619 \| 0.0538 \| \| 315.1897 \| 41.0 \| 2050 \| 312.5119 \| 0.815 \| 0.3290 \| 1.1174 \| 0.815 \| 0.7858 \| 0.2540 \| 0.0604 \| \| 315.1897 \| 42.0 \| 2100 \| 312.5961 \| 0.82 \| 0.3305 \| 1.2144 \| 0.82 \| 0.7787 \| 0.2480 \| 0.0572 \| \| 315.1897 \| 43.0 \| 2150 \| 312.3510 \| 0.825 \| 0.3357 \| 1.1367 \| 0.825 \| 0.7936 \| 0.2398 \| 0.0658 \| \| 315.1897 \| 44.0 \| 2200 \| 312.4015 \| 0.81 \| 0.3303 \| 1.1015 \| 0.81 \| 0.7837 \| 0.2488 \| 0.0598 \| \| 315.1897 \| 45.0 \| 2250 \| 312.2003 \| 0.825 \| 0.3286 \| 1.1810 \| 0.825 \| 0.7953 \| 0.2480 \| 0.0614 \| \| 315.1897 \| 46.0 \| 2300 \| 312.1683 \| 0.825 \| 0.3283 \| 1.1112 \| 0.825 \| 0.7881 \| 0.2414 \| 0.0587 \| \| 315.1897 \| 47.0 \| 2350 \| 312.2554 \| 0.815 \| 0.3433 \| 1.1313 \| 0.815 \| 0.7709 \| 0.2579 \| 0.0694 \| \| 315.1897 \| 48.0 \| 2400 \| 312.0919 \| 0.825 \| 0.3364 \| 1.1074 \| 0.825 \| 0.7963 \| 0.2471 \| 0.0636 \| \| 315.1897 \| 49.0 \| 2450 \| 312.0760 \| 0.82 \| 0.3412 \| 1.1076 \| 0.82 \| 0.7859 \| 0.2554 \| 0.0661 \| \| 313.7276 \| 50.0 \| 2500 \| 311.7450 \| 0.83 \| 0.3245 \| 1.1723 \| 0.83 \| 0.7994 \| 0.2512 \| 0.0558 \| \| 313.7276 \| 51.0 \| 2550 \| 311.5801 \| 0.835 \| 0.3236 \| 1.1056 \| 0.835 \| 0.7954 \| 0.2581 \| 0.0576 \| \| 313.7276 \| 52.0 \| 2600 \| 311.7016 \| 0.83 \| 0.3235 \| 1.1182 \| 0.83 \| 0.7988 \| 0.2462 \| 0.0560 \| \| 313.7276 \| 53.0 \| 2650 \| 311.0808 \| 0.81 \| 0.3308 \| 1.0526 \| 0.81 \| 0.7716 \| 0.2401 \| 0.0687 \| \| 313.7276 \| 54.0 \| 2700 \| 311.3835 \| 0.81 \| 0.3304 \| 1.1210 \| 0.81 \| 0.7803 \| 0.2442 \| 0.0604 \| \| 313.7276 \| 55.0 \| 2750 \| 311.1007 \| 0.825 \| 0.3285 \| 1.1265 \| 0.825 \| 0.7931 \| 0.2569 \| 0.0639 \| \| 313.7276 \| 56.0 \| 2800 \| 311.3446 \| 0.81 \| 0.3273 \| 1.1896 \| 0.81 \| 0.7810 \| 0.2342 \| 0.0622 \| \| 313.7276 \| 57.0 \| 2850 \| 311.0753 \| 0.825 \| 0.3327 \| 1.1225 \| 0.825 \| 0.7929 \| 0.2659 \| 0.0668 \| \| 313.7276 \| 58.0 \| 2900 \| 311.3600 \| 0.825 \| 0.3320 \| 1.1142 \| 0.825 \| 0.8000 \| 0.2524 \| 0.0640 \| \| 313.7276 \| 59.0 \| 2950 \| 310.8636 \| 0.83 \| 0.3242 \| 1.1157 \| 0.83 \| 0.8022 \| 0.2416 \| 0.0633 \| \| 312.6368 \| 60.0 \| 3000 \| 310.7809 \| 0.815 \| 0.3386 \| 1.2166 \| 0.815 \| 0.7820 \| 0.2571 \| 0.0702 \| \| 312.6368 \| 61.0 \| 3050 \| 310.9625 \| 0.825 \| 0.3273 \| 1.1168 \| 0.825 \| 0.7923 \| 0.2362 \| 0.0608 \| \| 312.6368 \| 62.0 \| 3100 \| 311.1122 \| 0.81 \| 0.3369 \| 1.1021 \| 0.81 \| 0.7700 \| 0.2433 \| 0.0633 \| \| 312.6368 \| 63.0 \| 3150 \| 311.1530 \| 0.82 \| 0.3351 \| 1.1108 \| 0.82 \| 0.7780 \| 0.2584 \| 0.0615 \| \| 312.6368 \| 64.0 \| 3200 \| 310.9366 \| 0.8 \| 0.3288 \| 1.1112 \| 0.8000 \| 0.7616 \| 0.2545 \| 0.0609 \| \| 312.6368 \| 65.0 \| 3250 \| 310.7639 \| 0.82 \| 0.3379 \| 1.0992 \| 0.82 \| 0.7898 \| 0.2407 \| 0.0710 \| \| 312.6368 \| 66.0 \| 3300 \| 310.5876 \| 0.81 \| 0.3287 \| 1.1197 \| 0.81 \| 0.7763 \| 0.2270 \| 0.0654 \| \| 312.6368 \| 67.0 \| 3350 \| 310.7344 \| 0.805 \| 0.3387 \| 1.1279 \| 0.805 \| 0.7646 \| 0.2354 \| 0.0679 \| \| 312.6368 \| 68.0 \| 3400 \| 310.2750 \| 0.825 \| 0.3323 \| 1.1367 \| 0.825 \| 0.7971 \| 0.2514 \| 0.0673 \| \| 312.6368 \| 69.0 \| 3450 \| 310.5080 \| 0.815 \| 0.3298 \| 1.1049 \| 0.815 \| 0.7845 \| 0.2329 \| 0.0664 \| \| 311.7616 \| 70.0 \| 3500 \| 310.6353 \| 0.81 \| 0.3305 \| 1.1098 \| 0.81 \| 0.7745 \| 0.2346 \| 0.0633 \| \| 311.7616 \| 71.0 \| 3550 \| 310.3249 \| 0.825 \| 0.3286 \| 1.1117 \| 0.825 \| 0.7951 \| 0.2455 \| 0.0641 \| \| 311.7616 \| 72.0 \| 3600 \| 310.5689 \| 0.825 \| 0.3248 \| 1.1079 \| 0.825 \| 0.7911 \| 0.2388 \| 0.0586 \| \| 311.7616 \| 73.0 \| 3650 \| 310.4175 \| 0.82 \| 0.3298 \| 1.1169 \| 0.82 \| 0.7859 \| 0.2338 \| 0.0630 \| \| 311.7616 \| 74.0 \| 3700 \| 310.1338 \| 0.815 \| 0.3313 \| 1.1236 \| 0.815 \| 0.7902 \| 0.2558 \| 0.0677 \| \| 311.7616 \| 75.0 \| 3750 \| 310.4428 \| 0.825 \| 0.3310 \| 1.1269 \| 0.825 \| 0.7972 \| 0.2458 \| 0.0606 \| \| 311.7616 \| 76.0 \| 3800 \| 310.3477 \| 0.81 \| 0.3317 \| 1.1060 \| 0.81 \| 0.7775 \| 0.2392 \| 0.0654 \| \| 311.7616 \| 77.0 \| 3850 \| 310.2144 \| 0.815 \| 0.3294 \| 1.1076 \| 0.815 \| 0.7857 \| 0.2387 \| 0.0627 \| \| 311.7616 \| 78.0 \| 3900 \| 310.1073 \| 0.82 \| 0.3296 \| 1.1246 \| 0.82 \| 0.7993 \| 0.2496 \| 0.0634 \| \| 311.7616 \| 79.0 \| 3950 \| 310.1449 \| 0.805 \| 0.3246 \| 1.1134 \| 0.805 \| 0.7734 \| 0.2277 \| 0.0627 \| \| 311.1587 \| 80.0 \| 4000 \| 310.1684 \| 0.81 \| 0.3327 \| 1.1094 \| 0.81 \| 0.7781 \| 0.2493 \| 0.0660 \| \| 311.1587 \| 81.0 \| 4050 \| 310.1772 \| 0.815 \| 0.3311 \| 1.1129 \| 0.815 \| 0.7876 \| 0.2447 \| 0.0668 \| \| 311.1587 \| 82.0 \| 4100 \| 309.9326 \| 0.805 \| 0.3295 \| 1.1172 \| 0.805 \| 0.7716 \| 0.2508 \| 0.0666 \| \| 311.1587 \| 83.0 \| 4150 \| 310.1067 \| 0.805 \| 0.3330 \| 1.1209 \| 0.805 \| 0.7756 \| 0.2252 \| 0.0653 \| \| 311.1587 \| 84.0 \| 4200 \| 309.9362 \| 0.825 \| 0.3288 \| 1.1150 \| 0.825 \| 0.8024 \| 0.2500 \| 0.0637 \| \| 311.1587 \| 85.0 \| 4250 \| 309.6593 \| 0.81 \| 0.3292 \| 1.1226 \| 0.81 \| 0.7723 \| 0.2306 \| 0.0680 \| \| 311.1587 \| 86.0 \| 4300 \| 309.9828 \| 0.8 \| 0.3310 \| 1.1252 \| 0.8000 \| 0.7643 \| 0.2474 \| 0.0662 \| \| 311.1587 \| 87.0 \| 4350 \| 310.0325 \| 0.825 \| 0.3322 \| 1.1136 \| 0.825 \| 0.7935 \| 0.2633 \| 0.0634 \| \| 311.1587 \| 88.0 \| 4400 \| 309.8688 \| 0.815 \| 0.3320 \| 1.1145 \| 0.815 \| 0.7824 \| 0.2478 \| 0.0675 \| \| 311.1587 \| 89.0 \| 4450 \| 310.0577 \| 0.81 \| 0.3324 \| 1.1160 \| 0.81 \| 0.7810 \| 0.2475 \| 0.0648 \| \| 310.732 \| 90.0 \| 4500 \| 309.8999 \| 0.81 \| 0.3273 \| 1.1120 \| 0.81 \| 0.7720 \| 0.2356 \| 0.0624 \| \| 310.732 \| 91.0 \| 4550 \| 309.7399 \| 0.815 \| 0.3256 \| 1.1164 \| 0.815 \| 0.7824 \| 0.2502 \| 0.0649 \| \| 310.732 \| 92.0 \| 4600 \| 309.9419 \| 0.805 \| 0.3287 \| 1.1183 \| 0.805 \| 0.7751 \| 0.2353 \| 0.0640 \| \| 310.732 \| 93.0 \| 4650 \| 309.9055 \| 0.81 \| 0.3268 \| 1.1194 \| 0.81 \| 0.7761 \| 0.2429 \| 0.0613 \| \| 310.732 \| 94.0 \| 4700 \| 309.7320 \| 0.82 \| 0.3275 \| 1.1117 \| 0.82 \| 0.7914 \| 0.2408 \| 0.0654 \| \| 310.732 \| 95.0 \| 4750 \| 309.9635 \| 0.81 \| 0.3334 \| 1.1067 \| 0.81 \| 0.7747 \| 0.2317 \| 0.0637 \| \| 310.732 \| 96.0 \| 4800 \| 309.9630 \| 0.805 \| 0.3304 \| 1.1165 \| 0.805 \| 0.7712 \| 0.2316 \| 0.0631 \| \| 310.732 \| 97.0 \| 4850 \| 309.8564 \| 0.815 \| 0.3263 \| 1.1130 \| 0.815 \| 0.7870 \| 0.2355 \| 0.0619 \| \| 310.732 \| 98.0 \| 4900 \| 309.7815 \| 0.815 \| 0.3298 \| 1.1198 \| 0.815 \| 0.7857 \| 0.2386 \| 0.0634 \| \| 310.732 \| 99.0 \| 4950 \| 309.8337 \| 0.81 \| 0.3354 \| 1.0806 \| 0.81 \| 0.7818 \| 0.2480 \| 0.0672 \| \| 310.5225 \| 100.0 \| 5000 \| 309.8690 \| 0.815 \| 0.3313 \| 1.1190 \| 0.815 \| 0.7825 \| 0.2569 \| 0.0659 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_kd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8158 - Accuracy: 0.565 - Brier Loss: 0.6104 - Nll: 2.6027 - F1 Micro: 0.565 - F1 Macro: 0.4783 - Ece: 0.2677 - Aurc: 0.2516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 1.4988 \| 0.065 \| 0.9007 \| 9.6504 \| 0.065 \| 0.0267 \| 0.1512 \| 0.9377 \| \| No log \| 2.0 \| 8 \| 1.4615 \| 0.155 \| 0.8961 \| 7.9200 \| 0.155 \| 0.0268 \| 0.2328 \| 0.9605 \| \| No log \| 3.0 \| 12 \| 1.4913 \| 0.155 \| 0.9531 \| 11.6402 \| 0.155 \| 0.0268 \| 0.3390 \| 0.8899 \| \| No log \| 4.0 \| 16 \| 2.2747 \| 0.155 \| 1.4111 \| 11.0294 \| 0.155 \| 0.0268 \| 0.7077 \| 0.7068 \| \| No log \| 5.0 \| 20 \| 2.4543 \| 0.155 \| 1.4359 \| 8.3074 \| 0.155 \| 0.0268 \| 0.7226 \| 0.6151 \| \| No log \| 6.0 \| 24 \| 1.9614 \| 0.155 \| 1.1785 \| 6.6431 \| 0.155 \| 0.0283 \| 0.5497 \| 0.6022 \| \| No log \| 7.0 \| 28 \| 1.6280 \| 0.18 \| 0.9978 \| 5.8468 \| 0.18 \| 0.0488 \| 0.4014 \| 0.6135 \| \| No log \| 8.0 \| 32 \| 1.3465 \| 0.225 \| 0.8993 \| 5.6177 \| 0.225 \| 0.0740 \| 0.3378 \| 0.5786 \| \| No log \| 9.0 \| 36 \| 1.2597 \| 0.225 \| 0.8794 \| 5.0542 \| 0.225 \| 0.0727 \| 0.3403 \| 0.5658 \| \| No log \| 10.0 \| 40 \| 1.1149 \| 0.27 \| 0.8181 \| 4.5188 \| 0.27 \| 0.1222 \| 0.2890 \| 0.5230 \| \| No log \| 11.0 \| 44 \| 0.9805 \| 0.31 \| 0.7600 \| 3.8687 \| 0.31 \| 0.1726 \| 0.2703 \| 0.4690 \| \| No log \| 12.0 \| 48 \| 1.0099 \| 0.335 \| 0.7732 \| 3.6652 \| 0.335 \| 0.2095 \| 0.2892 \| 0.4739 \| \| No log \| 13.0 \| 52 \| 1.0522 \| 0.335 \| 0.7919 \| 3.3843 \| 0.335 \| 0.2562 \| 0.3006 \| 0.6402 \| \| No log \| 14.0 \| 56 \| 1.0566 \| 0.32 \| 0.7868 \| 3.4244 \| 0.32 \| 0.2373 \| 0.3023 \| 0.6094 \| \| No log \| 15.0 \| 60 \| 0.9670 \| 0.405 \| 0.7333 \| 3.3926 \| 0.405 \| 0.3189 \| 0.3013 \| 0.4037 \| \| No log \| 16.0 \| 64 \| 1.0979 \| 0.31 \| 0.7877 \| 3.3045 \| 0.31 \| 0.2262 \| 0.2792 \| 0.5720 \| \| No log \| 17.0 \| 68 \| 0.9022 \| 0.44 \| 0.6913 \| 3.2277 \| 0.44 \| 0.3429 \| 0.2902 \| 0.3657 \| \| No log \| 18.0 \| 72 \| 1.2120 \| 0.315 \| 0.8075 \| 4.1289 \| 0.315 \| 0.2323 \| 0.2857 \| 0.5909 \| \| No log \| 19.0 \| 76 \| 1.1945 \| 0.39 \| 0.7974 \| 4.2350 \| 0.39 \| 0.3292 \| 0.3271 \| 0.5989 \| \| No log \| 20.0 \| 80 \| 1.3861 \| 0.345 \| 0.7981 \| 5.2605 \| 0.345 \| 0.2700 \| 0.2832 \| 0.5299 \| \| No log \| 21.0 \| 84 \| 1.2243 \| 0.33 \| 0.8073 \| 4.5262 \| 0.33 \| 0.2545 \| 0.3068 \| 0.6133 \| \| No log \| 22.0 \| 88 \| 1.0455 \| 0.38 \| 0.7238 \| 2.7133 \| 0.38 \| 0.3084 \| 0.2901 \| 0.4855 \| \| No log \| 23.0 \| 92 \| 0.9044 \| 0.45 \| 0.6814 \| 3.4361 \| 0.45 \| 0.3273 \| 0.2927 \| 0.3246 \| \| No log \| 24.0 \| 96 \| 0.8930 \| 0.495 \| 0.6596 \| 3.3412 \| 0.495 \| 0.4185 \| 0.2882 \| 0.3070 \| \| No log \| 25.0 \| 100 \| 0.8665 \| 0.485 \| 0.6534 \| 2.9998 \| 0.485 \| 0.4154 \| 0.2641 \| 0.3298 \| \| No log \| 26.0 \| 104 \| 1.0458 \| 0.375 \| 0.7579 \| 3.1074 \| 0.375 \| 0.3333 \| 0.2735 \| 0.5293 \| \| No log \| 27.0 \| 108 \| 1.0170 \| 0.41 \| 0.7321 \| 2.8884 \| 0.41 \| 0.3468 \| 0.2976 \| 0.4566 \| \| No log \| 28.0 \| 112 \| 1.0956 \| 0.395 \| 0.7464 \| 3.3094 \| 0.395 \| 0.3255 \| 0.3154 \| 0.4684 \| \| No log \| 29.0 \| 116 \| 1.0805 \| 0.39 \| 0.7544 \| 3.2115 \| 0.39 \| 0.3193 \| 0.3014 \| 0.4594 \| \| No log \| 30.0 \| 120 \| 1.2358 \| 0.375 \| 0.7733 \| 4.3992 \| 0.375 \| 0.3058 \| 0.2845 \| 0.4876 \| \| No log \| 31.0 \| 124 \| 1.0532 \| 0.4 \| 0.7458 \| 2.7398 \| 0.4000 \| 0.3614 \| 0.2890 \| 0.4961 \| \| No log \| 32.0 \| 128 \| 1.0166 \| 0.365 \| 0.7355 \| 2.5093 \| 0.3650 \| 0.2862 \| 0.2728 \| 0.5057 \| \| No log \| 33.0 \| 132 \| 0.9395 \| 0.48 \| 0.6807 \| 2.6211 \| 0.48 \| 0.4394 \| 0.2843 \| 0.3719 \| \| No log \| 34.0 \| 136 \| 0.8718 \| 0.52 \| 0.6538 \| 2.6802 \| 0.52 \| 0.4697 \| 0.2954 \| 0.3051 \| \| No log \| 35.0 \| 140 \| 0.8339 \| 0.51 \| 0.6362 \| 3.1084 \| 0.51 \| 0.4373 \| 0.2654 \| 0.3006 \| \| No log \| 36.0 \| 144 \| 0.8411 \| 0.51 \| 0.6359 \| 2.7881 \| 0.51 \| 0.4286 \| 0.2759 \| 0.2906 \| \| No log \| 37.0 \| 148 \| 0.8556 \| 0.505 \| 0.6402 \| 2.5519 \| 0.505 \| 0.4076 \| 0.2522 \| 0.3060 \| \| No log \| 38.0 \| 152 \| 1.0928 \| 0.395 \| 0.7438 \| 2.8660 \| 0.395 \| 0.3337 \| 0.2815 \| 0.4724 \| \| No log \| 39.0 \| 156 \| 1.3830 \| 0.39 \| 0.8135 \| 4.7392 \| 0.39 \| 0.3094 \| 0.2879 \| 0.5239 \| \| No log \| 40.0 \| 160 \| 1.2180 \| 0.38 \| 0.7760 \| 3.8384 \| 0.38 \| 0.3106 \| 0.2614 \| 0.5109 \| \| No log \| 41.0 \| 164 \| 1.1337 \| 0.365 \| 0.7486 \| 2.8843 \| 0.3650 \| 0.2948 \| 0.2665 \| 0.4630 \| \| No log \| 42.0 \| 168 \| 0.8814 \| 0.53 \| 0.6425 \| 2.3353 \| 0.53 \| 0.4645 \| 0.2968 \| 0.2973 \| \| No log \| 43.0 \| 172 \| 0.8324 \| 0.515 \| 0.6174 \| 2.4407 \| 0.515 \| 0.4517 \| 0.2847 \| 0.2742 \| \| No log \| 44.0 \| 176 \| 0.8477 \| 0.53 \| 0.6282 \| 2.5469 \| 0.53 \| 0.4615 \| 0.2712 \| 0.2831 \| \| No log \| 45.0 \| 180 \| 0.8307 \| 0.515 \| 0.6190 \| 2.4871 \| 0.515 \| 0.4404 \| 0.2594 \| 0.2845 \| \| No log \| 46.0 \| 184 \| 0.8116 \| 0.53 \| 0.6070 \| 2.4944 \| 0.53 \| 0.4410 \| 0.2337 \| 0.2451 \| \| No log \| 47.0 \| 188 \| 0.8349 \| 0.54 \| 0.6260 \| 2.2843 \| 0.54 \| 0.4423 \| 0.2911 \| 0.2616 \| \| No log \| 48.0 \| 192 \| 0.8298 \| 0.555 \| 0.6178 \| 2.2946 \| 0.555 \| 0.4725 \| 0.2568 \| 0.2482 \| \| No log \| 49.0 \| 196 \| 0.8252 \| 0.565 \| 0.6141 \| 2.3311 \| 0.565 \| 0.4762 \| 0.2810 \| 0.2504 \| \| No log \| 50.0 \| 200 \| 0.8158 \| 0.565 \| 0.6104 \| 2.6027 \| 0.565 \| 0.4783 \| 0.2677 \| 0.2516 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hkivancoral/hushem_5x_deit_base_adamax_0001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_0001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4553 - Accuracy: 0.9024 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7557 \| 1.0 \| 28 \| 0.4830 \| 0.7805 \| \| 0.1392 \| 2.0 \| 56 \| 0.3138 \| 0.8293 \| \| 0.0115 \| 3.0 \| 84 \| 0.3481 \| 0.8537 \| \| 0.0078 \| 4.0 \| 112 \| 0.3098 \| 0.8537 \| \| 0.0013 \| 5.0 \| 140 \| 0.3283 \| 0.9024 \| \| 0.0023 \| 6.0 \| 168 \| 0.5654 \| 0.8537 \| \| 0.0004 \| 7.0 \| 196 \| 0.4129 \| 0.9024 \| \| 0.0003 \| 8.0 \| 224 \| 0.4041 \| 0.9024 \| \| 0.0002 \| 9.0 \| 252 \| 0.4192 \| 0.9024 \| \| 0.0002 \| 10.0 \| 280 \| 0.4257 \| 0.9024 \| \| 0.0002 \| 11.0 \| 308 \| 0.4271 \| 0.9024 \| \| 0.0002 \| 12.0 \| 336 \| 0.4272 \| 0.9024 \| \| 0.0001 \| 13.0 \| 364 \| 0.4303 \| 0.9024 \| \| 0.0002 \| 14.0 \| 392 \| 0.4309 \| 0.9024 \| \| 0.0001 \| 15.0 \| 420 \| 0.4302 \| 0.9024 \| \| 0.0001 \| 16.0 \| 448 \| 0.4300 \| 0.9024 \| \| 0.0001 \| 17.0 \| 476 \| 0.4319 \| 0.9024 \| \| 0.0001 \| 18.0 \| 504 \| 0.4342 \| 0.9024 \| \| 0.0001 \| 19.0 \| 532 \| 0.4349 \| 0.9024 \| \| 0.0001 \| 20.0 \| 560 \| 0.4354 \| 0.9024 \| \| 0.0001 \| 21.0 \| 588 \| 0.4378 \| 0.9024 \| \| 0.0001 \| 22.0 \| 616 \| 0.4393 \| 0.9024 \| \| 0.0001 \| 23.0 \| 644 \| 0.4414 \| 0.9024 \| \| 0.0001 \| 24.0 \| 672 \| 0.4417 \| 0.9024 \| \| 0.0001 \| 25.0 \| 700 \| 0.4428 \| 0.9024 \| \| 0.0001 \| 26.0 \| 728 \| 0.4429 \| 0.9024 \| \| 0.0001 \| 27.0 \| 756 \| 0.4437 \| 0.9024 \| \| 0.0001 \| 28.0 \| 784 \| 0.4437 \| 0.9024 \| \| 0.0001 \| 29.0 \| 812 \| 0.4449 \| 0.9024 \| \| 0.0001 \| 30.0 \| 840 \| 0.4459 \| 0.9024 \| \| 0.0001 \| 31.0 \| 868 \| 0.4470 \| 0.9024 \| \| 0.0001 \| 32.0 \| 896 \| 0.4471 \| 0.9024 \| \| 0.0001 \| 33.0 \| 924 \| 0.4499 \| 0.9024 \| \| 0.0001 \| 34.0 \| 952 \| 0.4499 \| 0.9024 \| \| 0.0001 \| 35.0 \| 980 \| 0.4504 \| 0.9024 \| \| 0.0001 \| 36.0 \| 1008 \| 0.4504 \| 0.9024 \| \| 0.0001 \| 37.0 \| 1036 \| 0.4513 \| 0.9024 \| \| 0.0001 \| 38.0 \| 1064 \| 0.4525 \| 0.9024 \| \| 0.0001 \| 39.0 \| 1092 \| 0.4530 \| 0.9024 \| \| 0.0001 \| 40.0 \| 1120 \| 0.4533 \| 0.9024 \| \| 0.0001 \| 41.0 \| 1148 \| 0.4538 \| 0.9024 \| \| 0.0001 \| 42.0 \| 1176 \| 0.4539 \| 0.9024 \| \| 0.0001 \| 43.0 \| 1204 \| 0.4547 \| 0.9024 \| \| 0.0001 \| 44.0 \| 1232 \| 0.4551 \| 0.9024 \| \| 0.0001 \| 45.0 \| 1260 \| 0.4551 \| 0.9024 \| \| 0.0001 \| 46.0 \| 1288 \| 0.4551 \| 0.9024 \| \| 0.0001 \| 47.0 \| 1316 \| 0.4553 \| 0.9024 \| \| 0.0001 \| 48.0 \| 1344 \| 0.4553 \| 0.9024 \| \| 0.0001 \| 49.0 \| 1372 \| 0.4553 \| 0.9024 \| \| 0.0001 \| 50.0 \| 1400 \| 0.4553 \| 0.9024 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.0_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.0_a0.5 This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8577 - Accuracy: 0.53 - Brier Loss: 0.6406 - Nll: 2.1208 - F1 Micro: 0.53 - F1 Macro: 0.4957 - Ece: 0.3004 - Aurc: 0.3168 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 1.4267 \| 0.05 \| 0.9008 \| 9.6592 \| 0.0500 \| 0.0177 \| 0.1432 \| 0.9439 \| \| No log \| 2.0 \| 8 \| 1.4006 \| 0.155 \| 0.8969 \| 7.9140 \| 0.155 \| 0.0268 \| 0.2365 \| 0.9603 \| \| No log \| 3.0 \| 12 \| 1.4621 \| 0.155 \| 0.9457 \| 13.3695 \| 0.155 \| 0.0268 \| 0.3013 \| 0.9107 \| \| No log \| 4.0 \| 16 \| 2.1836 \| 0.155 \| 1.3252 \| 12.8977 \| 0.155 \| 0.0268 \| 0.6400 \| 0.7514 \| \| No log \| 5.0 \| 20 \| 2.4365 \| 0.155 \| 1.3998 \| 8.4435 \| 0.155 \| 0.0268 \| 0.7030 \| 0.6102 \| \| No log \| 6.0 \| 24 \| 2.1554 \| 0.155 \| 1.2534 \| 6.9190 \| 0.155 \| 0.0279 \| 0.5987 \| 0.6271 \| \| No log \| 7.0 \| 28 \| 1.5617 \| 0.175 \| 0.9637 \| 5.7454 \| 0.175 \| 0.0462 \| 0.3802 \| 0.6485 \| \| No log \| 8.0 \| 32 \| 1.3267 \| 0.245 \| 0.8707 \| 5.2368 \| 0.245 \| 0.0835 \| 0.2961 \| 0.5438 \| \| No log \| 9.0 \| 36 \| 1.2434 \| 0.19 \| 0.8886 \| 5.0360 \| 0.19 \| 0.0471 \| 0.3198 \| 0.7720 \| \| No log \| 10.0 \| 40 \| 1.0721 \| 0.305 \| 0.8123 \| 4.5157 \| 0.305 \| 0.1762 \| 0.2684 \| 0.5269 \| \| No log \| 11.0 \| 44 \| 1.1256 \| 0.22 \| 0.8429 \| 3.9215 \| 0.22 \| 0.1083 \| 0.2812 \| 0.7346 \| \| No log \| 12.0 \| 48 \| 0.9865 \| 0.35 \| 0.7676 \| 3.4553 \| 0.35 \| 0.2565 \| 0.2884 \| 0.4790 \| \| No log \| 13.0 \| 52 \| 1.0206 \| 0.355 \| 0.7899 \| 3.3582 \| 0.3550 \| 0.2278 \| 0.2954 \| 0.5883 \| \| No log \| 14.0 \| 56 \| 0.9096 \| 0.415 \| 0.6994 \| 3.2174 \| 0.415 \| 0.3147 \| 0.2563 \| 0.3596 \| \| No log \| 15.0 \| 60 \| 0.9187 \| 0.415 \| 0.7129 \| 3.2059 \| 0.415 \| 0.2742 \| 0.2941 \| 0.3971 \| \| No log \| 16.0 \| 64 \| 0.8905 \| 0.395 \| 0.6956 \| 2.9931 \| 0.395 \| 0.2618 \| 0.2590 \| 0.3826 \| \| No log \| 17.0 \| 68 \| 0.9108 \| 0.425 \| 0.7073 \| 3.1634 \| 0.425 \| 0.2855 \| 0.2995 \| 0.3685 \| \| No log \| 18.0 \| 72 \| 0.8769 \| 0.465 \| 0.6706 \| 3.1088 \| 0.465 \| 0.3652 \| 0.2855 \| 0.3261 \| \| No log \| 19.0 \| 76 \| 0.8585 \| 0.475 \| 0.6687 \| 2.8710 \| 0.4750 \| 0.3884 \| 0.2916 \| 0.3282 \| \| No log \| 20.0 \| 80 \| 0.9822 \| 0.405 \| 0.7378 \| 2.8889 \| 0.405 \| 0.3570 \| 0.2850 \| 0.4895 \| \| No log \| 21.0 \| 84 \| 0.9324 \| 0.445 \| 0.6992 \| 2.7975 \| 0.445 \| 0.3553 \| 0.3021 \| 0.3762 \| \| No log \| 22.0 \| 88 \| 1.0330 \| 0.42 \| 0.7350 \| 2.7487 \| 0.4200 \| 0.3506 \| 0.2984 \| 0.4771 \| \| No log \| 23.0 \| 92 \| 0.8755 \| 0.455 \| 0.6674 \| 2.5903 \| 0.455 \| 0.3415 \| 0.2570 \| 0.3352 \| \| No log \| 24.0 \| 96 \| 0.8651 \| 0.47 \| 0.6443 \| 2.8456 \| 0.47 \| 0.3800 \| 0.2451 \| 0.2975 \| \| No log \| 25.0 \| 100 \| 0.9567 \| 0.445 \| 0.7150 \| 2.7083 \| 0.445 \| 0.3727 \| 0.2667 \| 0.4676 \| \| No log \| 26.0 \| 104 \| 1.0224 \| 0.42 \| 0.7376 \| 2.4408 \| 0.4200 \| 0.3367 \| 0.2968 \| 0.5019 \| \| No log \| 27.0 \| 108 \| 0.8365 \| 0.525 \| 0.6407 \| 2.6426 \| 0.525 \| 0.4496 \| 0.2960 \| 0.2657 \| \| No log \| 28.0 \| 112 \| 0.9798 \| 0.425 \| 0.7287 \| 2.6379 \| 0.425 \| 0.3489 \| 0.2640 \| 0.4668 \| \| No log \| 29.0 \| 116 \| 0.9226 \| 0.44 \| 0.6965 \| 2.5748 \| 0.44 \| 0.3669 \| 0.2561 \| 0.4054 \| \| No log \| 30.0 \| 120 \| 0.8303 \| 0.49 \| 0.6398 \| 2.4839 \| 0.49 \| 0.3924 \| 0.2981 \| 0.2936 \| \| No log \| 31.0 \| 124 \| 0.8426 \| 0.52 \| 0.6478 \| 2.5282 \| 0.52 \| 0.4322 \| 0.3109 \| 0.3084 \| \| No log \| 32.0 \| 128 \| 0.9111 \| 0.45 \| 0.6970 \| 2.3870 \| 0.45 \| 0.3947 \| 0.2837 \| 0.4448 \| \| No log \| 33.0 \| 132 \| 0.8723 \| 0.51 \| 0.6524 \| 2.6124 \| 0.51 \| 0.4170 \| 0.2536 \| 0.3365 \| \| No log \| 34.0 \| 136 \| 0.8936 \| 0.47 \| 0.6671 \| 2.8892 \| 0.47 \| 0.3814 \| 0.2436 \| 0.3357 \| \| No log \| 35.0 \| 140 \| 1.2870 \| 0.42 \| 0.7660 \| 4.4020 \| 0.4200 \| 0.3468 \| 0.2860 \| 0.4606 \| \| No log \| 36.0 \| 144 \| 0.9991 \| 0.455 \| 0.7289 \| 2.6973 \| 0.455 \| 0.4132 \| 0.3272 \| 0.4684 \| \| No log \| 37.0 \| 148 \| 1.6352 \| 0.365 \| 0.8356 \| 4.7695 \| 0.3650 \| 0.3020 \| 0.3312 \| 0.6069 \| \| No log \| 38.0 \| 152 \| 1.3014 \| 0.39 \| 0.8213 \| 2.9436 \| 0.39 \| 0.3382 \| 0.3262 \| 0.5476 \| \| No log \| 39.0 \| 156 \| 1.0294 \| 0.415 \| 0.7361 \| 2.7188 \| 0.415 \| 0.3446 \| 0.2454 \| 0.4632 \| \| No log \| 40.0 \| 160 \| 0.8825 \| 0.52 \| 0.6538 \| 2.3887 \| 0.52 \| 0.4608 \| 0.2721 \| 0.3186 \| \| No log \| 41.0 \| 164 \| 0.8572 \| 0.54 \| 0.6288 \| 2.4201 \| 0.54 \| 0.4822 \| 0.2963 \| 0.2899 \| \| No log \| 42.0 \| 168 \| 0.8393 \| 0.535 \| 0.6291 \| 2.3587 \| 0.535 \| 0.4726 \| 0.2824 \| 0.2937 \| \| No log \| 43.0 \| 172 \| 0.8369 \| 0.515 \| 0.6303 \| 2.4060 \| 0.515 \| 0.4583 \| 0.2689 \| 0.2903 \| \| No log \| 44.0 \| 176 \| 0.8458 \| 0.49 \| 0.6346 \| 2.3323 \| 0.49 \| 0.4428 \| 0.2526 \| 0.2951 \| \| No log \| 45.0 \| 180 \| 0.8446 \| 0.49 \| 0.6367 \| 2.2207 \| 0.49 \| 0.4289 \| 0.2655 \| 0.3041 \| \| No log \| 46.0 \| 184 \| 0.8324 \| 0.54 \| 0.6289 \| 2.3685 \| 0.54 \| 0.4779 \| 0.2571 \| 0.2873 \| \| No log \| 47.0 \| 188 \| 0.8658 \| 0.515 \| 0.6486 \| 2.3922 \| 0.515 \| 0.4584 \| 0.2623 \| 0.3100 \| \| No log \| 48.0 \| 192 \| 0.8516 \| 0.525 \| 0.6410 \| 2.4448 \| 0.525 \| 0.4700 \| 0.3006 \| 0.3044 \| \| No log \| 49.0 \| 196 \| 0.8520 \| 0.55 \| 0.6350 \| 2.2049 \| 0.55 \| 0.4947 \| 0.3030 \| 0.2980 \| \| No log \| 50.0 \| 200 \| 0.8577 \| 0.53 \| 0.6406 \| 2.1208 \| 0.53 \| 0.4957 \| 0.3004 \| 0.3168 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_MSE	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_kd_MSE This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1315 - Accuracy: 0.365 - Brier Loss: 0.7313 - Nll: 5.5846 - F1 Micro: 0.3650 - F1 Macro: 0.2369 - Ece: 0.2526 - Aurc: 0.4412 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 1.1507 \| 0.1 \| 0.8998 \| 9.9508 \| 0.1000 \| 0.0462 \| 0.1728 \| 0.9208 \| \| No log \| 2.0 \| 8 \| 0.9900 \| 0.155 \| 0.8924 \| 9.6289 \| 0.155 \| 0.0268 \| 0.2400 \| 0.9571 \| \| No log \| 3.0 \| 12 \| 0.8441 \| 0.155 \| 0.9273 \| 8.9944 \| 0.155 \| 0.0268 \| 0.3276 \| 0.9345 \| \| No log \| 4.0 \| 16 \| 1.4048 \| 0.155 \| 1.3149 \| 8.9869 \| 0.155 \| 0.0268 \| 0.6569 \| 0.6091 \| \| No log \| 5.0 \| 20 \| 1.0761 \| 0.155 \| 1.1553 \| 8.9185 \| 0.155 \| 0.0272 \| 0.5441 \| 0.6112 \| \| No log \| 6.0 \| 24 \| 1.1745 \| 0.155 \| 1.1386 \| 9.2644 \| 0.155 \| 0.0304 \| 0.4982 \| 0.6120 \| \| No log \| 7.0 \| 28 \| 0.4686 \| 0.225 \| 0.8829 \| 7.3879 \| 0.225 \| 0.0724 \| 0.3173 \| 0.5804 \| \| No log \| 8.0 \| 32 \| 0.3535 \| 0.24 \| 0.8393 \| 7.0880 \| 0.24 \| 0.0797 \| 0.2963 \| 0.5518 \| \| No log \| 9.0 \| 36 \| 0.2519 \| 0.295 \| 0.8157 \| 6.6738 \| 0.295 \| 0.1375 \| 0.2944 \| 0.4810 \| \| No log \| 10.0 \| 40 \| 0.2957 \| 0.265 \| 0.8432 \| 6.8903 \| 0.265 \| 0.1030 \| 0.3171 \| 0.5807 \| \| No log \| 11.0 \| 44 \| 0.5224 \| 0.21 \| 0.8832 \| 8.6128 \| 0.2100 \| 0.0987 \| 0.2948 \| 0.6814 \| \| No log \| 12.0 \| 48 \| 0.4088 \| 0.18 \| 0.8807 \| 7.0533 \| 0.18 \| 0.0309 \| 0.2966 \| 0.7466 \| \| No log \| 13.0 \| 52 \| 0.5082 \| 0.225 \| 0.8732 \| 8.3126 \| 0.225 \| 0.0606 \| 0.2761 \| 0.7285 \| \| No log \| 14.0 \| 56 \| 0.5253 \| 0.18 \| 0.8905 \| 8.3229 \| 0.18 \| 0.0305 \| 0.2973 \| 0.7838 \| \| No log \| 15.0 \| 60 \| 0.5612 \| 0.225 \| 0.8579 \| 7.9410 \| 0.225 \| 0.0642 \| 0.2690 \| 0.7108 \| \| No log \| 16.0 \| 64 \| 0.2805 \| 0.28 \| 0.8094 \| 6.0275 \| 0.28 \| 0.1475 \| 0.2633 \| 0.5701 \| \| No log \| 17.0 \| 68 \| 0.3076 \| 0.32 \| 0.8151 \| 6.1462 \| 0.32 \| 0.1641 \| 0.2852 \| 0.6162 \| \| No log \| 18.0 \| 72 \| 0.3824 \| 0.29 \| 0.8072 \| 6.0214 \| 0.29 \| 0.1681 \| 0.2900 \| 0.6048 \| \| No log \| 19.0 \| 76 \| 0.5089 \| 0.19 \| 0.8701 \| 8.9391 \| 0.19 \| 0.0418 \| 0.2582 \| 0.7152 \| \| No log \| 20.0 \| 80 \| 0.1490 \| 0.335 \| 0.7347 \| 5.7349 \| 0.335 \| 0.1786 \| 0.2500 \| 0.4430 \| \| No log \| 21.0 \| 84 \| 0.3448 \| 0.255 \| 0.8455 \| 6.6598 \| 0.255 \| 0.0998 \| 0.3124 \| 0.7183 \| \| No log \| 22.0 \| 88 \| 0.6254 \| 0.22 \| 0.8413 \| 6.9926 \| 0.22 \| 0.0966 \| 0.2654 \| 0.7197 \| \| No log \| 23.0 \| 92 \| 0.5464 \| 0.215 \| 0.8909 \| 8.4952 \| 0.2150 \| 0.0570 \| 0.2931 \| 0.7084 \| \| No log \| 24.0 \| 96 \| 0.4465 \| 0.24 \| 0.8445 \| 7.2319 \| 0.24 \| 0.1396 \| 0.2575 \| 0.6667 \| \| No log \| 25.0 \| 100 \| 0.3967 \| 0.215 \| 0.8547 \| 6.7234 \| 0.2150 \| 0.0962 \| 0.2913 \| 0.7053 \| \| No log \| 26.0 \| 104 \| 0.2459 \| 0.295 \| 0.8041 \| 5.1627 \| 0.295 \| 0.1901 \| 0.2525 \| 0.6590 \| \| No log \| 27.0 \| 108 \| 0.4125 \| 0.19 \| 0.8595 \| 7.1181 \| 0.19 \| 0.0551 \| 0.2707 \| 0.7087 \| \| No log \| 28.0 \| 112 \| 0.1686 \| 0.36 \| 0.7309 \| 5.1322 \| 0.36 \| 0.2178 \| 0.2296 \| 0.4432 \| \| No log \| 29.0 \| 116 \| 0.3573 \| 0.205 \| 0.8664 \| 6.6815 \| 0.205 \| 0.0523 \| 0.2753 \| 0.7131 \| \| No log \| 30.0 \| 120 \| 0.1634 \| 0.32 \| 0.7416 \| 5.6798 \| 0.32 \| 0.1862 \| 0.2473 \| 0.4616 \| \| No log \| 31.0 \| 124 \| 0.1404 \| 0.35 \| 0.7295 \| 5.6538 \| 0.35 \| 0.2152 \| 0.2688 \| 0.4389 \| \| No log \| 32.0 \| 128 \| 0.1435 \| 0.325 \| 0.7415 \| 5.5376 \| 0.325 \| 0.1439 \| 0.2567 \| 0.4489 \| \| No log \| 33.0 \| 132 \| 0.1428 \| 0.33 \| 0.7292 \| 5.5151 \| 0.33 \| 0.1791 \| 0.2502 \| 0.4403 \| \| No log \| 34.0 \| 136 \| 0.1602 \| 0.33 \| 0.7371 \| 5.8829 \| 0.33 \| 0.1941 \| 0.2542 \| 0.4481 \| \| No log \| 35.0 \| 140 \| 0.1663 \| 0.325 \| 0.7398 \| 5.6501 \| 0.325 \| 0.1880 \| 0.2443 \| 0.4564 \| \| No log \| 36.0 \| 144 \| 0.1637 \| 0.35 \| 0.7422 \| 5.9440 \| 0.35 \| 0.2053 \| 0.2748 \| 0.4361 \| \| No log \| 37.0 \| 148 \| 0.1520 \| 0.325 \| 0.7317 \| 5.3284 \| 0.325 \| 0.1787 \| 0.2677 \| 0.4531 \| \| No log \| 38.0 \| 152 \| 0.1585 \| 0.335 \| 0.7385 \| 5.9712 \| 0.335 \| 0.1939 \| 0.2648 \| 0.4483 \| \| No log \| 39.0 \| 156 \| 0.1491 \| 0.335 \| 0.7334 \| 5.6729 \| 0.335 \| 0.1912 \| 0.2533 \| 0.4404 \| \| No log \| 40.0 \| 160 \| 0.1367 \| 0.32 \| 0.7297 \| 5.7350 \| 0.32 \| 0.1818 \| 0.2512 \| 0.4498 \| \| No log \| 41.0 \| 164 \| 0.2089 \| 0.335 \| 0.7583 \| 5.2150 \| 0.335 \| 0.2073 \| 0.2822 \| 0.4712 \| \| No log \| 42.0 \| 168 \| 0.1612 \| 0.335 \| 0.7323 \| 4.9145 \| 0.335 \| 0.2058 \| 0.2696 \| 0.4482 \| \| No log \| 43.0 \| 172 \| 0.1616 \| 0.335 \| 0.7349 \| 5.4305 \| 0.335 \| 0.1916 \| 0.2650 \| 0.4493 \| \| No log \| 44.0 \| 176 \| 0.1477 \| 0.335 \| 0.7335 \| 5.3482 \| 0.335 \| 0.1761 \| 0.2478 \| 0.4410 \| \| No log \| 45.0 \| 180 \| 0.1426 \| 0.34 \| 0.7321 \| 5.4265 \| 0.34 \| 0.2018 \| 0.2307 \| 0.4483 \| \| No log \| 46.0 \| 184 \| 0.1531 \| 0.345 \| 0.7351 \| 5.2269 \| 0.345 \| 0.2108 \| 0.2812 \| 0.4572 \| \| No log \| 47.0 \| 188 \| 0.1426 \| 0.34 \| 0.7299 \| 5.1412 \| 0.34 \| 0.2040 \| 0.2418 \| 0.4443 \| \| No log \| 48.0 \| 192 \| 0.1321 \| 0.335 \| 0.7353 \| 5.2955 \| 0.335 \| 0.2017 \| 0.2515 \| 0.4547 \| \| No log \| 49.0 \| 196 \| 0.1330 \| 0.34 \| 0.7332 \| 5.5391 \| 0.34 \| 0.2065 \| 0.2485 \| 0.4524 \| \| No log \| 50.0 \| 200 \| 0.1315 \| 0.365 \| 0.7313 \| 5.5846 \| 0.3650 \| 0.2369 \| 0.2526 \| 0.4412 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2266 - Accuracy: 0.385 - Brier Loss: 0.7374 - Nll: 4.0859 - F1 Micro: 0.3850 - F1 Macro: 0.2652 - Ece: 0.2858 - Aurc: 0.4261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 3.9610 \| 0.05 \| 0.9004 \| 9.1922 \| 0.0500 \| 0.0100 \| 0.1457 \| 0.9423 \| \| No log \| 2.0 \| 8 \| 3.8752 \| 0.155 \| 0.8934 \| 8.5838 \| 0.155 \| 0.0268 \| 0.2356 \| 0.9630 \| \| No log \| 3.0 \| 12 \| 3.8665 \| 0.155 \| 0.9261 \| 8.8645 \| 0.155 \| 0.0268 \| 0.3113 \| 0.7263 \| \| No log \| 4.0 \| 16 \| 5.0552 \| 0.155 \| 1.3190 \| 8.8736 \| 0.155 \| 0.0268 \| 0.6667 \| 0.6226 \| \| No log \| 5.0 \| 20 \| 4.9755 \| 0.155 \| 1.2873 \| 8.9603 \| 0.155 \| 0.0270 \| 0.6315 \| 0.6033 \| \| No log \| 6.0 \| 24 \| 4.6069 \| 0.155 \| 1.1443 \| 7.0637 \| 0.155 \| 0.0301 \| 0.5057 \| 0.6065 \| \| No log \| 7.0 \| 28 \| 3.7058 \| 0.22 \| 0.9193 \| 6.6528 \| 0.22 \| 0.0685 \| 0.3454 \| 0.5737 \| \| No log \| 8.0 \| 32 \| 3.3000 \| 0.25 \| 0.8140 \| 6.8642 \| 0.25 \| 0.1011 \| 0.2638 \| 0.5377 \| \| No log \| 9.0 \| 36 \| 3.3805 \| 0.195 \| 0.8768 \| 6.5108 \| 0.195 \| 0.0779 \| 0.2955 \| 0.7532 \| \| No log \| 10.0 \| 40 \| 3.4626 \| 0.2 \| 0.8985 \| 6.3933 \| 0.2000 \| 0.0745 \| 0.3154 \| 0.7384 \| \| No log \| 11.0 \| 44 \| 3.2088 \| 0.32 \| 0.7621 \| 6.0433 \| 0.32 \| 0.1695 \| 0.2375 \| 0.4457 \| \| No log \| 12.0 \| 48 \| 3.4543 \| 0.22 \| 0.8720 \| 6.1413 \| 0.22 \| 0.1065 \| 0.3144 \| 0.7026 \| \| No log \| 13.0 \| 52 \| 3.5300 \| 0.225 \| 0.8684 \| 7.0938 \| 0.225 \| 0.1182 \| 0.2747 \| 0.7110 \| \| No log \| 14.0 \| 56 \| 3.5981 \| 0.215 \| 0.8821 \| 7.5146 \| 0.2150 \| 0.0978 \| 0.3047 \| 0.7351 \| \| No log \| 15.0 \| 60 \| 3.5641 \| 0.23 \| 0.8895 \| 7.7554 \| 0.23 \| 0.0944 \| 0.2985 \| 0.7568 \| \| No log \| 16.0 \| 64 \| 3.5853 \| 0.235 \| 0.8698 \| 6.6949 \| 0.235 \| 0.1292 \| 0.2634 \| 0.6518 \| \| No log \| 17.0 \| 68 \| 3.5539 \| 0.255 \| 0.8597 \| 7.5062 \| 0.255 \| 0.1331 \| 0.2821 \| 0.6332 \| \| No log \| 18.0 \| 72 \| 3.5725 \| 0.265 \| 0.8569 \| 7.4117 \| 0.265 \| 0.1396 \| 0.2708 \| 0.5940 \| \| No log \| 19.0 \| 76 \| 3.5207 \| 0.27 \| 0.8415 \| 6.5482 \| 0.27 \| 0.1542 \| 0.2592 \| 0.5619 \| \| No log \| 20.0 \| 80 \| 3.5360 \| 0.26 \| 0.8573 \| 7.4207 \| 0.26 \| 0.1358 \| 0.2942 \| 0.5949 \| \| No log \| 21.0 \| 84 \| 3.2807 \| 0.345 \| 0.7933 \| 4.8232 \| 0.345 \| 0.2077 \| 0.2903 \| 0.5385 \| \| No log \| 22.0 \| 88 \| 3.1633 \| 0.39 \| 0.7217 \| 4.3843 \| 0.39 \| 0.2417 \| 0.2547 \| 0.3857 \| \| No log \| 23.0 \| 92 \| 3.2159 \| 0.39 \| 0.7463 \| 4.4691 \| 0.39 \| 0.2481 \| 0.2923 \| 0.3756 \| \| No log \| 24.0 \| 96 \| 3.1650 \| 0.375 \| 0.7248 \| 4.4043 \| 0.375 \| 0.2276 \| 0.2433 \| 0.3809 \| \| No log \| 25.0 \| 100 \| 3.2000 \| 0.375 \| 0.7470 \| 4.7004 \| 0.375 \| 0.2473 \| 0.2671 \| 0.4264 \| \| No log \| 26.0 \| 104 \| 3.4356 \| 0.27 \| 0.8326 \| 6.6479 \| 0.27 \| 0.1466 \| 0.2636 \| 0.5640 \| \| No log \| 27.0 \| 108 \| 3.5761 \| 0.285 \| 0.8347 \| 6.5689 \| 0.285 \| 0.1796 \| 0.2537 \| 0.6182 \| \| No log \| 28.0 \| 112 \| 3.5778 \| 0.26 \| 0.8546 \| 7.0753 \| 0.26 \| 0.1380 \| 0.2629 \| 0.5870 \| \| No log \| 29.0 \| 116 \| 3.1280 \| 0.39 \| 0.7075 \| 4.5179 \| 0.39 \| 0.2450 \| 0.2479 \| 0.3759 \| \| No log \| 30.0 \| 120 \| 3.1559 \| 0.37 \| 0.7268 \| 4.3444 \| 0.37 \| 0.2413 \| 0.2588 \| 0.3941 \| \| No log \| 31.0 \| 124 \| 3.1493 \| 0.39 \| 0.7133 \| 4.6188 \| 0.39 \| 0.2305 \| 0.2338 \| 0.3686 \| \| No log \| 32.0 \| 128 \| 3.1287 \| 0.39 \| 0.7015 \| 4.0848 \| 0.39 \| 0.2379 \| 0.2271 \| 0.3655 \| \| No log \| 33.0 \| 132 \| 3.1409 \| 0.395 \| 0.7048 \| 4.0026 \| 0.395 \| 0.2290 \| 0.2210 \| 0.3606 \| \| No log \| 34.0 \| 136 \| 3.1691 \| 0.375 \| 0.7210 \| 4.4086 \| 0.375 \| 0.2215 \| 0.2495 \| 0.3800 \| \| No log \| 35.0 \| 140 \| 3.1529 \| 0.4 \| 0.7117 \| 4.1376 \| 0.4000 \| 0.2487 \| 0.2200 \| 0.3605 \| \| No log \| 36.0 \| 144 \| 3.1088 \| 0.4 \| 0.6989 \| 4.0773 \| 0.4000 \| 0.2645 \| 0.2478 \| 0.3641 \| \| No log \| 37.0 \| 148 \| 3.2158 \| 0.4 \| 0.7230 \| 4.1145 \| 0.4000 \| 0.2603 \| 0.2517 \| 0.3761 \| \| No log \| 38.0 \| 152 \| 3.1351 \| 0.39 \| 0.7064 \| 4.3952 \| 0.39 \| 0.2398 \| 0.2475 \| 0.3606 \| \| No log \| 39.0 \| 156 \| 3.1239 \| 0.395 \| 0.7001 \| 4.0496 \| 0.395 \| 0.2569 \| 0.2364 \| 0.3583 \| \| No log \| 40.0 \| 160 \| 3.1855 \| 0.385 \| 0.7169 \| 4.0634 \| 0.3850 \| 0.2274 \| 0.2467 \| 0.3687 \| \| No log \| 41.0 \| 164 \| 3.1938 \| 0.37 \| 0.7098 \| 3.9505 \| 0.37 \| 0.2146 \| 0.2207 \| 0.3781 \| \| No log \| 42.0 \| 168 \| 3.3495 \| 0.395 \| 0.7438 \| 4.0247 \| 0.395 \| 0.2428 \| 0.2901 \| 0.3973 \| \| No log \| 43.0 \| 172 \| 3.2352 \| 0.395 \| 0.7115 \| 3.9875 \| 0.395 \| 0.2431 \| 0.2651 \| 0.3790 \| \| No log \| 44.0 \| 176 \| 3.2838 \| 0.39 \| 0.7223 \| 3.8867 \| 0.39 \| 0.2246 \| 0.2590 \| 0.3824 \| \| No log \| 45.0 \| 180 \| 3.3175 \| 0.395 \| 0.7304 \| 4.2165 \| 0.395 \| 0.2286 \| 0.2549 \| 0.3811 \| \| No log \| 46.0 \| 184 \| 3.1183 \| 0.395 \| 0.6916 \| 3.9786 \| 0.395 \| 0.2338 \| 0.2345 \| 0.3581 \| \| No log \| 47.0 \| 188 \| 3.1608 \| 0.395 \| 0.7049 \| 3.7245 \| 0.395 \| 0.2580 \| 0.2429 \| 0.3668 \| \| No log \| 48.0 \| 192 \| 3.2144 \| 0.38 \| 0.7316 \| 3.9593 \| 0.38 \| 0.2512 \| 0.2517 \| 0.4202 \| \| No log \| 49.0 \| 196 \| 3.2781 \| 0.365 \| 0.7561 \| 3.9721 \| 0.3650 \| 0.2440 \| 0.2429 \| 0.4654 \| \| No log \| 50.0 \| 200 \| 3.2266 \| 0.385 \| 0.7374 \| 4.0859 \| 0.3850 \| 0.2652 \| 0.2858 \| 0.4261 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_hint	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_hint This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 24.6607 - Accuracy: 0.57 - Brier Loss: 0.6012 - Nll: 2.9238 - F1 Micro: 0.57 - F1 Macro: 0.5344 - Ece: 0.2496 - Aurc: 0.2274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 27.2504 \| 0.07 \| 0.9006 \| 8.5876 \| 0.07 \| 0.0131 \| 0.1634 \| 0.9646 \| \| No log \| 2.0 \| 14 \| 27.1186 \| 0.155 \| 0.9229 \| 12.2960 \| 0.155 \| 0.0268 \| 0.2967 \| 0.8769 \| \| No log \| 3.0 \| 21 \| 27.9163 \| 0.155 \| 1.3722 \| 11.2040 \| 0.155 \| 0.0268 \| 0.6887 \| 0.5963 \| \| No log \| 4.0 \| 28 \| 28.2724 \| 0.155 \| 1.4334 \| 9.3615 \| 0.155 \| 0.0273 \| 0.7029 \| 0.6185 \| \| No log \| 5.0 \| 35 \| 26.9699 \| 0.175 \| 1.0316 \| 5.3928 \| 0.175 \| 0.0465 \| 0.4168 \| 0.5989 \| \| No log \| 6.0 \| 42 \| 26.1797 \| 0.23 \| 0.8746 \| 3.9558 \| 0.23 \| 0.0993 \| 0.3120 \| 0.5627 \| \| No log \| 7.0 \| 49 \| 25.8507 \| 0.25 \| 0.8299 \| 3.2357 \| 0.25 \| 0.1721 \| 0.2686 \| 0.6618 \| \| No log \| 8.0 \| 56 \| 25.7515 \| 0.24 \| 0.8336 \| 2.7738 \| 0.24 \| 0.1579 \| 0.2670 \| 0.6619 \| \| No log \| 9.0 \| 63 \| 25.3041 \| 0.39 \| 0.7346 \| 2.5881 \| 0.39 \| 0.2914 \| 0.2649 \| 0.4362 \| \| No log \| 10.0 \| 70 \| 25.1996 \| 0.375 \| 0.7406 \| 2.7338 \| 0.375 \| 0.2616 \| 0.2903 \| 0.4923 \| \| No log \| 11.0 \| 77 \| 25.0418 \| 0.44 \| 0.6756 \| 3.2534 \| 0.44 \| 0.3173 \| 0.2520 \| 0.3197 \| \| No log \| 12.0 \| 84 \| 25.3664 \| 0.35 \| 0.8231 \| 3.6209 \| 0.35 \| 0.2628 \| 0.2924 \| 0.5484 \| \| No log \| 13.0 \| 91 \| 25.0353 \| 0.44 \| 0.6927 \| 3.5523 \| 0.44 \| 0.3230 \| 0.2842 \| 0.3332 \| \| No log \| 14.0 \| 98 \| 25.2980 \| 0.36 \| 0.8265 \| 3.3953 \| 0.36 \| 0.2859 \| 0.3158 \| 0.5347 \| \| No log \| 15.0 \| 105 \| 24.8521 \| 0.425 \| 0.6604 \| 3.0888 \| 0.425 \| 0.3379 \| 0.2641 \| 0.3096 \| \| No log \| 16.0 \| 112 \| 24.8368 \| 0.46 \| 0.6622 \| 2.7863 \| 0.46 \| 0.3626 \| 0.2771 \| 0.3429 \| \| No log \| 17.0 \| 119 \| 25.0490 \| 0.355 \| 0.7909 \| 2.9342 \| 0.3550 \| 0.2764 \| 0.3300 \| 0.5313 \| \| No log \| 18.0 \| 126 \| 24.9950 \| 0.4 \| 0.7521 \| 3.5010 \| 0.4000 \| 0.3467 \| 0.2801 \| 0.4721 \| \| No log \| 19.0 \| 133 \| 24.7232 \| 0.505 \| 0.6259 \| 2.9709 \| 0.505 \| 0.4017 \| 0.2799 \| 0.2807 \| \| No log \| 20.0 \| 140 \| 24.7500 \| 0.5 \| 0.6408 \| 3.1274 \| 0.5 \| 0.4278 \| 0.2398 \| 0.2752 \| \| No log \| 21.0 \| 147 \| 24.5976 \| 0.54 \| 0.5922 \| 2.7847 \| 0.54 \| 0.4872 \| 0.2422 \| 0.2319 \| \| No log \| 22.0 \| 154 \| 24.9329 \| 0.42 \| 0.7518 \| 2.9924 \| 0.4200 \| 0.3777 \| 0.3094 \| 0.4446 \| \| No log \| 23.0 \| 161 \| 24.6088 \| 0.535 \| 0.6089 \| 2.8494 \| 0.535 \| 0.5067 \| 0.2756 \| 0.2770 \| \| No log \| 24.0 \| 168 \| 25.1851 \| 0.39 \| 0.8175 \| 3.7625 \| 0.39 \| 0.3513 \| 0.3211 \| 0.5049 \| \| No log \| 25.0 \| 175 \| 24.5058 \| 0.585 \| 0.5754 \| 2.6524 \| 0.585 \| 0.5707 \| 0.2296 \| 0.2227 \| \| No log \| 26.0 \| 182 \| 25.2073 \| 0.435 \| 0.7812 \| 3.0365 \| 0.435 \| 0.3839 \| 0.3190 \| 0.5012 \| \| No log \| 27.0 \| 189 \| 24.7752 \| 0.54 \| 0.6558 \| 2.9071 \| 0.54 \| 0.4667 \| 0.2669 \| 0.2898 \| \| No log \| 28.0 \| 196 \| 24.8546 \| 0.515 \| 0.6697 \| 2.5989 \| 0.515 \| 0.4397 \| 0.2943 \| 0.3817 \| \| No log \| 29.0 \| 203 \| 24.5759 \| 0.56 \| 0.5969 \| 2.6234 \| 0.56 \| 0.5342 \| 0.2609 \| 0.2493 \| \| No log \| 30.0 \| 210 \| 24.7052 \| 0.53 \| 0.6198 \| 2.9462 \| 0.53 \| 0.4811 \| 0.2779 \| 0.2766 \| \| No log \| 31.0 \| 217 \| 24.5828 \| 0.545 \| 0.6038 \| 2.7967 \| 0.545 \| 0.4979 \| 0.2455 \| 0.2369 \| \| No log \| 32.0 \| 224 \| 24.6622 \| 0.545 \| 0.6220 \| 2.8878 \| 0.545 \| 0.4925 \| 0.2854 \| 0.2682 \| \| No log \| 33.0 \| 231 \| 24.6253 \| 0.57 \| 0.5991 \| 3.1607 \| 0.57 \| 0.5327 \| 0.2869 \| 0.2518 \| \| No log \| 34.0 \| 238 \| 24.6230 \| 0.535 \| 0.6351 \| 2.5626 \| 0.535 \| 0.5245 \| 0.2766 \| 0.3077 \| \| No log \| 35.0 \| 245 \| 24.5803 \| 0.59 \| 0.5900 \| 2.8215 \| 0.59 \| 0.5564 \| 0.2724 \| 0.2563 \| \| No log \| 36.0 \| 252 \| 24.5679 \| 0.57 \| 0.5709 \| 3.1573 \| 0.57 \| 0.5089 \| 0.2523 \| 0.2222 \| \| No log \| 37.0 \| 259 \| 24.5375 \| 0.575 \| 0.5631 \| 2.9349 \| 0.575 \| 0.5381 \| 0.2279 \| 0.2007 \| \| No log \| 38.0 \| 266 \| 24.6423 \| 0.565 \| 0.6072 \| 2.6772 \| 0.565 \| 0.5340 \| 0.2587 \| 0.2247 \| \| No log \| 39.0 \| 273 \| 24.6706 \| 0.575 \| 0.6139 \| 2.9241 \| 0.575 \| 0.5291 \| 0.2318 \| 0.2416 \| \| No log \| 40.0 \| 280 \| 24.6007 \| 0.575 \| 0.5774 \| 2.9918 \| 0.575 \| 0.5323 \| 0.2575 \| 0.2138 \| \| No log \| 41.0 \| 287 \| 24.7587 \| 0.565 \| 0.6231 \| 2.9588 \| 0.565 \| 0.5023 \| 0.2685 \| 0.2665 \| \| No log \| 42.0 \| 294 \| 24.5681 \| 0.56 \| 0.5786 \| 2.9999 \| 0.56 \| 0.5153 \| 0.2558 \| 0.2093 \| \| No log \| 43.0 \| 301 \| 24.5971 \| 0.59 \| 0.5687 \| 3.0595 \| 0.59 \| 0.5365 \| 0.2532 \| 0.2004 \| \| No log \| 44.0 \| 308 \| 24.6424 \| 0.58 \| 0.5918 \| 2.9073 \| 0.58 \| 0.5432 \| 0.2470 \| 0.2113 \| \| No log \| 45.0 \| 315 \| 24.5998 \| 0.58 \| 0.5705 \| 3.0442 \| 0.58 \| 0.5488 \| 0.2769 \| 0.2011 \| \| No log \| 46.0 \| 322 \| 24.5625 \| 0.62 \| 0.5561 \| 2.9855 \| 0.62 \| 0.5869 \| 0.2492 \| 0.2069 \| \| No log \| 47.0 \| 329 \| 24.6409 \| 0.57 \| 0.5817 \| 2.8587 \| 0.57 \| 0.5400 \| 0.2480 \| 0.2239 \| \| No log \| 48.0 \| 336 \| 24.6218 \| 0.57 \| 0.5958 \| 2.8299 \| 0.57 \| 0.5426 \| 0.2725 \| 0.2251 \| \| No log \| 49.0 \| 343 \| 24.5568 \| 0.585 \| 0.5762 \| 2.9178 \| 0.585 \| 0.5590 \| 0.2374 \| 0.2102 \| \| No log \| 50.0 \| 350 \| 24.6607 \| 0.57 \| 0.6012 \| 2.9238 \| 0.57 \| 0.5344 \| 0.2496 \| 0.2274 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_simkd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 13.1229 - Accuracy: 0.295 - Brier Loss: 0.7636 - Nll: 6.8757 - F1 Micro: 0.295 - F1 Macro: 0.1150 - Ece: 0.2446 - Aurc: 0.4919 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 0.2512 \| 0.18 \| 0.9617 \| 7.0686 \| 0.18 \| 0.0305 \| 0.3439 \| 0.7810 \| \| No log \| 2.0 \| 14 \| 0.3629 \| 0.18 \| 1.0943 \| 7.0153 \| 0.18 \| 0.0305 \| 0.4345 \| 0.8186 \| \| No log \| 3.0 \| 21 \| 0.4745 \| 0.18 \| 1.1577 \| 6.9805 \| 0.18 \| 0.0305 \| 0.5034 \| 0.8029 \| \| No log \| 4.0 \| 28 \| 0.6953 \| 0.18 \| 1.1290 \| 6.9352 \| 0.18 \| 0.0305 \| 0.4731 \| 0.8367 \| \| No log \| 5.0 \| 35 \| 173.4450 \| 0.18 \| 1.1346 \| 6.8314 \| 0.18 \| 0.0305 \| 0.4615 \| 0.8814 \| \| No log \| 6.0 \| 42 \| 412.7549 \| 0.18 \| 1.1098 \| 6.8364 \| 0.18 \| 0.0305 \| 0.4420 \| 0.8716 \| \| No log \| 7.0 \| 49 \| 148.0839 \| 0.18 \| 1.0291 \| 6.9271 \| 0.18 \| 0.0305 \| 0.3960 \| 0.7698 \| \| No log \| 8.0 \| 56 \| 61.2696 \| 0.18 \| 0.9674 \| 6.9593 \| 0.18 \| 0.0305 \| 0.3413 \| 0.7924 \| \| No log \| 9.0 \| 63 \| 175.4512 \| 0.18 \| 0.9708 \| 6.9854 \| 0.18 \| 0.0305 \| 0.3549 \| 0.8252 \| \| No log \| 10.0 \| 70 \| 139.2036 \| 0.18 \| 0.9400 \| 6.9022 \| 0.18 \| 0.0305 \| 0.3300 \| 0.7760 \| \| No log \| 11.0 \| 77 \| 12.5605 \| 0.295 \| 0.8656 \| 6.9766 \| 0.295 \| 0.1138 \| 0.3093 \| 0.5354 \| \| No log \| 12.0 \| 84 \| 2.3147 \| 0.18 \| 0.9363 \| 6.9778 \| 0.18 \| 0.0305 \| 0.3084 \| 0.7507 \| \| No log \| 13.0 \| 91 \| 75.2050 \| 0.18 \| 0.9543 \| 9.1566 \| 0.18 \| 0.0305 \| 0.2990 \| 0.7716 \| \| No log \| 14.0 \| 98 \| 37.4873 \| 0.18 \| 0.9410 \| 9.1473 \| 0.18 \| 0.0305 \| 0.3029 \| 0.7517 \| \| No log \| 15.0 \| 105 \| 8.5750 \| 0.18 \| 0.9304 \| 9.1440 \| 0.18 \| 0.0305 \| 0.3033 \| 0.7718 \| \| No log \| 16.0 \| 112 \| 21.5310 \| 0.18 \| 0.9232 \| 9.1349 \| 0.18 \| 0.0305 \| 0.3122 \| 0.7717 \| \| No log \| 17.0 \| 119 \| 66.9546 \| 0.18 \| 0.9287 \| 9.1376 \| 0.18 \| 0.0305 \| 0.2920 \| 0.7715 \| \| No log \| 18.0 \| 126 \| 2.6525 \| 0.285 \| 0.8357 \| 7.0773 \| 0.285 \| 0.1143 \| 0.3156 \| 0.5306 \| \| No log \| 19.0 \| 133 \| 7.7253 \| 0.24 \| 0.8574 \| 7.0190 \| 0.24 \| 0.0880 \| 0.2948 \| 0.7186 \| \| No log \| 20.0 \| 140 \| 30.0305 \| 0.285 \| 0.8086 \| 6.9862 \| 0.285 \| 0.1133 \| 0.3001 \| 0.5273 \| \| No log \| 21.0 \| 147 \| 3.9243 \| 0.18 \| 0.8680 \| 7.4799 \| 0.18 \| 0.0306 \| 0.2739 \| 0.7704 \| \| No log \| 22.0 \| 154 \| 4.4660 \| 0.18 \| 0.8831 \| 8.9935 \| 0.18 \| 0.0308 \| 0.2652 \| 0.7313 \| \| No log \| 23.0 \| 161 \| 3.9728 \| 0.18 \| 0.8719 \| 8.9609 \| 0.18 \| 0.0308 \| 0.2600 \| 0.7651 \| \| No log \| 24.0 \| 168 \| 2.6913 \| 0.285 \| 0.8089 \| 6.9969 \| 0.285 \| 0.1146 \| 0.2873 \| 0.5122 \| \| No log \| 25.0 \| 175 \| 1.3141 \| 0.29 \| 0.8086 \| 7.0227 \| 0.29 \| 0.1156 \| 0.3154 \| 0.5256 \| \| No log \| 26.0 \| 182 \| 13.5853 \| 0.29 \| 0.7782 \| 6.8763 \| 0.29 \| 0.1168 \| 0.2735 \| 0.5045 \| \| No log \| 27.0 \| 189 \| 11.9763 \| 0.3 \| 0.7730 \| 6.8499 \| 0.3 \| 0.1171 \| 0.2740 \| 0.4971 \| \| No log \| 28.0 \| 196 \| 1.6467 \| 0.285 \| 0.8067 \| 7.1641 \| 0.285 \| 0.1144 \| 0.2870 \| 0.5193 \| \| No log \| 29.0 \| 203 \| 30.5306 \| 0.285 \| 0.8424 \| 7.1576 \| 0.285 \| 0.1129 \| 0.2686 \| 0.6662 \| \| No log \| 30.0 \| 210 \| 13.5964 \| 0.18 \| 0.8584 \| 7.0972 \| 0.18 \| 0.0305 \| 0.2704 \| 0.7307 \| \| No log \| 31.0 \| 217 \| 98.3061 \| 0.29 \| 0.8274 \| 7.0330 \| 0.29 \| 0.1167 \| 0.3163 \| 0.5653 \| \| No log \| 32.0 \| 224 \| 53.0911 \| 0.29 \| 0.7984 \| 6.9311 \| 0.29 \| 0.1167 \| 0.2911 \| 0.5181 \| \| No log \| 33.0 \| 231 \| 2.2010 \| 0.265 \| 0.8291 \| 6.9883 \| 0.265 \| 0.1037 \| 0.2945 \| 0.6039 \| \| No log \| 34.0 \| 238 \| 3.6255 \| 0.295 \| 0.7836 \| 6.8954 \| 0.295 \| 0.1176 \| 0.2636 \| 0.5025 \| \| No log \| 35.0 \| 245 \| 0.9640 \| 0.3 \| 0.7571 \| 6.7913 \| 0.3 \| 0.1170 \| 0.2388 \| 0.4746 \| \| No log \| 36.0 \| 252 \| 1.1935 \| 0.295 \| 0.7711 \| 6.7993 \| 0.295 \| 0.1175 \| 0.2619 \| 0.4779 \| \| No log \| 37.0 \| 259 \| 12.7465 \| 0.305 \| 0.7650 \| 6.8142 \| 0.305 \| 0.1205 \| 0.2512 \| 0.4798 \| \| No log \| 38.0 \| 266 \| 56.6876 \| 0.305 \| 0.7840 \| 6.8750 \| 0.305 \| 0.1205 \| 0.2835 \| 0.4985 \| \| No log \| 39.0 \| 273 \| 122.6602 \| 0.295 \| 0.7919 \| 6.9220 \| 0.295 \| 0.1116 \| 0.2493 \| 0.5312 \| \| No log \| 40.0 \| 280 \| 14.4685 \| 0.295 \| 0.7757 \| 6.8232 \| 0.295 \| 0.1162 \| 0.2575 \| 0.4988 \| \| No log \| 41.0 \| 287 \| 3.9605 \| 0.295 \| 0.7601 \| 6.7809 \| 0.295 \| 0.1138 \| 0.2437 \| 0.4911 \| \| No log \| 42.0 \| 294 \| 7.9424 \| 0.295 \| 0.7567 \| 6.7609 \| 0.295 \| 0.1138 \| 0.2398 \| 0.4883 \| \| No log \| 43.0 \| 301 \| 17.7810 \| 0.295 \| 0.7713 \| 6.8075 \| 0.295 \| 0.1175 \| 0.2503 \| 0.5090 \| \| No log \| 44.0 \| 308 \| 30.8773 \| 0.295 \| 0.7747 \| 6.8248 \| 0.295 \| 0.1127 \| 0.2651 \| 0.5149 \| \| No log \| 45.0 \| 315 \| 16.3877 \| 0.29 \| 0.7736 \| 6.8888 \| 0.29 \| 0.1117 \| 0.2641 \| 0.5026 \| \| No log \| 46.0 \| 322 \| 7.4195 \| 0.29 \| 0.7674 \| 6.8179 \| 0.29 \| 0.1117 \| 0.2621 \| 0.4991 \| \| No log \| 47.0 \| 329 \| 9.6560 \| 0.295 \| 0.7694 \| 6.8960 \| 0.295 \| 0.1138 \| 0.2604 \| 0.4963 \| \| No log \| 48.0 \| 336 \| 6.6040 \| 0.29 \| 0.7622 \| 6.7835 \| 0.29 \| 0.1117 \| 0.2271 \| 0.4958 \| \| No log \| 49.0 \| 343 \| 10.3365 \| 0.29 \| 0.7640 \| 6.8293 \| 0.29 \| 0.1117 \| 0.2583 \| 0.4941 \| \| No log \| 50.0 \| 350 \| 13.1229 \| 0.295 \| 0.7636 \| 6.8757 \| 0.295 \| 0.1150 \| 0.2446 \| 0.4919 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/resnet101-base_tobacco-cnn_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet101-base_tobacco-cnn_tobacco3482_og_simkd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1263 - Accuracy: 0.295 - Brier Loss: 0.7485 - Nll: 6.2362 - F1 Micro: 0.295 - F1 Macro: 0.1126 - Ece: 0.2177 - Aurc: 0.4648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 0.1887 \| 0.18 \| 0.8839 \| 8.4886 \| 0.18 \| 0.0305 \| 0.2321 \| 0.8333 \| \| No log \| 2.0 \| 14 \| 0.1901 \| 0.18 \| 0.8775 \| 6.8406 \| 0.18 \| 0.0305 \| 0.2593 \| 0.8187 \| \| No log \| 3.0 \| 21 \| 0.2085 \| 0.28 \| 0.9045 \| 7.0658 \| 0.28 \| 0.1005 \| 0.3168 \| 0.6083 \| \| No log \| 4.0 \| 28 \| 0.2493 \| 0.155 \| 0.9936 \| 8.0153 \| 0.155 \| 0.0300 \| 0.3996 \| 0.6329 \| \| No log \| 5.0 \| 35 \| 0.2952 \| 0.16 \| 1.0253 \| 8.3946 \| 0.16 \| 0.0355 \| 0.4181 \| 0.6026 \| \| No log \| 6.0 \| 42 \| 0.2344 \| 0.195 \| 0.9022 \| 7.0548 \| 0.195 \| 0.0485 \| 0.3118 \| 0.8445 \| \| No log \| 7.0 \| 49 \| 0.1665 \| 0.18 \| 0.9004 \| 6.7765 \| 0.18 \| 0.0310 \| 0.2888 \| 0.7617 \| \| No log \| 8.0 \| 56 \| 0.1696 \| 0.18 \| 0.9279 \| 9.0648 \| 0.18 \| 0.0309 \| 0.3021 \| 0.7025 \| \| No log \| 9.0 \| 63 \| 0.1715 \| 0.18 \| 0.9330 \| 9.0774 \| 0.18 \| 0.0305 \| 0.2992 \| 0.7525 \| \| No log \| 10.0 \| 70 \| 0.1369 \| 0.285 \| 0.8092 \| 6.9372 \| 0.285 \| 0.1134 \| 0.2993 \| 0.4899 \| \| No log \| 11.0 \| 77 \| 0.1584 \| 0.18 \| 0.8953 \| 8.9899 \| 0.18 \| 0.0310 \| 0.2666 \| 0.7495 \| \| No log \| 12.0 \| 84 \| 0.1690 \| 0.18 \| 0.8896 \| 8.9605 \| 0.18 \| 0.0310 \| 0.2593 \| 0.7452 \| \| No log \| 13.0 \| 91 \| 0.1636 \| 0.18 \| 0.8848 \| 8.9907 \| 0.18 \| 0.0310 \| 0.2661 \| 0.7474 \| \| No log \| 14.0 \| 98 \| 0.1685 \| 0.18 \| 0.8815 \| 8.9991 \| 0.18 \| 0.0309 \| 0.2676 \| 0.7750 \| \| No log \| 15.0 \| 105 \| 0.1678 \| 0.18 \| 0.8807 \| 8.9352 \| 0.18 \| 0.0305 \| 0.2658 \| 0.7448 \| \| No log \| 16.0 \| 112 \| 0.1599 \| 0.18 \| 0.8848 \| 9.0210 \| 0.18 \| 0.0309 \| 0.2707 \| 0.7742 \| \| No log \| 17.0 \| 119 \| 0.1553 \| 0.18 \| 0.8559 \| 7.2132 \| 0.18 \| 0.0305 \| 0.2569 \| 0.7479 \| \| No log \| 18.0 \| 126 \| 0.1620 \| 0.18 \| 0.8728 \| 8.8826 \| 0.18 \| 0.0308 \| 0.2472 \| 0.7289 \| \| No log \| 19.0 \| 133 \| 0.1631 \| 0.18 \| 0.8600 \| 8.8681 \| 0.18 \| 0.0305 \| 0.2689 \| 0.7046 \| \| No log \| 20.0 \| 140 \| 0.1616 \| 0.18 \| 0.8702 \| 8.8768 \| 0.18 \| 0.0305 \| 0.2532 \| 0.7489 \| \| No log \| 21.0 \| 147 \| 0.1521 \| 0.18 \| 0.8505 \| 6.9939 \| 0.18 \| 0.0310 \| 0.2687 \| 0.7479 \| \| No log \| 22.0 \| 154 \| 0.1290 \| 0.285 \| 0.7742 \| 6.8763 \| 0.285 \| 0.1123 \| 0.2907 \| 0.4899 \| \| No log \| 23.0 \| 161 \| 0.1256 \| 0.305 \| 0.7453 \| 6.2659 \| 0.305 \| 0.1190 \| 0.2133 \| 0.4457 \| \| No log \| 24.0 \| 168 \| 0.1257 \| 0.305 \| 0.7527 \| 6.7983 \| 0.305 \| 0.1192 \| 0.2483 \| 0.4694 \| \| No log \| 25.0 \| 175 \| 0.1256 \| 0.295 \| 0.7483 \| 6.7540 \| 0.295 \| 0.1106 \| 0.2233 \| 0.4632 \| \| No log \| 26.0 \| 182 \| 0.1277 \| 0.3 \| 0.7590 \| 6.6632 \| 0.3 \| 0.1214 \| 0.2641 \| 0.4563 \| \| No log \| 27.0 \| 189 \| 0.1644 \| 0.18 \| 0.8539 \| 8.7216 \| 0.18 \| 0.0306 \| 0.2483 \| 0.7170 \| \| No log \| 28.0 \| 196 \| 0.1268 \| 0.305 \| 0.7494 \| 6.5633 \| 0.305 \| 0.1146 \| 0.2379 \| 0.4509 \| \| No log \| 29.0 \| 203 \| 0.1246 \| 0.305 \| 0.7376 \| 6.2718 \| 0.305 \| 0.1158 \| 0.2319 \| 0.4326 \| \| No log \| 30.0 \| 210 \| 0.1249 \| 0.3 \| 0.7428 \| 6.5246 \| 0.3 \| 0.1138 \| 0.2463 \| 0.4449 \| \| No log \| 31.0 \| 217 \| 0.1284 \| 0.295 \| 0.7474 \| 6.4668 \| 0.295 \| 0.1116 \| 0.2566 \| 0.4550 \| \| No log \| 32.0 \| 224 \| 0.1715 \| 0.18 \| 0.8599 \| 8.5902 \| 0.18 \| 0.0310 \| 0.2413 \| 0.7447 \| \| No log \| 33.0 \| 231 \| 0.1566 \| 0.18 \| 0.8495 \| 7.4352 \| 0.18 \| 0.0308 \| 0.2624 \| 0.7110 \| \| No log \| 34.0 \| 238 \| 0.1370 \| 0.275 \| 0.7990 \| 6.5052 \| 0.275 \| 0.1096 \| 0.2760 \| 0.5186 \| \| No log \| 35.0 \| 245 \| 0.1289 \| 0.3 \| 0.7569 \| 6.4685 \| 0.3 \| 0.1212 \| 0.2643 \| 0.4524 \| \| No log \| 36.0 \| 252 \| 0.1557 \| 0.18 \| 0.8493 \| 6.8218 \| 0.18 \| 0.0305 \| 0.2574 \| 0.7401 \| \| No log \| 37.0 \| 259 \| 0.1629 \| 0.18 \| 0.8558 \| 8.5068 \| 0.18 \| 0.0310 \| 0.2522 \| 0.7466 \| \| No log \| 38.0 \| 266 \| 0.1386 \| 0.275 \| 0.8117 \| 6.4244 \| 0.275 \| 0.1053 \| 0.2455 \| 0.5912 \| \| No log \| 39.0 \| 273 \| 0.1601 \| 0.18 \| 0.8508 \| 8.3697 \| 0.18 \| 0.0305 \| 0.2445 \| 0.7048 \| \| No log \| 40.0 \| 280 \| 0.1510 \| 0.185 \| 0.8428 \| 6.8710 \| 0.185 \| 0.0369 \| 0.2517 \| 0.7155 \| \| No log \| 41.0 \| 287 \| 0.1315 \| 0.29 \| 0.7675 \| 6.1897 \| 0.29 \| 0.1167 \| 0.2708 \| 0.4594 \| \| No log \| 42.0 \| 294 \| 0.1235 \| 0.3 \| 0.7405 \| 6.1762 \| 0.3 \| 0.1158 \| 0.2338 \| 0.4496 \| \| No log \| 43.0 \| 301 \| 0.1250 \| 0.295 \| 0.7456 \| 6.3789 \| 0.295 \| 0.1174 \| 0.2524 \| 0.4548 \| \| No log \| 44.0 \| 308 \| 0.1249 \| 0.285 \| 0.7440 \| 6.3862 \| 0.285 \| 0.1097 \| 0.2405 \| 0.4680 \| \| No log \| 45.0 \| 315 \| 0.1245 \| 0.29 \| 0.7428 \| 6.4641 \| 0.29 \| 0.1117 \| 0.2403 \| 0.4623 \| \| No log \| 46.0 \| 322 \| 0.1245 \| 0.295 \| 0.7440 \| 6.5208 \| 0.295 \| 0.1149 \| 0.2385 \| 0.4610 \| \| No log \| 47.0 \| 329 \| 0.1250 \| 0.29 \| 0.7464 \| 6.2221 \| 0.29 \| 0.1117 \| 0.2332 \| 0.4674 \| \| No log \| 48.0 \| 336 \| 0.1263 \| 0.295 \| 0.7458 \| 6.3085 \| 0.295 \| 0.1126 \| 0.2375 \| 0.4670 \| \| No log \| 49.0 \| 343 \| 0.1252 \| 0.29 \| 0.7469 \| 6.0647 \| 0.29 \| 0.1117 \| 0.2410 \| 0.4679 \| \| No log \| 50.0 \| 350 \| 0.1263 \| 0.295 \| 0.7485 \| 6.2362 \| 0.295 \| 0.1126 \| 0.2177 \| 0.4648 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
dima806/smoker_image_classification	Returns whether the person is a smoker based on image with about 97% accuracy. See https://www.kaggle.com/code/dima806/smoker-image-detection-vit for more details. ``` Classification report: precision recall f1-score support notsmoking 0.9907 0.9464 0.9680 112 smoking 0.9487 0.9911 0.9694 112 accuracy 0.9688 224 macro avg 0.9697 0.9688 0.9687 224 weighted avg 0.9697 0.9688 0.9687 224 ```	[ "notsmoking", "smoking" ]
cjade100/vit-base-patch16-224-finetuned-flower	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
dima806/mammals_45_types_image_classification	Returns a common mammal type given an image with about 96% accuracy. See https://www.kaggle.com/code/dima806/mammals-45-types-image-classification-vit for more details. ``` Classification report: precision recall f1-score support african_elephant 1.0000 1.0000 1.0000 71 alpaca 0.9200 0.9718 0.9452 71 american_bison 1.0000 1.0000 1.0000 71 anteater 0.9853 0.9437 0.9640 71 arctic_fox 0.9286 0.9155 0.9220 71 armadillo 0.9726 1.0000 0.9861 71 baboon 0.9718 0.9718 0.9718 71 badger 1.0000 0.9718 0.9857 71 blue_whale 0.9710 0.9437 0.9571 71 brown_bear 0.9722 0.9859 0.9790 71 camel 0.9861 1.0000 0.9930 71 dolphin 0.8974 0.9859 0.9396 71 giraffe 0.9857 0.9718 0.9787 71 groundhog 0.9714 0.9577 0.9645 71 highland_cattle 0.9859 0.9859 0.9859 71 horse 1.0000 0.9859 0.9929 71 jackal 0.9577 0.9444 0.9510 72 kangaroo 0.8415 0.9583 0.8961 72 koala 0.9589 0.9859 0.9722 71 manatee 0.9861 0.9861 0.9861 72 mongoose 0.9483 0.7746 0.8527 71 mountain_goat 0.9855 0.9577 0.9714 71 opossum 1.0000 0.9577 0.9784 71 orangutan 1.0000 1.0000 1.0000 71 otter 1.0000 0.9577 0.9784 71 polar_bear 0.9706 0.9296 0.9496 71 porcupine 1.0000 0.9722 0.9859 72 red_panda 0.9718 0.9718 0.9718 71 rhinoceros 0.9859 0.9859 0.9859 71 sea_lion 0.7600 0.8028 0.7808 71 seal 0.8308 0.7500 0.7883 72 snow_leopard 1.0000 1.0000 1.0000 71 squirrel 0.9444 0.9577 0.9510 71 sugar_glider 0.8554 1.0000 0.9221 71 tapir 1.0000 1.0000 1.0000 71 vampire_bat 1.0000 0.9861 0.9930 72 vicuna 1.0000 0.8873 0.9403 71 walrus 0.9342 0.9861 0.9595 72 warthog 0.9571 0.9437 0.9504 71 water_buffalo 0.9333 0.9859 0.9589 71 weasel 0.9583 0.9583 0.9583 72 wildebeest 0.9577 0.9444 0.9510 72 wombat 0.8947 0.9577 0.9252 71 yak 1.0000 0.9437 0.9710 71 zebra 0.9595 1.0000 0.9793 71 accuracy 0.9572 3204 macro avg 0.9587 0.9573 0.9572 3204 weighted avg 0.9586 0.9572 0.9572 3204 ```	[ "african_elephant", "alpaca", "american_bison", "anteater", "arctic_fox", "armadillo", "baboon", "badger", "blue_whale", "brown_bear", "camel", "dolphin", "giraffe", "groundhog", "highland_cattle", "horse", "jackal", "kangaroo", "koala", "manatee", "mongoose", "mountain_goat", "opossum", "orangutan", "otter", "polar_bear", "porcupine", "red_panda", "rhinoceros", "sea_lion", "seal", "snow_leopard", "squirrel", "sugar_glider", "tapir", "vampire_bat", "vicuna", "walrus", "warthog", "water_buffalo", "weasel", "wildebeest", "wombat", "yak", "zebra" ]
phuong-tk-nguyen/resnet-50-finetuned-cifar10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-cifar10 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.9060 - Accuracy: 0.5076 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.3058 \| 0.03 \| 10 \| 2.3106 \| 0.0794 \| \| 2.3033 \| 0.06 \| 20 \| 2.3026 \| 0.0892 \| \| 2.3012 \| 0.09 \| 30 \| 2.2971 \| 0.1042 \| \| 2.2914 \| 0.11 \| 40 \| 2.2890 \| 0.1254 \| \| 2.2869 \| 0.14 \| 50 \| 2.2816 \| 0.16 \| \| 2.2785 \| 0.17 \| 60 \| 2.2700 \| 0.1902 \| \| 2.2712 \| 0.2 \| 70 \| 2.2602 \| 0.2354 \| \| 2.2619 \| 0.23 \| 80 \| 2.2501 \| 0.2688 \| \| 2.2509 \| 0.26 \| 90 \| 2.2383 \| 0.3022 \| \| 2.2382 \| 0.28 \| 100 \| 2.2229 \| 0.3268 \| \| 2.2255 \| 0.31 \| 110 \| 2.2084 \| 0.353 \| \| 2.2164 \| 0.34 \| 120 \| 2.1939 \| 0.3608 \| \| 2.2028 \| 0.37 \| 130 \| 2.1829 \| 0.3668 \| \| 2.1977 \| 0.4 \| 140 \| 2.1646 \| 0.401 \| \| 2.1844 \| 0.43 \| 150 \| 2.1441 \| 0.4244 \| \| 2.1689 \| 0.45 \| 160 \| 2.1323 \| 0.437 \| \| 2.1555 \| 0.48 \| 170 \| 2.1159 \| 0.4462 \| \| 2.1448 \| 0.51 \| 180 \| 2.0992 \| 0.45 \| \| 2.1313 \| 0.54 \| 190 \| 2.0810 \| 0.4642 \| \| 2.1189 \| 0.57 \| 200 \| 2.0589 \| 0.4708 \| \| 2.1111 \| 0.6 \| 210 \| 2.0430 \| 0.4828 \| \| 2.0905 \| 0.63 \| 220 \| 2.0288 \| 0.4938 \| \| 2.082 \| 0.65 \| 230 \| 2.0089 \| 0.4938 \| \| 2.0646 \| 0.68 \| 240 \| 1.9970 \| 0.5014 \| \| 2.0636 \| 0.71 \| 250 \| 1.9778 \| 0.4946 \| \| 2.0579 \| 0.74 \| 260 \| 1.9609 \| 0.49 \| \| 2.028 \| 0.77 \| 270 \| 1.9602 \| 0.4862 \| \| 2.0447 \| 0.8 \| 280 \| 1.9460 \| 0.4934 \| \| 2.0168 \| 0.82 \| 290 \| 1.9369 \| 0.505 \| \| 2.0126 \| 0.85 \| 300 \| 1.9317 \| 0.4926 \| \| 2.0099 \| 0.88 \| 310 \| 1.9235 \| 0.4952 \| \| 1.9978 \| 0.91 \| 320 \| 1.9174 \| 0.4972 \| \| 1.9951 \| 0.94 \| 330 \| 1.9119 \| 0.507 \| \| 1.9823 \| 0.97 \| 340 \| 1.9120 \| 0.4992 \| \| 1.985 \| 1.0 \| 350 \| 1.9064 \| 0.5022 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
PK-B/roof_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # PK-B/roof_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6844 - Validation Loss: 2.3315 - Train Accuracy: 0.425 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1770, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.9736 \| 2.9756 \| 0.05 \| 0 \| \| 2.9016 \| 2.9430 \| 0.1 \| 1 \| \| 2.8192 \| 2.9084 \| 0.1 \| 2 \| \| 2.7004 \| 2.8564 \| 0.175 \| 3 \| \| 2.6005 \| 2.8109 \| 0.175 \| 4 \| \| 2.4981 \| 2.7452 \| 0.225 \| 5 \| \| 2.3819 \| 2.6988 \| 0.2125 \| 6 \| \| 2.2867 \| 2.6998 \| 0.25 \| 7 \| \| 2.1804 \| 2.6510 \| 0.275 \| 8 \| \| 2.1115 \| 2.5307 \| 0.3375 \| 9 \| \| 2.0161 \| 2.5523 \| 0.3 \| 10 \| \| 1.9189 \| 2.5310 \| 0.2875 \| 11 \| \| 1.8863 \| 2.4733 \| 0.3375 \| 12 \| \| 1.7518 \| 2.4233 \| 0.3625 \| 13 \| \| 1.6844 \| 2.3315 \| 0.425 \| 14 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "atlas_pinnacle_wwood_shade", "atlas_pinnacle_wwood_sun", "gaf_wwood_shade", "gaf_wwood_sun", "iko_cornerstone_shade", "iko_cornerstone_sun", "malarkey_wwood_shade", "malarkey_wwood_sun", "oc_driftwood_shade", "oc_driftwood_sun", "tamko_wwood_shade", "tamko_wwood_sun", "cteed_maxdef_wwood_shade", "cteed_maxdef_wwood_sun", "cteed_wwood_shade", "cteed_wwood_sun", "gaf_mission_brown_shade", "gaf_mission_brown_sun", "gaf_pewter_gray_shade", "gaf_pewter_gray_sun" ]
andakm/swin-tiny-patch4-window7-224	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3635 - Accuracy: 0.5294 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 1.7560 \| 0.3137 \| \| No log \| 1.87 \| 7 \| 1.6225 \| 0.3725 \| \| 1.7919 \| 2.93 \| 11 \| 1.5661 \| 0.4510 \| \| 1.7919 \| 4.0 \| 15 \| 1.5332 \| 0.4510 \| \| 1.7919 \| 4.8 \| 18 \| 1.4522 \| 0.5294 \| \| 1.5187 \| 5.87 \| 22 \| 1.3873 \| 0.4902 \| \| 1.5187 \| 6.93 \| 26 \| 1.3741 \| 0.4902 \| \| 1.2773 \| 8.0 \| 30 \| 1.3635 \| 0.5294 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "1-series", "3-series", "4-series", "5-series", "6-series", "7-series", "8-series", "m3", "m4", "m5" ]
phuong-tk-nguyen/vit-base-patch16-224-finetuned-cifar10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0564 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.4597 \| 0.03 \| 10 \| 2.2902 \| 0.1662 \| \| 2.1429 \| 0.06 \| 20 \| 1.7855 \| 0.5086 \| \| 1.6466 \| 0.09 \| 30 \| 1.0829 \| 0.8484 \| \| 0.9962 \| 0.11 \| 40 \| 0.4978 \| 0.9288 \| \| 0.6127 \| 0.14 \| 50 \| 0.2717 \| 0.9508 \| \| 0.4544 \| 0.17 \| 60 \| 0.1942 \| 0.9588 \| \| 0.4352 \| 0.2 \| 70 \| 0.1504 \| 0.9672 \| \| 0.374 \| 0.23 \| 80 \| 0.1221 \| 0.9718 \| \| 0.3261 \| 0.26 \| 90 \| 0.1057 \| 0.9772 \| \| 0.34 \| 0.28 \| 100 \| 0.0943 \| 0.979 \| \| 0.284 \| 0.31 \| 110 \| 0.0958 \| 0.9754 \| \| 0.3151 \| 0.34 \| 120 \| 0.0866 \| 0.9776 \| \| 0.3004 \| 0.37 \| 130 \| 0.0838 \| 0.9788 \| \| 0.3334 \| 0.4 \| 140 \| 0.0798 \| 0.9806 \| \| 0.3018 \| 0.43 \| 150 \| 0.0800 \| 0.9778 \| \| 0.2957 \| 0.45 \| 160 \| 0.0749 \| 0.9808 \| \| 0.2952 \| 0.48 \| 170 \| 0.0704 \| 0.9814 \| \| 0.3084 \| 0.51 \| 180 \| 0.0720 \| 0.9812 \| \| 0.3015 \| 0.54 \| 190 \| 0.0708 \| 0.983 \| \| 0.2763 \| 0.57 \| 200 \| 0.0672 \| 0.9832 \| \| 0.3376 \| 0.6 \| 210 \| 0.0700 \| 0.982 \| \| 0.285 \| 0.63 \| 220 \| 0.0657 \| 0.9828 \| \| 0.2857 \| 0.65 \| 230 \| 0.0629 \| 0.9836 \| \| 0.2644 \| 0.68 \| 240 \| 0.0612 \| 0.9842 \| \| 0.2461 \| 0.71 \| 250 \| 0.0601 \| 0.9836 \| \| 0.2802 \| 0.74 \| 260 \| 0.0589 \| 0.9842 \| \| 0.2481 \| 0.77 \| 270 \| 0.0604 \| 0.9838 \| \| 0.2641 \| 0.8 \| 280 \| 0.0591 \| 0.9846 \| \| 0.2737 \| 0.82 \| 290 \| 0.0581 \| 0.9842 \| \| 0.2391 \| 0.85 \| 300 \| 0.0565 \| 0.9852 \| \| 0.2283 \| 0.88 \| 310 \| 0.0558 \| 0.986 \| \| 0.2626 \| 0.91 \| 320 \| 0.0559 \| 0.9852 \| \| 0.2325 \| 0.94 \| 330 \| 0.0563 \| 0.9846 \| \| 0.2459 \| 0.97 \| 340 \| 0.0565 \| 0.9846 \| \| 0.2474 \| 1.0 \| 350 \| 0.0564 \| 0.9844 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
HarshaSingamshetty1/roof_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # HarshaSingamshetty1/roof_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6380 - Validation Loss: 2.1987 - Train Accuracy: 0.4375 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1770, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.9764 \| 2.9730 \| 0.05 \| 0 \| \| 2.8746 \| 2.9232 \| 0.125 \| 1 \| \| 2.7792 \| 2.8802 \| 0.1375 \| 2 \| \| 2.6648 \| 2.8491 \| 0.225 \| 3 \| \| 2.5573 \| 2.7563 \| 0.1625 \| 4 \| \| 2.4614 \| 2.7155 \| 0.2875 \| 5 \| \| 2.3453 \| 2.7005 \| 0.2 \| 6 \| \| 2.2737 \| 2.6443 \| 0.2875 \| 7 \| \| 2.1555 \| 2.5396 \| 0.3625 \| 8 \| \| 2.0694 \| 2.4244 \| 0.425 \| 9 \| \| 2.0112 \| 2.3738 \| 0.4875 \| 10 \| \| 1.8867 \| 2.3843 \| 0.4125 \| 11 \| \| 1.8217 \| 2.2878 \| 0.45 \| 12 \| \| 1.7253 \| 2.2642 \| 0.475 \| 13 \| \| 1.6380 \| 2.1987 \| 0.4375 \| 14 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "atlas_pinnacle_wwood_shade", "atlas_pinnacle_wwood_sun", "gaf_wwood_shade", "gaf_wwood_sun", "iko_cornerstone_shade", "iko_cornerstone_sun", "malarkey_wwood_shade", "malarkey_wwood_sun", "oc_driftwood_shade", "oc_driftwood_sun", "tamko_wwood_shade", "tamko_wwood_sun", "cteed_maxdef_wwood_shade", "cteed_maxdef_wwood_sun", "cteed_wwood_shade", "cteed_wwood_sun", "gaf_mission_brown_shade", "gaf_mission_brown_sun", "gaf_pewter_gray_shade", "gaf_pewter_gray_sun" ]
Iust1n2/resnet-18-finetuned-wikiart	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-18-finetuned-wikiart This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6256 - Accuracy: 0.6247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0382 \| 1.0 \| 2037 \| 1.8771 \| 0.5938 \| \| 1.8027 \| 2.0 \| 4074 \| 1.6860 \| 0.6160 \| \| 1.7033 \| 3.0 \| 6111 \| 1.6256 \| 0.6247 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unknown artist", "boris-kustodiev", "camille-pissarro", "childe-hassam", "claude-monet", "edgar-degas", "eugene-boudin", "gustave-dore", "ilya-repin", "ivan-aivazovsky", "ivan-shishkin", "john-singer-sargent", "marc-chagall", "martiros-saryan", "nicholas-roerich", "pablo-picasso", "paul-cezanne", "pierre-auguste-renoir", "pyotr-konchalovsky", "raphael-kirchner", "rembrandt", "salvador-dali", "vincent-van-gogh", "hieronymus-bosch", "leonardo-da-vinci", "albrecht-durer", "edouard-cortes", "sam-francis", "juan-gris", "lucas-cranach-the-elder", "paul-gauguin", "konstantin-makovsky", "egon-schiele", "thomas-eakins", "gustave-moreau", "francisco-goya", "edvard-munch", "henri-matisse", "fra-angelico", "maxime-maufra", "jan-matejko", "mstislav-dobuzhinsky", "alfred-sisley", "mary-cassatt", "gustave-loiseau", "fernando-botero", "zinaida-serebriakova", "georges-seurat", "isaac-levitan", "joaquãn-sorolla", "jacek-malczewski", "berthe-morisot", "andy-warhol", "arkhip-kuindzhi", "niko-pirosmani", "james-tissot", "vasily-polenov", "valentin-serov", "pietro-perugino", "pierre-bonnard", "ferdinand-hodler", "bartolome-esteban-murillo", "giovanni-boldini", "henri-martin", "gustav-klimt", "vasily-perov", "odilon-redon", "tintoretto", "gene-davis", "raphael", "john-henry-twachtman", "henri-de-toulouse-lautrec", "antoine-blanchard", "david-burliuk", "camille-corot", "konstantin-korovin", "ivan-bilibin", "titian", "maurice-prendergast", "edouard-manet", "peter-paul-rubens", "aubrey-beardsley", "paolo-veronese", "joshua-reynolds", "kuzma-petrov-vodkin", "gustave-caillebotte", "lucian-freud", "michelangelo", "dante-gabriel-rossetti", "felix-vallotton", "nikolay-bogdanov-belsky", "georges-braque", "vasily-surikov", "fernand-leger", "konstantin-somov", "katsushika-hokusai", "sir-lawrence-alma-tadema", "vasily-vereshchagin", "ernst-ludwig-kirchner", "mikhail-vrubel", "orest-kiprensky", "william-merritt-chase", "aleksey-savrasov", "hans-memling", "amedeo-modigliani", "ivan-kramskoy", "utagawa-kuniyoshi", "gustave-courbet", "william-turner", "theo-van-rysselberghe", "joseph-wright", "edward-burne-jones", "koloman-moser", "viktor-vasnetsov", "anthony-van-dyck", "raoul-dufy", "frans-hals", "hans-holbein-the-younger", "ilya-mashkov", "henri-fantin-latour", "m.c.-escher", "el-greco", "mikalojus-ciurlionis", "james-mcneill-whistler", "karl-bryullov", "jacob-jordaens", "thomas-gainsborough", "eugene-delacroix", "canaletto" ]
fashxp/car_manufacturer_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # car_manufacturer_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 2.7826 - Accuracy: 0.3394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 7 \| 3.1387 \| 0.2018 \| \| 2.8998 \| 2.0 \| 14 \| 3.1029 \| 0.2018 \| \| 2.7326 \| 3.0 \| 21 \| 3.0453 \| 0.2294 \| \| 2.7326 \| 4.0 \| 28 \| 3.0104 \| 0.2385 \| \| 2.5797 \| 5.0 \| 35 \| 2.9655 \| 0.2477 \| \| 2.4873 \| 6.0 \| 42 \| 2.9166 \| 0.3211 \| \| 2.4873 \| 7.0 \| 49 \| 2.9122 \| 0.2569 \| \| 2.3408 \| 8.0 \| 56 \| 2.8122 \| 0.3119 \| \| 2.2696 \| 9.0 \| 63 \| 2.8159 \| 0.3578 \| \| 2.1527 \| 10.0 \| 70 \| 2.8589 \| 0.2752 \| \| 2.1527 \| 11.0 \| 77 \| 2.8248 \| 0.2936 \| \| 2.0649 \| 12.0 \| 84 \| 2.7709 \| 0.2936 \| \| 2.0855 \| 13.0 \| 91 \| 2.8183 \| 0.2477 \| \| 2.0855 \| 14.0 \| 98 \| 2.7552 \| 0.2569 \| \| 1.9347 \| 15.0 \| 105 \| 2.7826 \| 0.3394 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "ac cars", "alfa romeo", "chrysler", "citroen", "datsun", "dodge", "ferrari", "fiat", "ford", "jaguar", "lamborghini", "lincoln", "amc", "mazda", "mercedes", "mercury", "mga", "morris", "oldsmobile", "opel", "peugeot", "plymouth", "pontiac", "aston martin", "porsche", "renault", "saab", "toyota", "trabant", "triumph", "volkswagen", "volvo", "audi", "austin healey", "bmw", "buick", "cadillac", "chevrolet" ]
anirudhmu/swin-tiny-patch4-window7-224-finetuned-soccer-binary2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-soccer-binary2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1078 - Accuracy: 0.9719 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4085 \| 0.99 \| 20 \| 0.1740 \| 0.9544 \| \| 0.1281 \| 1.98 \| 40 \| 0.1078 \| 0.9719 \| \| 0.108 \| 2.96 \| 60 \| 0.0978 \| 0.9684 \| \| 0.1077 \| 4.0 \| 81 \| 0.1006 \| 0.9684 \| \| 0.0916 \| 4.99 \| 101 \| 0.0954 \| 0.9649 \| \| 0.0824 \| 5.98 \| 121 \| 0.0935 \| 0.9684 \| \| 0.0859 \| 6.96 \| 141 \| 0.0975 \| 0.9684 \| \| 0.0927 \| 8.0 \| 162 \| 0.0949 \| 0.9684 \| \| 0.0836 \| 8.99 \| 182 \| 0.0928 \| 0.9684 \| \| 0.0958 \| 9.88 \| 200 \| 0.0940 \| 0.9684 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "closeup", "overview", "logo" ]
phuong-tk-nguyen/swin-base-patch4-window7-224-in22k-finetuned-cifar10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-base-patch4-window7-224-in22k-finetuned-cifar10 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0414 - Accuracy: 0.9858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.303 \| 0.03 \| 10 \| 2.1672 \| 0.2334 \| \| 2.0158 \| 0.06 \| 20 \| 1.6672 \| 0.657 \| \| 1.4855 \| 0.09 \| 30 \| 0.8292 \| 0.8704 \| \| 0.7451 \| 0.11 \| 40 \| 0.2578 \| 0.93 \| \| 0.5618 \| 0.14 \| 50 \| 0.1476 \| 0.962 \| \| 0.4545 \| 0.17 \| 60 \| 0.1248 \| 0.9642 \| \| 0.4587 \| 0.2 \| 70 \| 0.0941 \| 0.9748 \| \| 0.3911 \| 0.23 \| 80 \| 0.0944 \| 0.9712 \| \| 0.3839 \| 0.26 \| 90 \| 0.0848 \| 0.9756 \| \| 0.3864 \| 0.28 \| 100 \| 0.0744 \| 0.978 \| \| 0.3141 \| 0.31 \| 110 \| 0.0673 \| 0.98 \| \| 0.3764 \| 0.34 \| 120 \| 0.0706 \| 0.9764 \| \| 0.3003 \| 0.37 \| 130 \| 0.0600 \| 0.984 \| \| 0.3566 \| 0.4 \| 140 \| 0.0562 \| 0.9826 \| \| 0.2855 \| 0.43 \| 150 \| 0.0567 \| 0.9816 \| \| 0.3351 \| 0.45 \| 160 \| 0.0543 \| 0.9828 \| \| 0.2977 \| 0.48 \| 170 \| 0.0568 \| 0.9798 \| \| 0.2924 \| 0.51 \| 180 \| 0.0577 \| 0.9804 \| \| 0.2884 \| 0.54 \| 190 \| 0.0551 \| 0.983 \| \| 0.3067 \| 0.57 \| 200 \| 0.0487 \| 0.983 \| \| 0.3159 \| 0.6 \| 210 \| 0.0513 \| 0.984 \| \| 0.2795 \| 0.63 \| 220 \| 0.0460 \| 0.9846 \| \| 0.3113 \| 0.65 \| 230 \| 0.0495 \| 0.9832 \| \| 0.2882 \| 0.68 \| 240 \| 0.0475 \| 0.9838 \| \| 0.263 \| 0.71 \| 250 \| 0.0449 \| 0.9854 \| \| 0.2686 \| 0.74 \| 260 \| 0.0510 \| 0.9826 \| \| 0.2705 \| 0.77 \| 270 \| 0.0483 \| 0.9846 \| \| 0.2807 \| 0.8 \| 280 \| 0.0430 \| 0.9854 \| \| 0.2583 \| 0.82 \| 290 \| 0.0452 \| 0.9858 \| \| 0.2346 \| 0.85 \| 300 \| 0.0435 \| 0.9858 \| \| 0.2294 \| 0.88 \| 310 \| 0.0434 \| 0.986 \| \| 0.2608 \| 0.91 \| 320 \| 0.0433 \| 0.986 \| \| 0.2642 \| 0.94 \| 330 \| 0.0425 \| 0.9866 \| \| 0.2781 \| 0.97 \| 340 \| 0.0417 \| 0.986 \| \| 0.247 \| 1.0 \| 350 \| 0.0414 \| 0.9858 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
edwinpalegre/vit-base-trashnet-demo	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-trashnet-demo This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the edwinpalegre/trashnet-enhanced dataset. It achieves the following results on the evaluation set: - Loss: 0.0701 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2636 \| 0.4 \| 100 \| 0.2388 \| 0.9394 \| \| 0.1748 \| 0.8 \| 200 \| 0.1414 \| 0.9623 \| \| 0.1231 \| 1.2 \| 300 \| 0.1565 \| 0.9545 \| \| 0.0769 \| 1.61 \| 400 \| 0.1074 \| 0.9713 \| \| 0.0556 \| 2.01 \| 500 \| 0.0994 \| 0.9726 \| \| 0.0295 \| 2.41 \| 600 \| 0.0720 \| 0.9812 \| \| 0.0311 \| 2.81 \| 700 \| 0.0774 \| 0.9806 \| \| 0.0061 \| 3.21 \| 800 \| 0.0703 \| 0.9822 \| \| 0.0289 \| 3.61 \| 900 \| 0.0701 \| 0.9822 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "biodegradable", "cardboard", "glass", "metal", "paper", "plastic", "trash" ]
Gracoy/swinv2-base-patch4-window8-256-Kaggle_test_20231123	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-base-patch4-window8-256-Kaggle_test_20231123 This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2066 - Accuracy: 0.9315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2882 \| 1.0 \| 64 \| 0.2488 \| 0.9311 \| \| 0.2568 \| 2.0 \| 128 \| 0.2522 \| 0.9311 \| \| 0.1961 \| 3.0 \| 192 \| 0.2066 \| 0.9315 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "0", "1" ]
parotnes/my_awesome_food_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6835 - Accuracy: 0.894 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7311 \| 0.99 \| 62 \| 2.5508 \| 0.833 \| \| 1.8635 \| 2.0 \| 125 \| 1.8232 \| 0.9 \| \| 1.6152 \| 2.98 \| 186 \| 1.6835 \| 0.894 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
Sharon8y/my_awesome_food_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4322 - Accuracy: 0.8855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4246 \| 0.99 \| 41 \| 1.0122 \| 0.7470 \| \| 0.3689 \| 1.99 \| 82 \| 0.8057 \| 0.7831 \| \| 0.1659 \| 2.98 \| 123 \| 0.4473 \| 0.8855 \| \| 0.1287 \| 4.0 \| 165 \| 0.5580 \| 0.8434 \| \| 0.0944 \| 4.97 \| 205 \| 0.4322 \| 0.8855 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "acerolas", "apples", "apricots", "avocados", "bananas", "blackberries", "blueberries", "cantaloupes", "cherries", "coconuts", "figs", "grapefruits", "grapes", "guava", "kiwifruit", "lemons", "limes", "mangos", "olives", "oranges", "passionfruit", "peaches", "pears", "pineapples", "plums", "pomegranates", "raspberries", "strawberries", "tomatoes", "watermelons" ]
danieltur/my_awesome_catdog_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_catdog_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.0083 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0132 \| 0.99 \| 62 \| 0.0121 \| 1.0 \| \| 0.0092 \| 2.0 \| 125 \| 0.0089 \| 1.0 \| \| 0.0083 \| 2.98 \| 186 \| 0.0083 \| 1.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "cat", "dog" ]
parotnes/my_awesome_animal_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_animal_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9721 - Accuracy: 0.966 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.675 \| 0.99 \| 62 \| 1.6475 \| 0.966 \| \| 1.0692 \| 2.0 \| 125 \| 1.1535 \| 0.966 \| \| 0.8611 \| 2.98 \| 186 \| 0.9721 \| 0.966 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "cane", "cavallo", "elefante", "farfalla", "gallina", "gatto", "mucca", "pecora", "ragno", "scoiattolo" ]
scottglover020/convnextv2-tiny-1k-224-finetuned-citrico-2615	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-tiny-1k-224-finetuned-beans This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9837 - Accuracy: 0.6381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7177 \| 1.0 \| 269 \| 0.6939 \| 0.5224 \| \| 0.6132 \| 2.0 \| 538 \| 0.6692 \| 0.6101 \| \| 0.642 \| 3.0 \| 807 \| 0.6729 \| 0.5951 \| \| 0.6431 \| 4.0 \| 1076 \| 0.6700 \| 0.5690 \| \| 0.5081 \| 5.0 \| 1345 \| 0.7537 \| 0.6213 \| \| 0.4114 \| 6.0 \| 1614 \| 0.9249 \| 0.6175 \| \| 0.3991 \| 7.0 \| 1883 \| 0.9837 \| 0.6381 \| \| 0.2194 \| 8.0 \| 2152 \| 1.4350 \| 0.5802 \| \| 0.0834 \| 9.0 \| 2421 \| 1.3808 \| 0.6138 \| \| 0.15 \| 10.0 \| 2690 \| 1.3277 \| 0.6306 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "belly", "notbelly", "unclear" ]
SirSkandrani/food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # SirSkandrani/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3560 - Validation Loss: 0.3026 - Train Accuracy: 0.93 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.7916 \| 1.6000 \| 0.841 \| 0 \| \| 1.2008 \| 0.7763 \| 0.904 \| 1 \| \| 0.6724 \| 0.4730 \| 0.92 \| 2 \| \| 0.4895 \| 0.3631 \| 0.919 \| 3 \| \| 0.3560 \| 0.3026 \| 0.93 \| 4 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
parotnes/my_animals_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_animals_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 2.2858 - Accuracy: 0.1566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 100 - eval_batch_size: 100 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 500 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.287 \| 1.0 \| 42 \| 2.2859 \| 0.1595 \| \| 2.2873 \| 2.0 \| 84 \| 2.2870 \| 0.1610 \| \| 2.287 \| 3.0 \| 126 \| 2.2858 \| 0.1566 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "t - shirt / top", "trouser", "pullover", "dress", "coat", "sandal", "shirt", "sneaker", "bag", "ankle boot" ]
hkivancoral/hushem_5x_deit_base_adamax_00001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_00001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1362 - Accuracy: 0.6444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2172 \| 1.0 \| 27 \| 1.2445 \| 0.4 \| \| 0.8523 \| 2.0 \| 54 \| 1.0947 \| 0.4667 \| \| 0.5686 \| 3.0 \| 81 \| 1.0185 \| 0.5778 \| \| 0.4004 \| 4.0 \| 108 \| 0.9768 \| 0.5778 \| \| 0.2464 \| 5.0 \| 135 \| 0.9587 \| 0.5778 \| \| 0.1691 \| 6.0 \| 162 \| 0.8886 \| 0.6222 \| \| 0.1191 \| 7.0 \| 189 \| 0.9107 \| 0.6 \| \| 0.0619 \| 8.0 \| 216 \| 0.8951 \| 0.6444 \| \| 0.0336 \| 9.0 \| 243 \| 0.9574 \| 0.6 \| \| 0.0186 \| 10.0 \| 270 \| 0.9860 \| 0.5778 \| \| 0.0125 \| 11.0 \| 297 \| 0.9869 \| 0.6 \| \| 0.0084 \| 12.0 \| 324 \| 1.0113 \| 0.6 \| \| 0.0076 \| 13.0 \| 351 \| 0.9936 \| 0.6 \| \| 0.0057 \| 14.0 \| 378 \| 1.0048 \| 0.6 \| \| 0.0052 \| 15.0 \| 405 \| 1.0120 \| 0.6 \| \| 0.0044 \| 16.0 \| 432 \| 1.0086 \| 0.6222 \| \| 0.0038 \| 17.0 \| 459 \| 1.0209 \| 0.6222 \| \| 0.0036 \| 18.0 \| 486 \| 1.0433 \| 0.6222 \| \| 0.0032 \| 19.0 \| 513 \| 1.0446 \| 0.6444 \| \| 0.0029 \| 20.0 \| 540 \| 1.0517 \| 0.6444 \| \| 0.0025 \| 21.0 \| 567 \| 1.0577 \| 0.6444 \| \| 0.0023 \| 22.0 \| 594 \| 1.0550 \| 0.6444 \| \| 0.0022 \| 23.0 \| 621 \| 1.0799 \| 0.6444 \| \| 0.002 \| 24.0 \| 648 \| 1.0753 \| 0.6444 \| \| 0.002 \| 25.0 \| 675 \| 1.0830 \| 0.6444 \| \| 0.002 \| 26.0 \| 702 \| 1.0841 \| 0.6444 \| \| 0.0018 \| 27.0 \| 729 \| 1.0884 \| 0.6444 \| \| 0.0017 \| 28.0 \| 756 \| 1.0904 \| 0.6444 \| \| 0.0017 \| 29.0 \| 783 \| 1.1034 \| 0.6444 \| \| 0.0016 \| 30.0 \| 810 \| 1.1073 \| 0.6444 \| \| 0.0015 \| 31.0 \| 837 \| 1.1021 \| 0.6444 \| \| 0.0015 \| 32.0 \| 864 \| 1.1089 \| 0.6444 \| \| 0.0014 \| 33.0 \| 891 \| 1.1157 \| 0.6444 \| \| 0.0014 \| 34.0 \| 918 \| 1.1170 \| 0.6444 \| \| 0.0013 \| 35.0 \| 945 \| 1.1193 \| 0.6444 \| \| 0.0012 \| 36.0 \| 972 \| 1.1215 \| 0.6444 \| \| 0.0013 \| 37.0 \| 999 \| 1.1225 \| 0.6444 \| \| 0.0012 \| 38.0 \| 1026 \| 1.1226 \| 0.6444 \| \| 0.0012 \| 39.0 \| 1053 \| 1.1299 \| 0.6444 \| \| 0.0011 \| 40.0 \| 1080 \| 1.1301 \| 0.6444 \| \| 0.0012 \| 41.0 \| 1107 \| 1.1312 \| 0.6444 \| \| 0.0011 \| 42.0 \| 1134 \| 1.1308 \| 0.6444 \| \| 0.0012 \| 43.0 \| 1161 \| 1.1360 \| 0.6444 \| \| 0.001 \| 44.0 \| 1188 \| 1.1351 \| 0.6444 \| \| 0.0011 \| 45.0 \| 1215 \| 1.1359 \| 0.6444 \| \| 0.0011 \| 46.0 \| 1242 \| 1.1364 \| 0.6444 \| \| 0.001 \| 47.0 \| 1269 \| 1.1364 \| 0.6444 \| \| 0.0011 \| 48.0 \| 1296 \| 1.1362 \| 0.6444 \| \| 0.0011 \| 49.0 \| 1323 \| 1.1362 \| 0.6444 \| \| 0.0011 \| 50.0 \| 1350 \| 1.1362 \| 0.6444 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_adamax_00001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_00001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3720 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2573 \| 1.0 \| 27 \| 1.3177 \| 0.3778 \| \| 0.9474 \| 2.0 \| 54 \| 1.2698 \| 0.4667 \| \| 0.6743 \| 3.0 \| 81 \| 1.1709 \| 0.5333 \| \| 0.53 \| 4.0 \| 108 \| 1.1238 \| 0.6 \| \| 0.3327 \| 5.0 \| 135 \| 1.1060 \| 0.6 \| \| 0.2187 \| 6.0 \| 162 \| 1.0991 \| 0.6444 \| \| 0.1497 \| 7.0 \| 189 \| 1.1072 \| 0.6444 \| \| 0.086 \| 8.0 \| 216 \| 1.1220 \| 0.6444 \| \| 0.0449 \| 9.0 \| 243 \| 1.1215 \| 0.6444 \| \| 0.0257 \| 10.0 \| 270 \| 1.1368 \| 0.6667 \| \| 0.0174 \| 11.0 \| 297 \| 1.1587 \| 0.6667 \| \| 0.0102 \| 12.0 \| 324 \| 1.1715 \| 0.6889 \| \| 0.0083 \| 13.0 \| 351 \| 1.2117 \| 0.6889 \| \| 0.0067 \| 14.0 \| 378 \| 1.2042 \| 0.6889 \| \| 0.0061 \| 15.0 \| 405 \| 1.2320 \| 0.6889 \| \| 0.0048 \| 16.0 \| 432 \| 1.2396 \| 0.6889 \| \| 0.0043 \| 17.0 \| 459 \| 1.2501 \| 0.6889 \| \| 0.0039 \| 18.0 \| 486 \| 1.2585 \| 0.6667 \| \| 0.0034 \| 19.0 \| 513 \| 1.2714 \| 0.6889 \| \| 0.0031 \| 20.0 \| 540 \| 1.2786 \| 0.6889 \| \| 0.0029 \| 21.0 \| 567 \| 1.2831 \| 0.6667 \| \| 0.0026 \| 22.0 \| 594 \| 1.2886 \| 0.6667 \| \| 0.0022 \| 23.0 \| 621 \| 1.2985 \| 0.6667 \| \| 0.0022 \| 24.0 \| 648 \| 1.3036 \| 0.6667 \| \| 0.002 \| 25.0 \| 675 \| 1.3071 \| 0.6667 \| \| 0.002 \| 26.0 \| 702 \| 1.3150 \| 0.6667 \| \| 0.0017 \| 27.0 \| 729 \| 1.3222 \| 0.6667 \| \| 0.0018 \| 28.0 \| 756 \| 1.3235 \| 0.6667 \| \| 0.0018 \| 29.0 \| 783 \| 1.3294 \| 0.6667 \| \| 0.0017 \| 30.0 \| 810 \| 1.3351 \| 0.6667 \| \| 0.0015 \| 31.0 \| 837 \| 1.3358 \| 0.6667 \| \| 0.0016 \| 32.0 \| 864 \| 1.3406 \| 0.6667 \| \| 0.0015 \| 33.0 \| 891 \| 1.3434 \| 0.6667 \| \| 0.0014 \| 34.0 \| 918 \| 1.3481 \| 0.6667 \| \| 0.0013 \| 35.0 \| 945 \| 1.3523 \| 0.6667 \| \| 0.0013 \| 36.0 \| 972 \| 1.3535 \| 0.6667 \| \| 0.0013 \| 37.0 \| 999 \| 1.3558 \| 0.6667 \| \| 0.0012 \| 38.0 \| 1026 \| 1.3590 \| 0.6667 \| \| 0.0012 \| 39.0 \| 1053 \| 1.3619 \| 0.6667 \| \| 0.0011 \| 40.0 \| 1080 \| 1.3634 \| 0.6667 \| \| 0.0012 \| 41.0 \| 1107 \| 1.3657 \| 0.6667 \| \| 0.0011 \| 42.0 \| 1134 \| 1.3669 \| 0.6667 \| \| 0.0011 \| 43.0 \| 1161 \| 1.3696 \| 0.6667 \| \| 0.0011 \| 44.0 \| 1188 \| 1.3699 \| 0.6667 \| \| 0.0011 \| 45.0 \| 1215 \| 1.3707 \| 0.6667 \| \| 0.0011 \| 46.0 \| 1242 \| 1.3712 \| 0.6667 \| \| 0.0011 \| 47.0 \| 1269 \| 1.3718 \| 0.6667 \| \| 0.0011 \| 48.0 \| 1296 \| 1.3720 \| 0.6667 \| \| 0.0011 \| 49.0 \| 1323 \| 1.3720 \| 0.6667 \| \| 0.0011 \| 50.0 \| 1350 \| 1.3720 \| 0.6667 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
bortle/astrophotography-object-classifier-alpha5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # astrophotography-object-classifier-alpha5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1827 - Accuracy: 0.9516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 0.2639 \| 1.0 \| 2575 \| 0.2192 \| 0.9461 \| \| 0.2457 \| 2.0 \| 5150 \| 0.2065 \| 0.9464 \| \| 0.3157 \| 3.0 \| 7725 \| 0.1827 \| 0.9516 \| \| 0.3149 \| 4.0 \| 10300 \| 0.1855 \| 0.9488 \| \| 0.1212 \| 5.0 \| 12875 \| 0.2079 \| 0.9480 \| \| 0.078 \| 6.0 \| 15450 \| 0.2008 \| 0.9516 \| \| 0.3493 \| 7.0 \| 18025 \| 0.2038 \| 0.9497 \| \| 0.131 \| 8.0 \| 20600 \| 0.2059 \| 0.9510 \| \| 0.2658 \| 9.0 \| 23175 \| 0.2089 \| 0.9510 \| \| 0.0762 \| 10.0 \| 25750 \| 0.2068 \| 0.9541 \| \| 0.127 \| 11.0 \| 28325 \| 0.1986 \| 0.9543 \| \| 0.181 \| 12.0 \| 30900 \| 0.2227 \| 0.9513 \| \| 0.1072 \| 13.0 \| 33475 \| 0.2303 \| 0.9502 \| \| 0.0179 \| 14.0 \| 36050 \| 0.2240 \| 0.9483 \| \| 0.1447 \| 15.0 \| 38625 \| 0.2364 \| 0.9505 \| \| 0.0933 \| 16.0 \| 41200 \| 0.2372 \| 0.9532 \| \| 0.17 \| 17.0 \| 43775 \| 0.2166 \| 0.9557 \| \| 0.0463 \| 18.0 \| 46350 \| 0.2852 \| 0.9461 \| \| 0.1207 \| 19.0 \| 48925 \| 0.2653 \| 0.9508 \| \| 0.1761 \| 20.0 \| 51500 \| 0.2443 \| 0.9521 \| \| 0.1441 \| 21.0 \| 54075 \| 0.2464 \| 0.9535 \| \| 0.1279 \| 22.0 \| 56650 \| 0.2681 \| 0.9499 \| \| 0.1811 \| 23.0 \| 59225 \| 0.2626 \| 0.9538 \| \| 0.1737 \| 24.0 \| 61800 \| 0.2604 \| 0.9541 \| \| 0.0275 \| 25.0 \| 64375 \| 0.2625 \| 0.9510 \| \| 0.1757 \| 26.0 \| 66950 \| 0.2819 \| 0.9488 \| \| 0.1257 \| 27.0 \| 69525 \| 0.2708 \| 0.9521 \| \| 0.1097 \| 28.0 \| 72100 \| 0.2801 \| 0.9519 \| \| 0.0772 \| 29.0 \| 74675 \| 0.2870 \| 0.9499 \| \| 0.132 \| 30.0 \| 77250 \| 0.2824 \| 0.9497 \| \| 0.0652 \| 31.0 \| 79825 \| 0.2628 \| 0.9538 \| \| 0.0324 \| 32.0 \| 82400 \| 0.3223 \| 0.9453 \| \| 0.1774 \| 33.0 \| 84975 \| 0.2749 \| 0.9549 \| \| 0.1178 \| 34.0 \| 87550 \| 0.2905 \| 0.9513 \| \| 0.0804 \| 35.0 \| 90125 \| 0.3100 \| 0.9480 \| \| 0.0617 \| 36.0 \| 92700 \| 0.3131 \| 0.9475 \| \| 0.0348 \| 37.0 \| 95275 \| 0.3341 \| 0.9486 \| \| 0.0057 \| 38.0 \| 97850 \| 0.3225 \| 0.9466 \| \| 0.0409 \| 39.0 \| 100425 \| 0.3206 \| 0.9483 \| \| 0.1052 \| 40.0 \| 103000 \| 0.3212 \| 0.9494 \| \| 0.0943 \| 41.0 \| 105575 \| 0.3075 \| 0.9508 \| \| 0.0018 \| 42.0 \| 108150 \| 0.3062 \| 0.9519 \| \| 0.0287 \| 43.0 \| 110725 \| 0.3224 \| 0.9469 \| \| 0.0384 \| 44.0 \| 113300 \| 0.3086 \| 0.9488 \| \| 0.1214 \| 45.0 \| 115875 \| 0.3145 \| 0.9494 \| \| 0.1735 \| 46.0 \| 118450 \| 0.3191 \| 0.9494 \| \| 0.0477 \| 47.0 \| 121025 \| 0.3004 \| 0.9521 \| \| 0.0221 \| 48.0 \| 123600 \| 0.3205 \| 0.9480 \| \| 0.0939 \| 49.0 \| 126175 \| 0.3431 \| 0.9486 \| \| 0.0599 \| 50.0 \| 128750 \| 0.3167 \| 0.9516 \| \| 0.1785 \| 51.0 \| 131325 \| 0.3274 \| 0.9513 \| \| 0.1039 \| 52.0 \| 133900 \| 0.3114 \| 0.9519 \| \| 0.0527 \| 53.0 \| 136475 \| 0.3252 \| 0.9477 \| \| 0.0584 \| 54.0 \| 139050 \| 0.3200 \| 0.9510 \| \| 0.1022 \| 55.0 \| 141625 \| 0.3284 \| 0.9491 \| \| 0.013 \| 56.0 \| 144200 \| 0.3386 \| 0.9475 \| \| 0.0488 \| 57.0 \| 146775 \| 0.3290 \| 0.9505 \| \| 0.0514 \| 58.0 \| 149350 \| 0.3126 \| 0.9535 \| \| 0.0184 \| 59.0 \| 151925 \| 0.3196 \| 0.9532 \| \| 0.1233 \| 60.0 \| 154500 \| 0.3270 \| 0.9516 \| \| 0.1667 \| 61.0 \| 157075 \| 0.3250 \| 0.9502 \| \| 0.0497 \| 62.0 \| 159650 \| 0.3375 \| 0.9466 \| \| 0.0445 \| 63.0 \| 162225 \| 0.3493 \| 0.9502 \| \| 0.114 \| 64.0 \| 164800 \| 0.3368 \| 0.9488 \| \| 0.048 \| 65.0 \| 167375 \| 0.3358 \| 0.9510 \| \| 0.2337 \| 66.0 \| 169950 \| 0.3330 \| 0.9510 \| \| 0.0705 \| 67.0 \| 172525 \| 0.3480 \| 0.9510 \| \| 0.094 \| 68.0 \| 175100 \| 0.3508 \| 0.9497 \| \| 0.0498 \| 69.0 \| 177675 \| 0.3328 \| 0.9508 \| \| 0.0535 \| 70.0 \| 180250 \| 0.3558 \| 0.9499 \| \| 0.0217 \| 71.0 \| 182825 \| 0.3583 \| 0.9488 \| \| 0.0264 \| 72.0 \| 185400 \| 0.3600 \| 0.9477 \| \| 0.0108 \| 73.0 \| 187975 \| 0.3629 \| 0.9491 \| \| 0.0446 \| 74.0 \| 190550 \| 0.3570 \| 0.9508 \| \| 0.0702 \| 75.0 \| 193125 \| 0.3600 \| 0.9502 \| \| 0.141 \| 76.0 \| 195700 \| 0.3428 \| 0.9527 \| \| 0.0226 \| 77.0 \| 198275 \| 0.3594 \| 0.9502 \| \| 0.0055 \| 78.0 \| 200850 \| 0.3653 \| 0.9508 \| \| 0.1442 \| 79.0 \| 203425 \| 0.3437 \| 0.9530 \| \| 0.0834 \| 80.0 \| 206000 \| 0.3431 \| 0.9524 \| \| 0.0388 \| 81.0 \| 208575 \| 0.3426 \| 0.9521 \| \| 0.0321 \| 82.0 \| 211150 \| 0.3555 \| 0.9497 \| \| 0.051 \| 83.0 \| 213725 \| 0.3730 \| 0.9505 \| \| 0.0049 \| 84.0 \| 216300 \| 0.3549 \| 0.9527 \| \| 0.043 \| 85.0 \| 218875 \| 0.3592 \| 0.9524 \| \| 0.0284 \| 86.0 \| 221450 \| 0.3749 \| 0.9499 \| \| 0.0923 \| 87.0 \| 224025 \| 0.3527 \| 0.9513 \| \| 0.1188 \| 88.0 \| 226600 \| 0.3725 \| 0.9486 \| \| 0.1493 \| 89.0 \| 229175 \| 0.3560 \| 0.9521 \| \| 0.0164 \| 90.0 \| 231750 \| 0.3573 \| 0.9508 \| \| 0.0477 \| 91.0 \| 234325 \| 0.3679 \| 0.9502 \| \| 0.0827 \| 92.0 \| 236900 \| 0.3683 \| 0.9486 \| \| 0.0799 \| 93.0 \| 239475 \| 0.3667 \| 0.9510 \| \| 0.0413 \| 94.0 \| 242050 \| 0.3604 \| 0.9516 \| \| 0.071 \| 95.0 \| 244625 \| 0.3725 \| 0.9483 \| \| 0.2079 \| 96.0 \| 247200 \| 0.3688 \| 0.9483 \| \| 0.0665 \| 97.0 \| 249775 \| 0.3576 \| 0.9521 \| \| 0.0673 \| 98.0 \| 252350 \| 0.3636 \| 0.9513 \| \| 0.062 \| 99.0 \| 254925 \| 0.3688 \| 0.9513 \| \| 0.1217 \| 100.0 \| 257500 \| 0.3742 \| 0.9508 \| \| 0.0951 \| 101.0 \| 260075 \| 0.3718 \| 0.9491 \| \| 0.0118 \| 102.0 \| 262650 \| 0.3849 \| 0.9491 \| \| 0.0307 \| 103.0 \| 265225 \| 0.3644 \| 0.9535 \| \| 0.0157 \| 104.0 \| 267800 \| 0.3647 \| 0.9524 \| \| 0.0125 \| 105.0 \| 270375 \| 0.3994 \| 0.9486 \| \| 0.0213 \| 106.0 \| 272950 \| 0.3775 \| 0.9499 \| \| 0.1249 \| 107.0 \| 275525 \| 0.3902 \| 0.9491 \| \| 0.0333 \| 108.0 \| 278100 \| 0.3637 \| 0.9516 \| \| 0.0545 \| 109.0 \| 280675 \| 0.3663 \| 0.9521 \| \| 0.1136 \| 110.0 \| 283250 \| 0.3847 \| 0.9502 \| \| 0.0751 \| 111.0 \| 285825 \| 0.3818 \| 0.9513 \| \| 0.001 \| 112.0 \| 288400 \| 0.3811 \| 0.9521 \| \| 0.0282 \| 113.0 \| 290975 \| 0.3843 \| 0.9510 \| \| 0.1117 \| 114.0 \| 293550 \| 0.3790 \| 0.9521 \| \| 0.0022 \| 115.0 \| 296125 \| 0.3717 \| 0.9521 \| \| 0.0203 \| 116.0 \| 298700 \| 0.3794 \| 0.9530 \| \| 0.0437 \| 117.0 \| 301275 \| 0.3807 \| 0.9527 \| \| 0.0045 \| 118.0 \| 303850 \| 0.3821 \| 0.9530 \| \| 0.0015 \| 119.0 \| 306425 \| 0.3867 \| 0.9527 \| \| 0.1152 \| 120.0 \| 309000 \| 0.3842 \| 0.9521 \| \| 0.0748 \| 121.0 \| 311575 \| 0.3839 \| 0.9527 \| \| 0.0955 \| 122.0 \| 314150 \| 0.3805 \| 0.9516 \| \| 0.0043 \| 123.0 \| 316725 \| 0.3833 \| 0.9521 \| \| 0.0249 \| 124.0 \| 319300 \| 0.3745 \| 0.9497 \| \| 0.0002 \| 125.0 \| 321875 \| 0.3744 \| 0.9519 \| \| 0.0169 \| 126.0 \| 324450 \| 0.3808 \| 0.9510 \| \| 0.0277 \| 127.0 \| 327025 \| 0.3735 \| 0.9524 \| \| 0.0082 \| 128.0 \| 329600 \| 0.3831 \| 0.9527 \| \| 0.0737 \| 129.0 \| 332175 \| 0.3891 \| 0.9524 \| \| 0.0517 \| 130.0 \| 334750 \| 0.3839 \| 0.9530 \| \| 0.0218 \| 131.0 \| 337325 \| 0.3863 \| 0.9527 \| \| 0.0228 \| 132.0 \| 339900 \| 0.3913 \| 0.9519 \| \| 0.0094 \| 133.0 \| 342475 \| 0.3968 \| 0.9513 \| \| 0.0784 \| 134.0 \| 345050 \| 0.3871 \| 0.9532 \| \| 0.0116 \| 135.0 \| 347625 \| 0.3890 \| 0.9538 \| \| 0.015 \| 136.0 \| 350200 \| 0.3846 \| 0.9530 \| \| 0.0307 \| 137.0 \| 352775 \| 0.3850 \| 0.9530 \| \| 0.0081 \| 138.0 \| 355350 \| 0.3852 \| 0.9532 \| \| 0.0705 \| 139.0 \| 357925 \| 0.3859 \| 0.9527 \| \| 0.0442 \| 140.0 \| 360500 \| 0.3871 \| 0.9524 \| \| 0.0888 \| 141.0 \| 363075 \| 0.3851 \| 0.9535 \| \| 0.0169 \| 142.0 \| 365650 \| 0.3908 \| 0.9527 \| \| 0.0132 \| 143.0 \| 368225 \| 0.3923 \| 0.9527 \| \| 0.0349 \| 144.0 \| 370800 \| 0.3880 \| 0.9527 \| \| 0.0014 \| 145.0 \| 373375 \| 0.3875 \| 0.9535 \| \| 0.0495 \| 146.0 \| 375950 \| 0.3898 \| 0.9535 \| \| 0.0006 \| 147.0 \| 378525 \| 0.3908 \| 0.9530 \| \| 0.0226 \| 148.0 \| 381100 \| 0.3899 \| 0.9527 \| \| 0.0927 \| 149.0 \| 383675 \| 0.3895 \| 0.9527 \| \| 0.081 \| 150.0 \| 386250 \| 0.3896 \| 0.9527 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "diffuse_nebula", "galaxy", "sun", "supernova_remnant", "globular_cluster", "jupiter", "mars", "milky_way", "moon", "open_cluster", "planetary_nebula", "saturn" ]
hkivancoral/hushem_5x_deit_base_adamax_00001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_00001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4419 - Accuracy: 0.8372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3275 \| 1.0 \| 28 \| 1.2372 \| 0.5814 \| \| 1.0641 \| 2.0 \| 56 \| 1.0484 \| 0.6977 \| \| 0.7591 \| 3.0 \| 84 \| 0.8760 \| 0.7442 \| \| 0.5652 \| 4.0 \| 112 \| 0.7360 \| 0.8140 \| \| 0.3906 \| 5.0 \| 140 \| 0.6489 \| 0.8372 \| \| 0.3059 \| 6.0 \| 168 \| 0.5954 \| 0.8605 \| \| 0.1994 \| 7.0 \| 196 \| 0.5269 \| 0.8372 \| \| 0.134 \| 8.0 \| 224 \| 0.5174 \| 0.8605 \| \| 0.0783 \| 9.0 \| 252 \| 0.4602 \| 0.8605 \| \| 0.0454 \| 10.0 \| 280 \| 0.4569 \| 0.8372 \| \| 0.0318 \| 11.0 \| 308 \| 0.4393 \| 0.8837 \| \| 0.018 \| 12.0 \| 336 \| 0.4222 \| 0.8605 \| \| 0.0132 \| 13.0 \| 364 \| 0.4453 \| 0.8837 \| \| 0.0088 \| 14.0 \| 392 \| 0.4098 \| 0.8837 \| \| 0.0068 \| 15.0 \| 420 \| 0.4226 \| 0.8605 \| \| 0.0058 \| 16.0 \| 448 \| 0.4268 \| 0.8605 \| \| 0.0055 \| 17.0 \| 476 \| 0.4132 \| 0.8605 \| \| 0.0045 \| 18.0 \| 504 \| 0.4342 \| 0.8605 \| \| 0.004 \| 19.0 \| 532 \| 0.4228 \| 0.8605 \| \| 0.0033 \| 20.0 \| 560 \| 0.4271 \| 0.8372 \| \| 0.0033 \| 21.0 \| 588 \| 0.4254 \| 0.8372 \| \| 0.0029 \| 22.0 \| 616 \| 0.4205 \| 0.8372 \| \| 0.0027 \| 23.0 \| 644 \| 0.4207 \| 0.8372 \| \| 0.0024 \| 24.0 \| 672 \| 0.4248 \| 0.8605 \| \| 0.0022 \| 25.0 \| 700 \| 0.4229 \| 0.8372 \| \| 0.0021 \| 26.0 \| 728 \| 0.4293 \| 0.8372 \| \| 0.002 \| 27.0 \| 756 \| 0.4267 \| 0.8372 \| \| 0.002 \| 28.0 \| 784 \| 0.4239 \| 0.8605 \| \| 0.0018 \| 29.0 \| 812 \| 0.4273 \| 0.8372 \| \| 0.0018 \| 30.0 \| 840 \| 0.4313 \| 0.8372 \| \| 0.0016 \| 31.0 \| 868 \| 0.4289 \| 0.8372 \| \| 0.0016 \| 32.0 \| 896 \| 0.4329 \| 0.8372 \| \| 0.0016 \| 33.0 \| 924 \| 0.4313 \| 0.8372 \| \| 0.0014 \| 34.0 \| 952 \| 0.4362 \| 0.8372 \| \| 0.0016 \| 35.0 \| 980 \| 0.4336 \| 0.8372 \| \| 0.0014 \| 36.0 \| 1008 \| 0.4353 \| 0.8372 \| \| 0.0014 \| 37.0 \| 1036 \| 0.4446 \| 0.8372 \| \| 0.0013 \| 38.0 \| 1064 \| 0.4482 \| 0.8372 \| \| 0.0013 \| 39.0 \| 1092 \| 0.4496 \| 0.8372 \| \| 0.0012 \| 40.0 \| 1120 \| 0.4442 \| 0.8372 \| \| 0.0013 \| 41.0 \| 1148 \| 0.4456 \| 0.8372 \| \| 0.0013 \| 42.0 \| 1176 \| 0.4450 \| 0.8372 \| \| 0.0012 \| 43.0 \| 1204 \| 0.4433 \| 0.8372 \| \| 0.0012 \| 44.0 \| 1232 \| 0.4424 \| 0.8372 \| \| 0.0011 \| 45.0 \| 1260 \| 0.4418 \| 0.8372 \| \| 0.0011 \| 46.0 \| 1288 \| 0.4417 \| 0.8372 \| \| 0.0011 \| 47.0 \| 1316 \| 0.4421 \| 0.8372 \| \| 0.0011 \| 48.0 \| 1344 \| 0.4419 \| 0.8372 \| \| 0.0011 \| 49.0 \| 1372 \| 0.4419 \| 0.8372 \| \| 0.0011 \| 50.0 \| 1400 \| 0.4419 \| 0.8372 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_adamax_00001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_00001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2070 - Accuracy: 0.9286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2779 \| 1.0 \| 28 \| 1.2578 \| 0.5 \| \| 0.9904 \| 2.0 \| 56 \| 1.0864 \| 0.5952 \| \| 0.7136 \| 3.0 \| 84 \| 0.8757 \| 0.7381 \| \| 0.5283 \| 4.0 \| 112 \| 0.7271 \| 0.8095 \| \| 0.3401 \| 5.0 \| 140 \| 0.5900 \| 0.8333 \| \| 0.2667 \| 6.0 \| 168 \| 0.4970 \| 0.8571 \| \| 0.1719 \| 7.0 \| 196 \| 0.4291 \| 0.8810 \| \| 0.1351 \| 8.0 \| 224 \| 0.3736 \| 0.8810 \| \| 0.0756 \| 9.0 \| 252 \| 0.3239 \| 0.8571 \| \| 0.0457 \| 10.0 \| 280 \| 0.2724 \| 0.9286 \| \| 0.0311 \| 11.0 \| 308 \| 0.2513 \| 0.9286 \| \| 0.0183 \| 12.0 \| 336 \| 0.2397 \| 0.9524 \| \| 0.0115 \| 13.0 \| 364 \| 0.2242 \| 0.9286 \| \| 0.0092 \| 14.0 \| 392 \| 0.2124 \| 0.9286 \| \| 0.0064 \| 15.0 \| 420 \| 0.2027 \| 0.9286 \| \| 0.0052 \| 16.0 \| 448 \| 0.2099 \| 0.9286 \| \| 0.0048 \| 17.0 \| 476 \| 0.2208 \| 0.9286 \| \| 0.0041 \| 18.0 \| 504 \| 0.2156 \| 0.9286 \| \| 0.0036 \| 19.0 \| 532 \| 0.2081 \| 0.9286 \| \| 0.0034 \| 20.0 \| 560 \| 0.2100 \| 0.9286 \| \| 0.003 \| 21.0 \| 588 \| 0.2099 \| 0.9286 \| \| 0.0028 \| 22.0 \| 616 \| 0.2113 \| 0.9286 \| \| 0.0024 \| 23.0 \| 644 \| 0.2110 \| 0.9286 \| \| 0.0023 \| 24.0 \| 672 \| 0.2106 \| 0.9286 \| \| 0.0022 \| 25.0 \| 700 \| 0.2101 \| 0.9286 \| \| 0.002 \| 26.0 \| 728 \| 0.2088 \| 0.9286 \| \| 0.002 \| 27.0 \| 756 \| 0.2066 \| 0.9286 \| \| 0.0018 \| 28.0 \| 784 \| 0.2096 \| 0.9286 \| \| 0.0018 \| 29.0 \| 812 \| 0.2064 \| 0.9286 \| \| 0.0016 \| 30.0 \| 840 \| 0.2088 \| 0.9286 \| \| 0.0016 \| 31.0 \| 868 \| 0.2088 \| 0.9286 \| \| 0.0015 \| 32.0 \| 896 \| 0.2078 \| 0.9286 \| \| 0.0015 \| 33.0 \| 924 \| 0.2057 \| 0.9286 \| \| 0.0014 \| 34.0 \| 952 \| 0.2073 \| 0.9286 \| \| 0.0014 \| 35.0 \| 980 \| 0.2070 \| 0.9286 \| \| 0.0014 \| 36.0 \| 1008 \| 0.2069 \| 0.9286 \| \| 0.0013 \| 37.0 \| 1036 \| 0.2071 \| 0.9286 \| \| 0.0013 \| 38.0 \| 1064 \| 0.2055 \| 0.9286 \| \| 0.0013 \| 39.0 \| 1092 \| 0.2077 \| 0.9286 \| \| 0.0011 \| 40.0 \| 1120 \| 0.2076 \| 0.9286 \| \| 0.0012 \| 41.0 \| 1148 \| 0.2068 \| 0.9286 \| \| 0.0012 \| 42.0 \| 1176 \| 0.2086 \| 0.9286 \| \| 0.0011 \| 43.0 \| 1204 \| 0.2084 \| 0.9286 \| \| 0.0011 \| 44.0 \| 1232 \| 0.2077 \| 0.9286 \| \| 0.0011 \| 45.0 \| 1260 \| 0.2078 \| 0.9286 \| \| 0.0011 \| 46.0 \| 1288 \| 0.2072 \| 0.9286 \| \| 0.0011 \| 47.0 \| 1316 \| 0.2070 \| 0.9286 \| \| 0.0011 \| 48.0 \| 1344 \| 0.2070 \| 0.9286 \| \| 0.0012 \| 49.0 \| 1372 \| 0.2070 \| 0.9286 \| \| 0.0011 \| 50.0 \| 1400 \| 0.2070 \| 0.9286 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_adamax_00001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_adamax_00001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5126 - Accuracy: 0.8780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2842 \| 1.0 \| 28 \| 1.2327 \| 0.4634 \| \| 1.0428 \| 2.0 \| 56 \| 1.0859 \| 0.5854 \| \| 0.7338 \| 3.0 \| 84 \| 0.9001 \| 0.6585 \| \| 0.526 \| 4.0 \| 112 \| 0.7803 \| 0.6829 \| \| 0.3644 \| 5.0 \| 140 \| 0.6952 \| 0.6829 \| \| 0.2797 \| 6.0 \| 168 \| 0.5854 \| 0.7073 \| \| 0.1574 \| 7.0 \| 196 \| 0.5546 \| 0.7561 \| \| 0.1102 \| 8.0 \| 224 \| 0.5073 \| 0.7805 \| \| 0.0668 \| 9.0 \| 252 \| 0.4703 \| 0.8049 \| \| 0.0352 \| 10.0 \| 280 \| 0.4612 \| 0.8293 \| \| 0.0202 \| 11.0 \| 308 \| 0.4468 \| 0.8537 \| \| 0.0126 \| 12.0 \| 336 \| 0.4508 \| 0.8537 \| \| 0.0083 \| 13.0 \| 364 \| 0.4495 \| 0.8537 \| \| 0.0078 \| 14.0 \| 392 \| 0.4584 \| 0.8780 \| \| 0.0058 \| 15.0 \| 420 \| 0.4565 \| 0.8537 \| \| 0.0051 \| 16.0 \| 448 \| 0.4617 \| 0.8780 \| \| 0.0041 \| 17.0 \| 476 \| 0.4620 \| 0.8537 \| \| 0.0038 \| 18.0 \| 504 \| 0.4684 \| 0.8780 \| \| 0.0035 \| 19.0 \| 532 \| 0.4714 \| 0.8780 \| \| 0.0033 \| 20.0 \| 560 \| 0.4764 \| 0.8780 \| \| 0.0027 \| 21.0 \| 588 \| 0.4824 \| 0.8780 \| \| 0.0027 \| 22.0 \| 616 \| 0.4828 \| 0.8780 \| \| 0.0023 \| 23.0 \| 644 \| 0.4858 \| 0.8780 \| \| 0.0021 \| 24.0 \| 672 \| 0.4874 \| 0.8780 \| \| 0.002 \| 25.0 \| 700 \| 0.4903 \| 0.8780 \| \| 0.002 \| 26.0 \| 728 \| 0.4912 \| 0.8780 \| \| 0.0019 \| 27.0 \| 756 \| 0.4926 \| 0.8780 \| \| 0.0017 \| 28.0 \| 784 \| 0.4940 \| 0.8780 \| \| 0.0016 \| 29.0 \| 812 \| 0.4962 \| 0.8780 \| \| 0.0016 \| 30.0 \| 840 \| 0.4964 \| 0.8780 \| \| 0.0015 \| 31.0 \| 868 \| 0.4991 \| 0.8780 \| \| 0.0015 \| 32.0 \| 896 \| 0.5002 \| 0.8780 \| \| 0.0013 \| 33.0 \| 924 \| 0.5024 \| 0.8780 \| \| 0.0014 \| 34.0 \| 952 \| 0.5035 \| 0.8780 \| \| 0.0013 \| 35.0 \| 980 \| 0.5045 \| 0.8780 \| \| 0.0013 \| 36.0 \| 1008 \| 0.5049 \| 0.8780 \| \| 0.0012 \| 37.0 \| 1036 \| 0.5067 \| 0.8780 \| \| 0.0012 \| 38.0 \| 1064 \| 0.5085 \| 0.8780 \| \| 0.0012 \| 39.0 \| 1092 \| 0.5083 \| 0.8780 \| \| 0.0011 \| 40.0 \| 1120 \| 0.5095 \| 0.8780 \| \| 0.0011 \| 41.0 \| 1148 \| 0.5101 \| 0.8780 \| \| 0.0011 \| 42.0 \| 1176 \| 0.5103 \| 0.8780 \| \| 0.0011 \| 43.0 \| 1204 \| 0.5116 \| 0.8780 \| \| 0.0011 \| 44.0 \| 1232 \| 0.5122 \| 0.8780 \| \| 0.0011 \| 45.0 \| 1260 \| 0.5120 \| 0.8780 \| \| 0.0011 \| 46.0 \| 1288 \| 0.5123 \| 0.8780 \| \| 0.001 \| 47.0 \| 1316 \| 0.5124 \| 0.8780 \| \| 0.0011 \| 48.0 \| 1344 \| 0.5126 \| 0.8780 \| \| 0.001 \| 49.0 \| 1372 \| 0.5126 \| 0.8780 \| \| 0.0011 \| 50.0 \| 1400 \| 0.5126 \| 0.8780 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_00001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_00001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3013 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7665 \| 1.0 \| 27 \| 0.8626 \| 0.6667 \| \| 0.1538 \| 2.0 \| 54 \| 0.9929 \| 0.6444 \| \| 0.0306 \| 3.0 \| 81 \| 1.0027 \| 0.6667 \| \| 0.0064 \| 4.0 \| 108 \| 0.7842 \| 0.7778 \| \| 0.0027 \| 5.0 \| 135 \| 0.8679 \| 0.7556 \| \| 0.0017 \| 6.0 \| 162 \| 0.8696 \| 0.7778 \| \| 0.0013 \| 7.0 \| 189 \| 0.8740 \| 0.8 \| \| 0.0009 \| 8.0 \| 216 \| 0.8908 \| 0.8 \| \| 0.0007 \| 9.0 \| 243 \| 0.9347 \| 0.8 \| \| 0.0006 \| 10.0 \| 270 \| 0.9426 \| 0.8 \| \| 0.0005 \| 11.0 \| 297 \| 0.9525 \| 0.8 \| \| 0.0004 \| 12.0 \| 324 \| 0.9729 \| 0.8 \| \| 0.0003 \| 13.0 \| 351 \| 0.9639 \| 0.8 \| \| 0.0003 \| 14.0 \| 378 \| 0.9770 \| 0.8 \| \| 0.0002 \| 15.0 \| 405 \| 1.0081 \| 0.8 \| \| 0.0002 \| 16.0 \| 432 \| 1.0083 \| 0.8 \| \| 0.0002 \| 17.0 \| 459 \| 1.0108 \| 0.8 \| \| 0.0002 \| 18.0 \| 486 \| 1.0375 \| 0.8 \| \| 0.0001 \| 19.0 \| 513 \| 1.0395 \| 0.8 \| \| 0.0001 \| 20.0 \| 540 \| 1.0606 \| 0.8 \| \| 0.0001 \| 21.0 \| 567 \| 1.0558 \| 0.8 \| \| 0.0001 \| 22.0 \| 594 \| 1.0825 \| 0.8 \| \| 0.0001 \| 23.0 \| 621 \| 1.0953 \| 0.8 \| \| 0.0001 \| 24.0 \| 648 \| 1.1052 \| 0.8 \| \| 0.0001 \| 25.0 \| 675 \| 1.1190 \| 0.8 \| \| 0.0001 \| 26.0 \| 702 \| 1.1262 \| 0.8 \| \| 0.0001 \| 27.0 \| 729 \| 1.1329 \| 0.8 \| \| 0.0 \| 28.0 \| 756 \| 1.1463 \| 0.8 \| \| 0.0 \| 29.0 \| 783 \| 1.1643 \| 0.8 \| \| 0.0 \| 30.0 \| 810 \| 1.1628 \| 0.8 \| \| 0.0 \| 31.0 \| 837 \| 1.1766 \| 0.8 \| \| 0.0 \| 32.0 \| 864 \| 1.1948 \| 0.8 \| \| 0.0 \| 33.0 \| 891 \| 1.2037 \| 0.8 \| \| 0.0 \| 34.0 \| 918 \| 1.2175 \| 0.8 \| \| 0.0 \| 35.0 \| 945 \| 1.2224 \| 0.8 \| \| 0.0 \| 36.0 \| 972 \| 1.2274 \| 0.8 \| \| 0.0 \| 37.0 \| 999 \| 1.2352 \| 0.8 \| \| 0.0 \| 38.0 \| 1026 \| 1.2512 \| 0.8 \| \| 0.0 \| 39.0 \| 1053 \| 1.2560 \| 0.8 \| \| 0.0 \| 40.0 \| 1080 \| 1.2629 \| 0.8 \| \| 0.0 \| 41.0 \| 1107 \| 1.2729 \| 0.8 \| \| 0.0 \| 42.0 \| 1134 \| 1.2812 \| 0.8 \| \| 0.0 \| 43.0 \| 1161 \| 1.2836 \| 0.8 \| \| 0.0 \| 44.0 \| 1188 \| 1.2893 \| 0.8 \| \| 0.0 \| 45.0 \| 1215 \| 1.2944 \| 0.8 \| \| 0.0 \| 46.0 \| 1242 \| 1.2996 \| 0.8 \| \| 0.0 \| 47.0 \| 1269 \| 1.3018 \| 0.8 \| \| 0.0 \| 48.0 \| 1296 \| 1.3014 \| 0.8 \| \| 0.0 \| 49.0 \| 1323 \| 1.3013 \| 0.8 \| \| 0.0 \| 50.0 \| 1350 \| 1.3013 \| 0.8 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_00001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_00001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6978 - Accuracy: 0.7333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7885 \| 1.0 \| 27 \| 1.0771 \| 0.6222 \| \| 0.1275 \| 2.0 \| 54 \| 1.2167 \| 0.6667 \| \| 0.0229 \| 3.0 \| 81 \| 1.0976 \| 0.6667 \| \| 0.0044 \| 4.0 \| 108 \| 1.1187 \| 0.6889 \| \| 0.0023 \| 5.0 \| 135 \| 1.1431 \| 0.7111 \| \| 0.0016 \| 6.0 \| 162 \| 1.1708 \| 0.7111 \| \| 0.0013 \| 7.0 \| 189 \| 1.2048 \| 0.7333 \| \| 0.0009 \| 8.0 \| 216 \| 1.2211 \| 0.7111 \| \| 0.0007 \| 9.0 \| 243 \| 1.2394 \| 0.7333 \| \| 0.0006 \| 10.0 \| 270 \| 1.2768 \| 0.7111 \| \| 0.0005 \| 11.0 \| 297 \| 1.2868 \| 0.7333 \| \| 0.0004 \| 12.0 \| 324 \| 1.2957 \| 0.7333 \| \| 0.0003 \| 13.0 \| 351 \| 1.3184 \| 0.7333 \| \| 0.0003 \| 14.0 \| 378 \| 1.3307 \| 0.7333 \| \| 0.0003 \| 15.0 \| 405 \| 1.3520 \| 0.7333 \| \| 0.0002 \| 16.0 \| 432 \| 1.3706 \| 0.7333 \| \| 0.0002 \| 17.0 \| 459 \| 1.3793 \| 0.7333 \| \| 0.0002 \| 18.0 \| 486 \| 1.3973 \| 0.7333 \| \| 0.0001 \| 19.0 \| 513 \| 1.4099 \| 0.7333 \| \| 0.0001 \| 20.0 \| 540 \| 1.4201 \| 0.7333 \| \| 0.0001 \| 21.0 \| 567 \| 1.4342 \| 0.7333 \| \| 0.0001 \| 22.0 \| 594 \| 1.4529 \| 0.7333 \| \| 0.0001 \| 23.0 \| 621 \| 1.4659 \| 0.7333 \| \| 0.0001 \| 24.0 \| 648 \| 1.4737 \| 0.7333 \| \| 0.0001 \| 25.0 \| 675 \| 1.4832 \| 0.7333 \| \| 0.0001 \| 26.0 \| 702 \| 1.5042 \| 0.7333 \| \| 0.0001 \| 27.0 \| 729 \| 1.5167 \| 0.7333 \| \| 0.0001 \| 28.0 \| 756 \| 1.5245 \| 0.7333 \| \| 0.0 \| 29.0 \| 783 \| 1.5454 \| 0.7333 \| \| 0.0 \| 30.0 \| 810 \| 1.5513 \| 0.7333 \| \| 0.0 \| 31.0 \| 837 \| 1.5670 \| 0.7333 \| \| 0.0 \| 32.0 \| 864 \| 1.5721 \| 0.7333 \| \| 0.0 \| 33.0 \| 891 \| 1.5854 \| 0.7333 \| \| 0.0 \| 34.0 \| 918 \| 1.5951 \| 0.7333 \| \| 0.0 \| 35.0 \| 945 \| 1.6025 \| 0.7333 \| \| 0.0 \| 36.0 \| 972 \| 1.6174 \| 0.7333 \| \| 0.0 \| 37.0 \| 999 \| 1.6316 \| 0.7333 \| \| 0.0 \| 38.0 \| 1026 \| 1.6382 \| 0.7333 \| \| 0.0 \| 39.0 \| 1053 \| 1.6457 \| 0.7333 \| \| 0.0 \| 40.0 \| 1080 \| 1.6550 \| 0.7333 \| \| 0.0 \| 41.0 \| 1107 \| 1.6656 \| 0.7333 \| \| 0.0 \| 42.0 \| 1134 \| 1.6708 \| 0.7333 \| \| 0.0 \| 43.0 \| 1161 \| 1.6757 \| 0.7333 \| \| 0.0 \| 44.0 \| 1188 \| 1.6846 \| 0.7333 \| \| 0.0 \| 45.0 \| 1215 \| 1.6907 \| 0.7333 \| \| 0.0 \| 46.0 \| 1242 \| 1.6942 \| 0.7333 \| \| 0.0 \| 47.0 \| 1269 \| 1.6967 \| 0.7333 \| \| 0.0 \| 48.0 \| 1296 \| 1.6977 \| 0.7333 \| \| 0.0 \| 49.0 \| 1323 \| 1.6978 \| 0.7333 \| \| 0.0 \| 50.0 \| 1350 \| 1.6978 \| 0.7333 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
phuong-tk-nguyen/resnet-50-finetuned-cifar10-finetuned-cifar10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-cifar10-finetuned-cifar10 This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.7363 - Accuracy: 0.561 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9934 \| 0.14 \| 10 \| 1.8738 \| 0.517 \| \| 1.9493 \| 0.28 \| 20 \| 1.8358 \| 0.532 \| \| 1.9328 \| 0.43 \| 30 \| 1.7941 \| 0.515 \| \| 1.9175 \| 0.57 \| 40 \| 1.7683 \| 0.531 \| \| 1.8875 \| 0.71 \| 50 \| 1.7649 \| 0.545 \| \| 1.8752 \| 0.85 \| 60 \| 1.7309 \| 0.559 \| \| 1.8881 \| 0.99 \| 70 \| 1.7363 \| 0.561 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
notepsk/food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # notepsk/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7870 - Validation Loss: 1.5762 - Train Accuracy: 0.869 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.7870 \| 1.5762 \| 0.869 \| 0 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
hkivancoral/hushem_5x_deit_base_rms_00001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_00001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6600 - Accuracy: 0.8837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7956 \| 1.0 \| 28 \| 0.6205 \| 0.7674 \| \| 0.1411 \| 2.0 \| 56 \| 0.3123 \| 0.8837 \| \| 0.014 \| 3.0 \| 84 \| 0.3844 \| 0.9070 \| \| 0.0034 \| 4.0 \| 112 \| 0.3408 \| 0.8837 \| \| 0.0022 \| 5.0 \| 140 \| 0.4472 \| 0.8605 \| \| 0.0015 \| 6.0 \| 168 \| 0.3917 \| 0.8605 \| \| 0.0011 \| 7.0 \| 196 \| 0.3836 \| 0.8837 \| \| 0.0008 \| 8.0 \| 224 \| 0.4047 \| 0.8837 \| \| 0.0006 \| 9.0 \| 252 \| 0.4079 \| 0.8605 \| \| 0.0005 \| 10.0 \| 280 \| 0.4138 \| 0.8837 \| \| 0.0004 \| 11.0 \| 308 \| 0.4271 \| 0.8837 \| \| 0.0004 \| 12.0 \| 336 \| 0.4048 \| 0.8837 \| \| 0.0003 \| 13.0 \| 364 \| 0.4452 \| 0.8837 \| \| 0.0002 \| 14.0 \| 392 \| 0.4491 \| 0.8837 \| \| 0.0002 \| 15.0 \| 420 \| 0.4640 \| 0.8837 \| \| 0.0002 \| 16.0 \| 448 \| 0.4755 \| 0.8837 \| \| 0.0002 \| 17.0 \| 476 \| 0.4421 \| 0.8837 \| \| 0.0001 \| 18.0 \| 504 \| 0.4868 \| 0.8837 \| \| 0.0001 \| 19.0 \| 532 \| 0.5095 \| 0.8837 \| \| 0.0001 \| 20.0 \| 560 \| 0.5094 \| 0.8837 \| \| 0.0001 \| 21.0 \| 588 \| 0.5135 \| 0.8837 \| \| 0.0001 \| 22.0 \| 616 \| 0.5162 \| 0.8837 \| \| 0.0001 \| 23.0 \| 644 \| 0.5296 \| 0.8837 \| \| 0.0001 \| 24.0 \| 672 \| 0.5403 \| 0.8837 \| \| 0.0001 \| 25.0 \| 700 \| 0.5417 \| 0.8837 \| \| 0.0001 \| 26.0 \| 728 \| 0.5505 \| 0.8837 \| \| 0.0 \| 27.0 \| 756 \| 0.5557 \| 0.8837 \| \| 0.0 \| 28.0 \| 784 \| 0.5868 \| 0.8837 \| \| 0.0 \| 29.0 \| 812 \| 0.5803 \| 0.8837 \| \| 0.0 \| 30.0 \| 840 \| 0.5730 \| 0.8837 \| \| 0.0 \| 31.0 \| 868 \| 0.5921 \| 0.8837 \| \| 0.0 \| 32.0 \| 896 \| 0.5971 \| 0.8837 \| \| 0.0 \| 33.0 \| 924 \| 0.5949 \| 0.8837 \| \| 0.0 \| 34.0 \| 952 \| 0.6083 \| 0.8837 \| \| 0.0 \| 35.0 \| 980 \| 0.5834 \| 0.8837 \| \| 0.0 \| 36.0 \| 1008 \| 0.6025 \| 0.8605 \| \| 0.0 \| 37.0 \| 1036 \| 0.6316 \| 0.8837 \| \| 0.0 \| 38.0 \| 1064 \| 0.6619 \| 0.8837 \| \| 0.0 \| 39.0 \| 1092 \| 0.6540 \| 0.8837 \| \| 0.0 \| 40.0 \| 1120 \| 0.6507 \| 0.8837 \| \| 0.0 \| 41.0 \| 1148 \| 0.6507 \| 0.8837 \| \| 0.0 \| 42.0 \| 1176 \| 0.6547 \| 0.8837 \| \| 0.0 \| 43.0 \| 1204 \| 0.6523 \| 0.8837 \| \| 0.0 \| 44.0 \| 1232 \| 0.6524 \| 0.8837 \| \| 0.0 \| 45.0 \| 1260 \| 0.6538 \| 0.8837 \| \| 0.0 \| 46.0 \| 1288 \| 0.6554 \| 0.8837 \| \| 0.0 \| 47.0 \| 1316 \| 0.6605 \| 0.8837 \| \| 0.0 \| 48.0 \| 1344 \| 0.6600 \| 0.8837 \| \| 0.0 \| 49.0 \| 1372 \| 0.6600 \| 0.8837 \| \| 0.0 \| 50.0 \| 1400 \| 0.6600 \| 0.8837 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_00001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_00001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1652 - Accuracy: 0.9524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8112 \| 1.0 \| 28 \| 0.6674 \| 0.6667 \| \| 0.1706 \| 2.0 \| 56 \| 0.2999 \| 0.8810 \| \| 0.0445 \| 3.0 \| 84 \| 0.2095 \| 0.9524 \| \| 0.0064 \| 4.0 \| 112 \| 0.0984 \| 0.9762 \| \| 0.0024 \| 5.0 \| 140 \| 0.0814 \| 0.9762 \| \| 0.0019 \| 6.0 \| 168 \| 0.0875 \| 0.9762 \| \| 0.0013 \| 7.0 \| 196 \| 0.1085 \| 0.9762 \| \| 0.001 \| 8.0 \| 224 \| 0.0872 \| 0.9762 \| \| 0.0008 \| 9.0 \| 252 \| 0.0829 \| 0.9762 \| \| 0.0006 \| 10.0 \| 280 \| 0.0900 \| 0.9762 \| \| 0.0005 \| 11.0 \| 308 \| 0.0916 \| 0.9762 \| \| 0.0004 \| 12.0 \| 336 \| 0.0984 \| 0.9762 \| \| 0.0004 \| 13.0 \| 364 \| 0.0990 \| 0.9762 \| \| 0.0003 \| 14.0 \| 392 \| 0.0990 \| 0.9762 \| \| 0.0003 \| 15.0 \| 420 \| 0.0986 \| 0.9762 \| \| 0.0002 \| 16.0 \| 448 \| 0.1009 \| 0.9762 \| \| 0.0002 \| 17.0 \| 476 \| 0.1045 \| 0.9762 \| \| 0.0002 \| 18.0 \| 504 \| 0.1034 \| 0.9762 \| \| 0.0001 \| 19.0 \| 532 \| 0.1105 \| 0.9762 \| \| 0.0001 \| 20.0 \| 560 \| 0.1077 \| 0.9762 \| \| 0.0001 \| 21.0 \| 588 \| 0.1166 \| 0.9762 \| \| 0.0001 \| 22.0 \| 616 \| 0.1228 \| 0.9762 \| \| 0.0001 \| 23.0 \| 644 \| 0.1152 \| 0.9762 \| \| 0.0001 \| 24.0 \| 672 \| 0.1166 \| 0.9762 \| \| 0.0001 \| 25.0 \| 700 \| 0.1199 \| 0.9762 \| \| 0.0001 \| 26.0 \| 728 \| 0.1209 \| 0.9762 \| \| 0.0001 \| 27.0 \| 756 \| 0.1278 \| 0.9762 \| \| 0.0001 \| 28.0 \| 784 \| 0.1240 \| 0.9762 \| \| 0.0 \| 29.0 \| 812 \| 0.1343 \| 0.9762 \| \| 0.0 \| 30.0 \| 840 \| 0.1301 \| 0.9762 \| \| 0.0 \| 31.0 \| 868 \| 0.1433 \| 0.9762 \| \| 0.0 \| 32.0 \| 896 \| 0.1322 \| 0.9762 \| \| 0.0 \| 33.0 \| 924 \| 0.1376 \| 0.9762 \| \| 0.0 \| 34.0 \| 952 \| 0.1447 \| 0.9524 \| \| 0.0 \| 35.0 \| 980 \| 0.1392 \| 0.9762 \| \| 0.0 \| 36.0 \| 1008 \| 0.1480 \| 0.9524 \| \| 0.0 \| 37.0 \| 1036 \| 0.1479 \| 0.9762 \| \| 0.0 \| 38.0 \| 1064 \| 0.1555 \| 0.9524 \| \| 0.0 \| 39.0 \| 1092 \| 0.1571 \| 0.9524 \| \| 0.0 \| 40.0 \| 1120 \| 0.1585 \| 0.9524 \| \| 0.0 \| 41.0 \| 1148 \| 0.1656 \| 0.9524 \| \| 0.0 \| 42.0 \| 1176 \| 0.1596 \| 0.9524 \| \| 0.0 \| 43.0 \| 1204 \| 0.1611 \| 0.9524 \| \| 0.0 \| 44.0 \| 1232 \| 0.1624 \| 0.9524 \| \| 0.0 \| 45.0 \| 1260 \| 0.1633 \| 0.9524 \| \| 0.0 \| 46.0 \| 1288 \| 0.1648 \| 0.9524 \| \| 0.0 \| 47.0 \| 1316 \| 0.1649 \| 0.9524 \| \| 0.0 \| 48.0 \| 1344 \| 0.1652 \| 0.9524 \| \| 0.0 \| 49.0 \| 1372 \| 0.1652 \| 0.9524 \| \| 0.0 \| 50.0 \| 1400 \| 0.1652 \| 0.9524 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_00001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_00001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6351 - Accuracy: 0.9024 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8457 \| 1.0 \| 28 \| 0.5947 \| 0.7317 \| \| 0.1932 \| 2.0 \| 56 \| 0.3789 \| 0.8780 \| \| 0.0378 \| 3.0 \| 84 \| 0.3371 \| 0.9024 \| \| 0.0061 \| 4.0 \| 112 \| 0.3727 \| 0.9024 \| \| 0.0027 \| 5.0 \| 140 \| 0.3487 \| 0.9024 \| \| 0.0018 \| 6.0 \| 168 \| 0.3750 \| 0.9024 \| \| 0.0012 \| 7.0 \| 196 \| 0.3872 \| 0.9024 \| \| 0.0009 \| 8.0 \| 224 \| 0.3976 \| 0.9024 \| \| 0.0007 \| 9.0 \| 252 \| 0.4053 \| 0.9024 \| \| 0.0006 \| 10.0 \| 280 \| 0.4125 \| 0.9024 \| \| 0.0005 \| 11.0 \| 308 \| 0.4192 \| 0.9024 \| \| 0.0004 \| 12.0 \| 336 \| 0.4329 \| 0.9024 \| \| 0.0003 \| 13.0 \| 364 \| 0.4400 \| 0.9024 \| \| 0.0003 \| 14.0 \| 392 \| 0.4408 \| 0.9024 \| \| 0.0002 \| 15.0 \| 420 \| 0.4473 \| 0.9024 \| \| 0.0002 \| 16.0 \| 448 \| 0.4630 \| 0.9024 \| \| 0.0002 \| 17.0 \| 476 \| 0.4703 \| 0.9024 \| \| 0.0002 \| 18.0 \| 504 \| 0.4685 \| 0.9024 \| \| 0.0001 \| 19.0 \| 532 \| 0.4848 \| 0.9024 \| \| 0.0001 \| 20.0 \| 560 \| 0.5034 \| 0.9024 \| \| 0.0001 \| 21.0 \| 588 \| 0.5008 \| 0.9024 \| \| 0.0001 \| 22.0 \| 616 \| 0.5129 \| 0.9024 \| \| 0.0001 \| 23.0 \| 644 \| 0.5167 \| 0.9024 \| \| 0.0001 \| 24.0 \| 672 \| 0.5213 \| 0.9024 \| \| 0.0001 \| 25.0 \| 700 \| 0.5209 \| 0.9024 \| \| 0.0001 \| 26.0 \| 728 \| 0.5340 \| 0.9024 \| \| 0.0001 \| 27.0 \| 756 \| 0.5439 \| 0.9024 \| \| 0.0 \| 28.0 \| 784 \| 0.5491 \| 0.9024 \| \| 0.0 \| 29.0 \| 812 \| 0.5502 \| 0.9024 \| \| 0.0 \| 30.0 \| 840 \| 0.5577 \| 0.9024 \| \| 0.0 \| 31.0 \| 868 \| 0.5662 \| 0.9024 \| \| 0.0 \| 32.0 \| 896 \| 0.5801 \| 0.9024 \| \| 0.0 \| 33.0 \| 924 \| 0.5760 \| 0.9024 \| \| 0.0 \| 34.0 \| 952 \| 0.5820 \| 0.9024 \| \| 0.0 \| 35.0 \| 980 \| 0.5825 \| 0.9024 \| \| 0.0 \| 36.0 \| 1008 \| 0.5963 \| 0.9024 \| \| 0.0 \| 37.0 \| 1036 \| 0.6052 \| 0.9024 \| \| 0.0 \| 38.0 \| 1064 \| 0.6015 \| 0.9024 \| \| 0.0 \| 39.0 \| 1092 \| 0.6109 \| 0.9024 \| \| 0.0 \| 40.0 \| 1120 \| 0.6162 \| 0.9024 \| \| 0.0 \| 41.0 \| 1148 \| 0.6213 \| 0.9024 \| \| 0.0 \| 42.0 \| 1176 \| 0.6284 \| 0.9024 \| \| 0.0 \| 43.0 \| 1204 \| 0.6259 \| 0.9024 \| \| 0.0 \| 44.0 \| 1232 \| 0.6257 \| 0.9024 \| \| 0.0 \| 45.0 \| 1260 \| 0.6306 \| 0.9024 \| \| 0.0 \| 46.0 \| 1288 \| 0.6336 \| 0.9024 \| \| 0.0 \| 47.0 \| 1316 \| 0.6353 \| 0.9024 \| \| 0.0 \| 48.0 \| 1344 \| 0.6351 \| 0.9024 \| \| 0.0 \| 49.0 \| 1372 \| 0.6351 \| 0.9024 \| \| 0.0 \| 50.0 \| 1400 \| 0.6351 \| 0.9024 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_0001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_0001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.9961 - Accuracy: 0.7111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4401 \| 1.0 \| 27 \| 1.3889 \| 0.2444 \| \| 1.4795 \| 2.0 \| 54 \| 1.6032 \| 0.2444 \| \| 1.2229 \| 3.0 \| 81 \| 1.1436 \| 0.5111 \| \| 0.8987 \| 4.0 \| 108 \| 1.0040 \| 0.5556 \| \| 0.4853 \| 5.0 \| 135 \| 1.0534 \| 0.6222 \| \| 0.1456 \| 6.0 \| 162 \| 1.8360 \| 0.5556 \| \| 0.0696 \| 7.0 \| 189 \| 1.2156 \| 0.7333 \| \| 0.0874 \| 8.0 \| 216 \| 0.7950 \| 0.7556 \| \| 0.0365 \| 9.0 \| 243 \| 1.6830 \| 0.7111 \| \| 0.0006 \| 10.0 \| 270 \| 1.6730 \| 0.7111 \| \| 0.0002 \| 11.0 \| 297 \| 1.6991 \| 0.7111 \| \| 0.0002 \| 12.0 \| 324 \| 1.7182 \| 0.7111 \| \| 0.0001 \| 13.0 \| 351 \| 1.7320 \| 0.7111 \| \| 0.0001 \| 14.0 \| 378 \| 1.7414 \| 0.7111 \| \| 0.0001 \| 15.0 \| 405 \| 1.7505 \| 0.7111 \| \| 0.0001 \| 16.0 \| 432 \| 1.7579 \| 0.7111 \| \| 0.0001 \| 17.0 \| 459 \| 1.7666 \| 0.7111 \| \| 0.0001 \| 18.0 \| 486 \| 1.7749 \| 0.7111 \| \| 0.0001 \| 19.0 \| 513 \| 1.7836 \| 0.7333 \| \| 0.0 \| 20.0 \| 540 \| 1.7919 \| 0.7333 \| \| 0.0 \| 21.0 \| 567 \| 1.8002 \| 0.7111 \| \| 0.0 \| 22.0 \| 594 \| 1.8101 \| 0.7111 \| \| 0.0 \| 23.0 \| 621 \| 1.8191 \| 0.7111 \| \| 0.0 \| 24.0 \| 648 \| 1.8264 \| 0.7111 \| \| 0.0 \| 25.0 \| 675 \| 1.8362 \| 0.7111 \| \| 0.0 \| 26.0 \| 702 \| 1.8441 \| 0.7111 \| \| 0.0 \| 27.0 \| 729 \| 1.8521 \| 0.7111 \| \| 0.0 \| 28.0 \| 756 \| 1.8613 \| 0.7111 \| \| 0.0 \| 29.0 \| 783 \| 1.8701 \| 0.7111 \| \| 0.0 \| 30.0 \| 810 \| 1.8780 \| 0.7111 \| \| 0.0 \| 31.0 \| 837 \| 1.8862 \| 0.7111 \| \| 0.0 \| 32.0 \| 864 \| 1.8953 \| 0.7111 \| \| 0.0 \| 33.0 \| 891 \| 1.9042 \| 0.7111 \| \| 0.0 \| 34.0 \| 918 \| 1.9125 \| 0.7111 \| \| 0.0 \| 35.0 \| 945 \| 1.9206 \| 0.7111 \| \| 0.0 \| 36.0 \| 972 \| 1.9289 \| 0.7111 \| \| 0.0 \| 37.0 \| 999 \| 1.9371 \| 0.7111 \| \| 0.0 \| 38.0 \| 1026 \| 1.9452 \| 0.7111 \| \| 0.0 \| 39.0 \| 1053 \| 1.9530 \| 0.7111 \| \| 0.0 \| 40.0 \| 1080 \| 1.9602 \| 0.7111 \| \| 0.0 \| 41.0 \| 1107 \| 1.9674 \| 0.7111 \| \| 0.0 \| 42.0 \| 1134 \| 1.9741 \| 0.7111 \| \| 0.0 \| 43.0 \| 1161 \| 1.9798 \| 0.7111 \| \| 0.0 \| 44.0 \| 1188 \| 1.9852 \| 0.7111 \| \| 0.0 \| 45.0 \| 1215 \| 1.9896 \| 0.7111 \| \| 0.0 \| 46.0 \| 1242 \| 1.9931 \| 0.7111 \| \| 0.0 \| 47.0 \| 1269 \| 1.9953 \| 0.7111 \| \| 0.0 \| 48.0 \| 1296 \| 1.9961 \| 0.7111 \| \| 0.0 \| 49.0 \| 1323 \| 1.9961 \| 0.7111 \| \| 0.0 \| 50.0 \| 1350 \| 1.9961 \| 0.7111 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_0001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_0001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.1764 - Accuracy: 0.5333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4732 \| 1.0 \| 27 \| 1.5871 \| 0.2667 \| \| 1.4137 \| 2.0 \| 54 \| 1.4271 \| 0.2667 \| \| 1.462 \| 3.0 \| 81 \| 1.4098 \| 0.2667 \| \| 1.4423 \| 4.0 \| 108 \| 1.4316 \| 0.2444 \| \| 1.4677 \| 5.0 \| 135 \| 1.1736 \| 0.6 \| \| 1.1753 \| 6.0 \| 162 \| 1.3090 \| 0.4889 \| \| 1.0628 \| 7.0 \| 189 \| 1.1008 \| 0.4 \| \| 0.8856 \| 8.0 \| 216 \| 1.3194 \| 0.4667 \| \| 0.7266 \| 9.0 \| 243 \| 1.5517 \| 0.4667 \| \| 0.7206 \| 10.0 \| 270 \| 1.5964 \| 0.4222 \| \| 0.6825 \| 11.0 \| 297 \| 1.9511 \| 0.5333 \| \| 0.6024 \| 12.0 \| 324 \| 1.1289 \| 0.5111 \| \| 0.7093 \| 13.0 \| 351 \| 1.6051 \| 0.4667 \| \| 0.5446 \| 14.0 \| 378 \| 1.0604 \| 0.5333 \| \| 0.4716 \| 15.0 \| 405 \| 2.6293 \| 0.5778 \| \| 0.4728 \| 16.0 \| 432 \| 3.2908 \| 0.4889 \| \| 0.5099 \| 17.0 \| 459 \| 2.0246 \| 0.5333 \| \| 0.4809 \| 18.0 \| 486 \| 3.4545 \| 0.5333 \| \| 0.3484 \| 19.0 \| 513 \| 2.2451 \| 0.5111 \| \| 0.352 \| 20.0 \| 540 \| 2.8572 \| 0.4889 \| \| 0.3258 \| 21.0 \| 567 \| 3.5970 \| 0.5556 \| \| 0.2785 \| 22.0 \| 594 \| 3.6404 \| 0.5556 \| \| 0.3005 \| 23.0 \| 621 \| 3.6333 \| 0.5111 \| \| 0.2089 \| 24.0 \| 648 \| 4.2561 \| 0.5333 \| \| 0.1996 \| 25.0 \| 675 \| 3.8526 \| 0.5111 \| \| 0.1044 \| 26.0 \| 702 \| 4.1245 \| 0.5333 \| \| 0.2042 \| 27.0 \| 729 \| 3.9154 \| 0.5556 \| \| 0.1371 \| 28.0 \| 756 \| 3.3906 \| 0.5556 \| \| 0.1014 \| 29.0 \| 783 \| 4.2534 \| 0.5556 \| \| 0.0761 \| 30.0 \| 810 \| 3.8328 \| 0.5778 \| \| 0.0321 \| 31.0 \| 837 \| 4.5117 \| 0.5556 \| \| 0.1194 \| 32.0 \| 864 \| 4.5296 \| 0.5333 \| \| 0.0072 \| 33.0 \| 891 \| 4.9299 \| 0.5333 \| \| 0.0276 \| 34.0 \| 918 \| 5.0433 \| 0.5111 \| \| 0.0121 \| 35.0 \| 945 \| 4.9519 \| 0.5333 \| \| 0.0051 \| 36.0 \| 972 \| 4.9546 \| 0.5333 \| \| 0.0001 \| 37.0 \| 999 \| 4.9700 \| 0.5111 \| \| 0.0001 \| 38.0 \| 1026 \| 4.9962 \| 0.5111 \| \| 0.0 \| 39.0 \| 1053 \| 5.0319 \| 0.5111 \| \| 0.0 \| 40.0 \| 1080 \| 5.0566 \| 0.5111 \| \| 0.0001 \| 41.0 \| 1107 \| 5.0812 \| 0.5333 \| \| 0.0 \| 42.0 \| 1134 \| 5.1051 \| 0.5333 \| \| 0.0 \| 43.0 \| 1161 \| 5.1228 \| 0.5333 \| \| 0.0 \| 44.0 \| 1188 \| 5.1393 \| 0.5333 \| \| 0.0 \| 45.0 \| 1215 \| 5.1531 \| 0.5333 \| \| 0.0 \| 46.0 \| 1242 \| 5.1647 \| 0.5333 \| \| 0.0 \| 47.0 \| 1269 \| 5.1724 \| 0.5333 \| \| 0.0 \| 48.0 \| 1296 \| 5.1763 \| 0.5333 \| \| 0.0 \| 49.0 \| 1323 \| 5.1764 \| 0.5333 \| \| 0.0 \| 50.0 \| 1350 \| 5.1764 \| 0.5333 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_0001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_0001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4738 - Accuracy: 0.7907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4909 \| 1.0 \| 28 \| 1.4982 \| 0.2558 \| \| 1.0101 \| 2.0 \| 56 \| 0.9495 \| 0.6744 \| \| 0.4042 \| 3.0 \| 84 \| 0.4221 \| 0.7442 \| \| 0.1388 \| 4.0 \| 112 \| 0.7310 \| 0.7442 \| \| 0.1089 \| 5.0 \| 140 \| 1.2245 \| 0.8140 \| \| 0.0672 \| 6.0 \| 168 \| 0.8270 \| 0.7907 \| \| 0.0498 \| 7.0 \| 196 \| 0.7672 \| 0.7674 \| \| 0.0008 \| 8.0 \| 224 \| 1.0591 \| 0.7907 \| \| 0.0002 \| 9.0 \| 252 \| 1.0196 \| 0.8140 \| \| 0.0001 \| 10.0 \| 280 \| 1.0272 \| 0.8140 \| \| 0.0001 \| 11.0 \| 308 \| 1.0400 \| 0.8140 \| \| 0.0001 \| 12.0 \| 336 \| 1.0507 \| 0.8140 \| \| 0.0001 \| 13.0 \| 364 \| 1.0623 \| 0.7907 \| \| 0.0001 \| 14.0 \| 392 \| 1.0738 \| 0.7907 \| \| 0.0001 \| 15.0 \| 420 \| 1.0809 \| 0.7907 \| \| 0.0 \| 16.0 \| 448 \| 1.0928 \| 0.7907 \| \| 0.0 \| 17.0 \| 476 \| 1.1047 \| 0.7907 \| \| 0.0 \| 18.0 \| 504 \| 1.1192 \| 0.7907 \| \| 0.0 \| 19.0 \| 532 \| 1.1317 \| 0.7907 \| \| 0.0 \| 20.0 \| 560 \| 1.1436 \| 0.7907 \| \| 0.0 \| 21.0 \| 588 \| 1.1568 \| 0.7907 \| \| 0.0 \| 22.0 \| 616 \| 1.1654 \| 0.7907 \| \| 0.0 \| 23.0 \| 644 \| 1.1790 \| 0.7907 \| \| 0.0 \| 24.0 \| 672 \| 1.1944 \| 0.7907 \| \| 0.0 \| 25.0 \| 700 \| 1.2077 \| 0.7907 \| \| 0.0 \| 26.0 \| 728 \| 1.2229 \| 0.7907 \| \| 0.0 \| 27.0 \| 756 \| 1.2379 \| 0.7907 \| \| 0.0 \| 28.0 \| 784 \| 1.2518 \| 0.7907 \| \| 0.0 \| 29.0 \| 812 \| 1.2649 \| 0.7907 \| \| 0.0 \| 30.0 \| 840 \| 1.2762 \| 0.7907 \| \| 0.0 \| 31.0 \| 868 \| 1.2927 \| 0.7907 \| \| 0.0 \| 32.0 \| 896 \| 1.3064 \| 0.7907 \| \| 0.0 \| 33.0 \| 924 \| 1.3200 \| 0.7907 \| \| 0.0 \| 34.0 \| 952 \| 1.3332 \| 0.7907 \| \| 0.0 \| 35.0 \| 980 \| 1.3480 \| 0.7907 \| \| 0.0 \| 36.0 \| 1008 \| 1.3592 \| 0.7907 \| \| 0.0 \| 37.0 \| 1036 \| 1.3743 \| 0.7907 \| \| 0.0 \| 38.0 \| 1064 \| 1.3941 \| 0.7907 \| \| 0.0 \| 39.0 \| 1092 \| 1.4057 \| 0.7907 \| \| 0.0 \| 40.0 \| 1120 \| 1.4180 \| 0.7907 \| \| 0.0 \| 41.0 \| 1148 \| 1.4282 \| 0.7907 \| \| 0.0 \| 42.0 \| 1176 \| 1.4383 \| 0.7907 \| \| 0.0 \| 43.0 \| 1204 \| 1.4471 \| 0.7907 \| \| 0.0 \| 44.0 \| 1232 \| 1.4565 \| 0.7907 \| \| 0.0 \| 45.0 \| 1260 \| 1.4629 \| 0.7907 \| \| 0.0 \| 46.0 \| 1288 \| 1.4680 \| 0.7907 \| \| 0.0 \| 47.0 \| 1316 \| 1.4718 \| 0.7907 \| \| 0.0 \| 48.0 \| 1344 \| 1.4738 \| 0.7907 \| \| 0.0 \| 49.0 \| 1372 \| 1.4738 \| 0.7907 \| \| 0.0 \| 50.0 \| 1400 \| 1.4738 \| 0.7907 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_5x_deit_base_rms_0001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_0001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5888 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6398 \| 1.0 \| 28 \| 1.4620 \| 0.2381 \| \| 1.4471 \| 2.0 \| 56 \| 1.4867 \| 0.2619 \| \| 1.4043 \| 3.0 \| 84 \| 1.4639 \| 0.2381 \| \| 1.6225 \| 4.0 \| 112 \| 1.1986 \| 0.4524 \| \| 1.0459 \| 5.0 \| 140 \| 1.1310 \| 0.4762 \| \| 0.7275 \| 6.0 \| 168 \| 0.7753 \| 0.6429 \| \| 0.4185 \| 7.0 \| 196 \| 0.5503 \| 0.7857 \| \| 0.2249 \| 8.0 \| 224 \| 0.5491 \| 0.8571 \| \| 0.0749 \| 9.0 \| 252 \| 0.2650 \| 0.9286 \| \| 0.0643 \| 10.0 \| 280 \| 0.5070 \| 0.8333 \| \| 0.083 \| 11.0 \| 308 \| 0.5183 \| 0.8810 \| \| 0.0258 \| 12.0 \| 336 \| 0.5166 \| 0.8571 \| \| 0.0004 \| 13.0 \| 364 \| 0.4395 \| 0.9524 \| \| 0.03 \| 14.0 \| 392 \| 0.5344 \| 0.9048 \| \| 0.0374 \| 15.0 \| 420 \| 1.0859 \| 0.8095 \| \| 0.032 \| 16.0 \| 448 \| 0.4372 \| 0.9048 \| \| 0.0018 \| 17.0 \| 476 \| 0.4691 \| 0.9048 \| \| 0.0319 \| 18.0 \| 504 \| 0.5620 \| 0.8810 \| \| 0.022 \| 19.0 \| 532 \| 0.4782 \| 0.9048 \| \| 0.0002 \| 20.0 \| 560 \| 0.4687 \| 0.9048 \| \| 0.0001 \| 21.0 \| 588 \| 0.4749 \| 0.9048 \| \| 0.0001 \| 22.0 \| 616 \| 0.4799 \| 0.9048 \| \| 0.0001 \| 23.0 \| 644 \| 0.4865 \| 0.9048 \| \| 0.0001 \| 24.0 \| 672 \| 0.4924 \| 0.9048 \| \| 0.0001 \| 25.0 \| 700 \| 0.4977 \| 0.9048 \| \| 0.0001 \| 26.0 \| 728 \| 0.5030 \| 0.9048 \| \| 0.0 \| 27.0 \| 756 \| 0.5085 \| 0.9048 \| \| 0.0 \| 28.0 \| 784 \| 0.5132 \| 0.9048 \| \| 0.0 \| 29.0 \| 812 \| 0.5184 \| 0.9048 \| \| 0.0 \| 30.0 \| 840 \| 0.5233 \| 0.9048 \| \| 0.0 \| 31.0 \| 868 \| 0.5283 \| 0.9048 \| \| 0.0 \| 32.0 \| 896 \| 0.5333 \| 0.9048 \| \| 0.0 \| 33.0 \| 924 \| 0.5383 \| 0.9048 \| \| 0.0 \| 34.0 \| 952 \| 0.5430 \| 0.9048 \| \| 0.0 \| 35.0 \| 980 \| 0.5476 \| 0.9048 \| \| 0.0 \| 36.0 \| 1008 \| 0.5522 \| 0.9048 \| \| 0.0 \| 37.0 \| 1036 \| 0.5569 \| 0.9048 \| \| 0.0 \| 38.0 \| 1064 \| 0.5613 \| 0.9048 \| \| 0.0 \| 39.0 \| 1092 \| 0.5655 \| 0.9048 \| \| 0.0 \| 40.0 \| 1120 \| 0.5694 \| 0.9048 \| \| 0.0 \| 41.0 \| 1148 \| 0.5725 \| 0.9048 \| \| 0.0 \| 42.0 \| 1176 \| 0.5761 \| 0.9048 \| \| 0.0 \| 43.0 \| 1204 \| 0.5794 \| 0.9048 \| \| 0.0 \| 44.0 \| 1232 \| 0.5824 \| 0.9048 \| \| 0.0 \| 45.0 \| 1260 \| 0.5848 \| 0.9048 \| \| 0.0 \| 46.0 \| 1288 \| 0.5868 \| 0.9048 \| \| 0.0 \| 47.0 \| 1316 \| 0.5882 \| 0.9048 \| \| 0.0 \| 48.0 \| 1344 \| 0.5888 \| 0.9048 \| \| 0.0 \| 49.0 \| 1372 \| 0.5888 \| 0.9048 \| \| 0.0 \| 50.0 \| 1400 \| 0.5888 \| 0.9048 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
harrytechiz/vit-base-patch16-224-blur_vs_clean	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-blur_vs_clean This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0714 - Accuracy: 0.9754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0539 \| 1.0 \| 151 \| 0.1078 \| 0.9596 \| \| 0.0611 \| 2.0 \| 302 \| 0.0846 \| 0.9698 \| \| 0.049 \| 3.0 \| 453 \| 0.0714 \| 0.9754 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3	[ "blur", "clean" ]
phuong-tk-nguyen/vit-base-patch16-224-newly-trained	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-newly-trained This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - Accuracy: 0.964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2183 \| 0.14 \| 10 \| 1.6296 \| 0.629 \| \| 1.4213 \| 0.28 \| 20 \| 0.8637 \| 0.899 \| \| 0.86 \| 0.43 \| 30 \| 0.4598 \| 0.949 \| \| 0.614 \| 0.57 \| 40 \| 0.2998 \| 0.96 \| \| 0.48 \| 0.71 \| 50 \| 0.2337 \| 0.967 \| \| 0.4123 \| 0.85 \| 60 \| 0.2091 \| 0.964 \| \| 0.4511 \| 0.99 \| 70 \| 0.1996 \| 0.964 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
hkivancoral/hushem_5x_deit_base_rms_0001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_deit_base_rms_0001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3118 - Accuracy: 0.8537 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5446 \| 1.0 \| 28 \| 1.3850 \| 0.2195 \| \| 1.371 \| 2.0 \| 56 \| 1.0037 \| 0.4878 \| \| 0.7358 \| 3.0 \| 84 \| 0.5519 \| 0.7561 \| \| 0.2869 \| 4.0 \| 112 \| 0.6592 \| 0.7561 \| \| 0.1411 \| 5.0 \| 140 \| 0.6324 \| 0.8780 \| \| 0.0266 \| 6.0 \| 168 \| 0.8126 \| 0.8049 \| \| 0.0011 \| 7.0 \| 196 \| 0.7003 \| 0.8537 \| \| 0.0012 \| 8.0 \| 224 \| 1.2708 \| 0.8049 \| \| 0.0223 \| 9.0 \| 252 \| 0.7784 \| 0.8780 \| \| 0.0109 \| 10.0 \| 280 \| 1.2289 \| 0.7805 \| \| 0.0002 \| 11.0 \| 308 \| 0.9688 \| 0.8537 \| \| 0.03 \| 12.0 \| 336 \| 0.8929 \| 0.8537 \| \| 0.0037 \| 13.0 \| 364 \| 0.7649 \| 0.8537 \| \| 0.0119 \| 14.0 \| 392 \| 0.9677 \| 0.8049 \| \| 0.0001 \| 15.0 \| 420 \| 1.0107 \| 0.7805 \| \| 0.0001 \| 16.0 \| 448 \| 1.0261 \| 0.7805 \| \| 0.0001 \| 17.0 \| 476 \| 1.0390 \| 0.7805 \| \| 0.0001 \| 18.0 \| 504 \| 1.0514 \| 0.7805 \| \| 0.0001 \| 19.0 \| 532 \| 1.0626 \| 0.7805 \| \| 0.0 \| 20.0 \| 560 \| 1.0741 \| 0.7805 \| \| 0.0 \| 21.0 \| 588 \| 1.0847 \| 0.7805 \| \| 0.0 \| 22.0 \| 616 \| 1.0958 \| 0.7805 \| \| 0.0 \| 23.0 \| 644 \| 1.1069 \| 0.7805 \| \| 0.0 \| 24.0 \| 672 \| 1.1169 \| 0.7805 \| \| 0.0 \| 25.0 \| 700 \| 1.1262 \| 0.8049 \| \| 0.0 \| 26.0 \| 728 \| 1.1359 \| 0.8049 \| \| 0.0 \| 27.0 \| 756 \| 1.1455 \| 0.8049 \| \| 0.0 \| 28.0 \| 784 \| 1.1554 \| 0.8049 \| \| 0.0 \| 29.0 \| 812 \| 1.1647 \| 0.8049 \| \| 0.0 \| 30.0 \| 840 \| 1.1746 \| 0.8049 \| \| 0.0 \| 31.0 \| 868 \| 1.1846 \| 0.8049 \| \| 0.0 \| 32.0 \| 896 \| 1.1951 \| 0.8049 \| \| 0.0 \| 33.0 \| 924 \| 1.2053 \| 0.8293 \| \| 0.0 \| 34.0 \| 952 \| 1.2145 \| 0.8293 \| \| 0.0 \| 35.0 \| 980 \| 1.2243 \| 0.8537 \| \| 0.0 \| 36.0 \| 1008 \| 1.2340 \| 0.8537 \| \| 0.0 \| 37.0 \| 1036 \| 1.2436 \| 0.8537 \| \| 0.0 \| 38.0 \| 1064 \| 1.2528 \| 0.8537 \| \| 0.0 \| 39.0 \| 1092 \| 1.2615 \| 0.8537 \| \| 0.0 \| 40.0 \| 1120 \| 1.2699 \| 0.8537 \| \| 0.0 \| 41.0 \| 1148 \| 1.2781 \| 0.8537 \| \| 0.0 \| 42.0 \| 1176 \| 1.2859 \| 0.8537 \| \| 0.0 \| 43.0 \| 1204 \| 1.2920 \| 0.8537 \| \| 0.0 \| 44.0 \| 1232 \| 1.2978 \| 0.8537 \| \| 0.0 \| 45.0 \| 1260 \| 1.3031 \| 0.8537 \| \| 0.0 \| 46.0 \| 1288 \| 1.3073 \| 0.8537 \| \| 0.0 \| 47.0 \| 1316 \| 1.3103 \| 0.8537 \| \| 0.0 \| 48.0 \| 1344 \| 1.3117 \| 0.8537 \| \| 0.0 \| 49.0 \| 1372 \| 1.3118 \| 0.8537 \| \| 0.0 \| 50.0 \| 1400 \| 1.3118 \| 0.8537 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
phuong-tk-nguyen/swin-base-patch4-window7-224-in22k-newly-trained	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-base-patch4-window7-224-in22k-newly-trained This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1335 - Accuracy: 0.959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2459 \| 0.14 \| 10 \| 1.7346 \| 0.575 \| \| 1.4338 \| 0.28 \| 20 \| 0.7222 \| 0.841 \| \| 0.8059 \| 0.43 \| 30 \| 0.3252 \| 0.915 \| \| 0.5772 \| 0.57 \| 40 \| 0.2071 \| 0.942 \| \| 0.5599 \| 0.71 \| 50 \| 0.1553 \| 0.958 \| \| 0.4473 \| 0.85 \| 60 \| 0.1373 \| 0.958 \| \| 0.4292 \| 0.99 \| 70 \| 0.1335 \| 0.959 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
MaksymDrobchak/vit-base-patch16-224-finetuned-flower	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon This model is a fine-tuned version of [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co/100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0971 - Accuracy: 0.9652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2504 \| 0.99 \| 20 \| 0.1401 \| 0.9512 \| \| 0.2051 \| 1.98 \| 40 \| 0.1083 \| 0.9652 \| \| 0.1894 \| 2.96 \| 60 \| 0.0939 \| 0.9652 \| \| 0.1115 \| 4.0 \| 81 \| 0.0880 \| 0.9686 \| \| 0.117 \| 4.94 \| 100 \| 0.0971 \| 0.9652 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "ambience", "hair_style", "manicure", "massage_room", "others", "pedicure" ]
shubhamWi91/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4049 - Accuracy: 0.8243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5093 \| 0.98 \| 37 \| 0.4578 \| 0.7776 \| \| 0.4411 \| 1.99 \| 75 \| 0.4189 \| 0.8131 \| \| 0.4177 \| 2.94 \| 111 \| 0.4049 \| 0.8243 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "cap", "no_cap" ]
dima806/card_type_image_detection	Returns card type given an image with about 66% accuracy. See https://www.kaggle.com/code/dima806/card-types-image-detection-vit for more details. ``` Classification report: precision recall f1-score support ace of clubs 0.8000 0.9474 0.8675 38 ace of diamonds 0.6604 0.9211 0.7692 38 ace of hearts 0.7727 0.8947 0.8293 38 ace of spades 0.6129 1.0000 0.7600 38 eight of clubs 0.6500 0.3421 0.4483 38 eight of diamonds 0.7500 0.5385 0.6269 39 eight of hearts 0.5000 0.1842 0.2692 38 eight of spades 0.7273 0.2105 0.3265 38 five of clubs 0.8438 0.6923 0.7606 39 five of diamonds 0.7750 0.8158 0.7949 38 five of hearts 0.7949 0.8158 0.8052 38 five of spades 0.7368 0.7368 0.7368 38 four of clubs 0.7333 0.8684 0.7952 38 four of diamonds 0.8571 0.6316 0.7273 38 four of hearts 0.7368 0.7368 0.7368 38 four of spades 0.9000 0.6923 0.7826 39 jack of clubs 0.7037 0.5000 0.5846 38 jack of diamonds 0.5806 0.4737 0.5217 38 jack of hearts 0.8889 0.2105 0.3404 38 jack of spades 0.4000 0.2051 0.2712 39 joker 0.9487 0.9737 0.9610 38 king of clubs 0.3721 0.8421 0.5161 38 king of diamonds 0.4865 0.9474 0.6429 38 king of hearts 0.5472 0.7436 0.6304 39 king of spades 0.4203 0.7632 0.5421 38 nine of clubs 0.5909 0.6842 0.6341 38 nine of diamonds 0.8095 0.4474 0.5763 38 nine of hearts 0.5455 0.6154 0.5783 39 nine of spades 0.4615 0.7895 0.5825 38 queen of clubs 0.2727 0.1538 0.1967 39 queen of diamonds 0.6250 0.1282 0.2128 39 queen of hearts 0.6216 0.6053 0.6133 38 queen of spades 0.7353 0.6579 0.6944 38 seven of clubs 0.5333 0.6316 0.5783 38 seven of diamonds 0.3571 0.3947 0.3750 38 seven of hearts 0.7143 0.7895 0.7500 38 seven of spades 0.7742 0.6316 0.6957 38 six of clubs 0.7368 0.7179 0.7273 39 six of diamonds 0.4462 0.7632 0.5631 38 six of hearts 0.8462 0.5789 0.6875 38 six of spades 0.7879 0.6842 0.7324 38 ten of clubs 0.8889 0.6316 0.7385 38 ten of diamonds 0.6136 0.7105 0.6585 38 ten of hearts 0.7021 0.8684 0.7765 38 ten of spades 0.8529 0.7632 0.8056 38 three of clubs 0.7561 0.7949 0.7750 39 three of diamonds 0.7419 0.6053 0.6667 38 three of hearts 0.7273 0.8205 0.7711 39 three of spades 0.6744 0.7632 0.7160 38 two of clubs 0.7179 0.7368 0.7273 38 two of diamonds 0.7667 0.6053 0.6765 38 two of hearts 0.7647 0.6842 0.7222 38 two of spades 0.7949 0.8158 0.8052 38 accuracy 0.6553 2025 macro avg 0.6804 0.6559 0.6431 2025 weighted avg 0.6802 0.6553 0.6427 2025 ```	[ "ace of clubs", "ace of diamonds", "ace of hearts", "ace of spades", "eight of clubs", "eight of diamonds", "eight of hearts", "eight of spades", "five of clubs", "five of diamonds", "five of hearts", "five of spades", "four of clubs", "four of diamonds", "four of hearts", "four of spades", "jack of clubs", "jack of diamonds", "jack of hearts", "jack of spades", "joker", "king of clubs", "king of diamonds", "king of hearts", "king of spades", "nine of clubs", "nine of diamonds", "nine of hearts", "nine of spades", "queen of clubs", "queen of diamonds", "queen of hearts", "queen of spades", "seven of clubs", "seven of diamonds", "seven of hearts", "seven of spades", "six of clubs", "six of diamonds", "six of hearts", "six of spades", "ten of clubs", "ten of diamonds", "ten of hearts", "ten of spades", "three of clubs", "three of diamonds", "three of hearts", "three of spades", "two of clubs", "two of diamonds", "two of hearts", "two of spades" ]
Zendel/my_awesome_food_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.5709 - Accuracy: 0.918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7167 \| 0.99 \| 62 \| 2.5215 \| 0.862 \| \| 1.8648 \| 2.0 \| 125 \| 1.7438 \| 0.891 \| \| 1.6405 \| 2.98 \| 186 \| 1.5709 \| 0.918 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
Artef/results	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [umm-maybe/AI-image-detector](https://huggingface.co/umm-maybe/AI-image-detector) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1365 - eval_accuracy: 0.9612 - eval_runtime: 20.7986 - eval_samples_per_second: 28.511 - eval_steps_per_second: 3.606 - epoch: 1.86 - step: 550 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.2	[ "artificial", "human" ]
hkivancoral/hushem_5x_beit_base_adamax_001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_5x_beit_base_adamax_001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 6.2787 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4418 \| 1.0 \| 27 \| 1.4079 \| 0.2444 \| \| 1.2606 \| 2.0 \| 54 \| 1.4566 \| 0.4 \| \| 1.1141 \| 3.0 \| 81 \| 1.4147 \| 0.3111 \| \| 0.9738 \| 4.0 \| 108 \| 1.7371 \| 0.3556 \| \| 0.7887 \| 5.0 \| 135 \| 1.5516 \| 0.3778 \| \| 0.7198 \| 6.0 \| 162 \| 1.3626 \| 0.4 \| \| 0.8269 \| 7.0 \| 189 \| 1.5448 \| 0.3778 \| \| 0.8171 \| 8.0 \| 216 \| 1.4576 \| 0.4 \| \| 0.7255 \| 9.0 \| 243 \| 2.3915 \| 0.3778 \| \| 0.6369 \| 10.0 \| 270 \| 1.6627 \| 0.3778 \| \| 0.6809 \| 11.0 \| 297 \| 1.5201 \| 0.3556 \| \| 0.6237 \| 12.0 \| 324 \| 1.3289 \| 0.4222 \| \| 0.6768 \| 13.0 \| 351 \| 1.6115 \| 0.3556 \| \| 0.6336 \| 14.0 \| 378 \| 2.0397 \| 0.3778 \| \| 0.5238 \| 15.0 \| 405 \| 1.5857 \| 0.3778 \| \| 0.5016 \| 16.0 \| 432 \| 1.4047 \| 0.4444 \| \| 0.4321 \| 17.0 \| 459 \| 2.2039 \| 0.3556 \| \| 0.4791 \| 18.0 \| 486 \| 2.3823 \| 0.3778 \| \| 0.484 \| 19.0 \| 513 \| 1.4706 \| 0.4222 \| \| 0.4812 \| 20.0 \| 540 \| 1.6485 \| 0.4222 \| \| 0.4413 \| 21.0 \| 567 \| 1.7092 \| 0.4 \| \| 0.4306 \| 22.0 \| 594 \| 1.8582 \| 0.4 \| \| 0.37 \| 23.0 \| 621 \| 1.8653 \| 0.3778 \| \| 0.3048 \| 24.0 \| 648 \| 1.6342 \| 0.4444 \| \| 0.3515 \| 25.0 \| 675 \| 1.5211 \| 0.4889 \| \| 0.3558 \| 26.0 \| 702 \| 1.9714 \| 0.4222 \| \| 0.2599 \| 27.0 \| 729 \| 1.7243 \| 0.4667 \| \| 0.267 \| 28.0 \| 756 \| 1.7049 \| 0.5111 \| \| 0.2625 \| 29.0 \| 783 \| 2.1704 \| 0.4222 \| \| 0.2368 \| 30.0 \| 810 \| 2.2942 \| 0.4667 \| \| 0.2036 \| 31.0 \| 837 \| 2.0691 \| 0.4667 \| \| 0.1938 \| 32.0 \| 864 \| 2.7340 \| 0.4 \| \| 0.1597 \| 33.0 \| 891 \| 3.0661 \| 0.4 \| \| 0.1166 \| 34.0 \| 918 \| 2.8536 \| 0.4667 \| \| 0.1248 \| 35.0 \| 945 \| 2.9508 \| 0.4444 \| \| 0.121 \| 36.0 \| 972 \| 3.2153 \| 0.4667 \| \| 0.0801 \| 37.0 \| 999 \| 3.0021 \| 0.4222 \| \| 0.0529 \| 38.0 \| 1026 \| 3.3247 \| 0.4222 \| \| 0.0434 \| 39.0 \| 1053 \| 4.0394 \| 0.4667 \| \| 0.0599 \| 40.0 \| 1080 \| 4.1062 \| 0.4889 \| \| 0.0437 \| 41.0 \| 1107 \| 5.3485 \| 0.4667 \| \| 0.0045 \| 42.0 \| 1134 \| 5.3122 \| 0.4667 \| \| 0.0368 \| 43.0 \| 1161 \| 5.1937 \| 0.4667 \| \| 0.0032 \| 44.0 \| 1188 \| 5.6803 \| 0.4889 \| \| 0.0061 \| 45.0 \| 1215 \| 5.8620 \| 0.4444 \| \| 0.0035 \| 46.0 \| 1242 \| 5.9016 \| 0.4889 \| \| 0.0011 \| 47.0 \| 1269 \| 6.3136 \| 0.4444 \| \| 0.0277 \| 48.0 \| 1296 \| 6.2816 \| 0.4444 \| \| 0.0067 \| 49.0 \| 1323 \| 6.2787 \| 0.4444 \| \| 0.0372 \| 50.0 \| 1350 \| 6.2787 \| 0.4444 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 3.6981 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.5800 \| 0.2444 \| \| 2.0893 \| 2.0 \| 12 \| 1.3869 \| 0.3333 \| \| 2.0893 \| 3.0 \| 18 \| 1.3893 \| 0.2444 \| \| 1.4148 \| 4.0 \| 24 \| 1.3366 \| 0.3333 \| \| 1.3117 \| 5.0 \| 30 \| 1.3938 \| 0.2889 \| \| 1.3117 \| 6.0 \| 36 \| 1.5221 \| 0.3778 \| \| 1.2096 \| 7.0 \| 42 \| 1.7519 \| 0.4222 \| \| 1.2096 \| 8.0 \| 48 \| 1.6213 \| 0.2444 \| \| 1.1162 \| 9.0 \| 54 \| 1.4721 \| 0.2889 \| \| 1.0871 \| 10.0 \| 60 \| 1.3748 \| 0.3333 \| \| 1.0871 \| 11.0 \| 66 \| 1.7274 \| 0.4667 \| \| 1.0753 \| 12.0 \| 72 \| 3.1976 \| 0.3333 \| \| 1.0753 \| 13.0 \| 78 \| 1.3693 \| 0.4222 \| \| 1.2635 \| 14.0 \| 84 \| 1.5090 \| 0.3778 \| \| 0.9248 \| 15.0 \| 90 \| 1.4886 \| 0.5333 \| \| 0.9248 \| 16.0 \| 96 \| 1.4765 \| 0.4444 \| \| 0.8798 \| 17.0 \| 102 \| 1.9348 \| 0.4222 \| \| 0.8798 \| 18.0 \| 108 \| 1.3064 \| 0.4667 \| \| 0.8666 \| 19.0 \| 114 \| 1.5832 \| 0.4444 \| \| 0.7171 \| 20.0 \| 120 \| 2.1360 \| 0.4444 \| \| 0.7171 \| 21.0 \| 126 \| 1.7636 \| 0.4444 \| \| 0.7588 \| 22.0 \| 132 \| 2.3529 \| 0.3556 \| \| 0.7588 \| 23.0 \| 138 \| 2.7880 \| 0.3556 \| \| 0.6002 \| 24.0 \| 144 \| 1.8764 \| 0.4222 \| \| 0.5204 \| 25.0 \| 150 \| 2.9921 \| 0.4 \| \| 0.5204 \| 26.0 \| 156 \| 2.6311 \| 0.4444 \| \| 0.4748 \| 27.0 \| 162 \| 2.1490 \| 0.4889 \| \| 0.4748 \| 28.0 \| 168 \| 2.4874 \| 0.4889 \| \| 0.4423 \| 29.0 \| 174 \| 1.9273 \| 0.4444 \| \| 0.3826 \| 30.0 \| 180 \| 3.0375 \| 0.4222 \| \| 0.3826 \| 31.0 \| 186 \| 3.0775 \| 0.4667 \| \| 0.3486 \| 32.0 \| 192 \| 2.5400 \| 0.4 \| \| 0.3486 \| 33.0 \| 198 \| 3.1424 \| 0.4444 \| \| 0.3116 \| 34.0 \| 204 \| 2.9144 \| 0.4667 \| \| 0.2168 \| 35.0 \| 210 \| 3.3792 \| 0.4444 \| \| 0.2168 \| 36.0 \| 216 \| 3.7895 \| 0.4667 \| \| 0.2383 \| 37.0 \| 222 \| 3.1800 \| 0.4889 \| \| 0.2383 \| 38.0 \| 228 \| 3.3532 \| 0.4444 \| \| 0.1463 \| 39.0 \| 234 \| 3.6524 \| 0.4222 \| \| 0.1584 \| 40.0 \| 240 \| 3.6346 \| 0.4444 \| \| 0.1584 \| 41.0 \| 246 \| 3.6838 \| 0.4444 \| \| 0.1431 \| 42.0 \| 252 \| 3.6981 \| 0.4444 \| \| 0.1431 \| 43.0 \| 258 \| 3.6981 \| 0.4444 \| \| 0.1356 \| 44.0 \| 264 \| 3.6981 \| 0.4444 \| \| 0.139 \| 45.0 \| 270 \| 3.6981 \| 0.4444 \| \| 0.139 \| 46.0 \| 276 \| 3.6981 \| 0.4444 \| \| 0.1502 \| 47.0 \| 282 \| 3.6981 \| 0.4444 \| \| 0.1502 \| 48.0 \| 288 \| 3.6981 \| 0.4444 \| \| 0.128 \| 49.0 \| 294 \| 3.6981 \| 0.4444 \| \| 0.1474 \| 50.0 \| 300 \| 3.6981 \| 0.4444 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
Sharon8y/my_hotdog_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_hotdog_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5346 - Accuracy: 0.81 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.95 \| 9 \| 2.1083 \| 0.5967 \| \| 2.2301 \| 2.0 \| 19 \| 1.8377 \| 0.7067 \| \| 1.9275 \| 2.95 \| 28 \| 1.6582 \| 0.78 \| \| 1.6897 \| 4.0 \| 38 \| 1.5653 \| 0.79 \| \| 1.5374 \| 4.74 \| 45 \| 1.5346 \| 0.81 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "baked potato", "burger", "crispy chicken", "donut", "fries", "hot dog", "pizza", "sandwich", "taco", "taquito" ]
hkivancoral/hushem_1x_beit_base_adamax_001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3645 - Accuracy: 0.5556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.4123 \| 0.2444 \| \| 1.8567 \| 2.0 \| 12 \| 1.3969 \| 0.2444 \| \| 1.8567 \| 3.0 \| 18 \| 1.3773 \| 0.4 \| \| 1.4001 \| 4.0 \| 24 \| 1.3688 \| 0.3778 \| \| 1.3691 \| 5.0 \| 30 \| 1.3640 \| 0.2444 \| \| 1.3691 \| 6.0 \| 36 \| 1.2556 \| 0.5111 \| \| 1.3116 \| 7.0 \| 42 \| 1.4009 \| 0.2667 \| \| 1.3116 \| 8.0 \| 48 \| 1.2324 \| 0.4222 \| \| 1.1799 \| 9.0 \| 54 \| 1.1289 \| 0.5111 \| \| 1.1098 \| 10.0 \| 60 \| 1.5348 \| 0.2667 \| \| 1.1098 \| 11.0 \| 66 \| 1.2341 \| 0.4222 \| \| 1.0933 \| 12.0 \| 72 \| 1.3191 \| 0.4667 \| \| 1.0933 \| 13.0 \| 78 \| 1.3567 \| 0.4 \| \| 0.986 \| 14.0 \| 84 \| 1.1728 \| 0.3778 \| \| 0.9075 \| 15.0 \| 90 \| 1.1993 \| 0.5111 \| \| 0.9075 \| 16.0 \| 96 \| 1.1869 \| 0.3556 \| \| 0.8205 \| 17.0 \| 102 \| 1.3241 \| 0.5333 \| \| 0.8205 \| 18.0 \| 108 \| 1.2073 \| 0.5333 \| \| 0.9036 \| 19.0 \| 114 \| 1.2788 \| 0.4889 \| \| 0.7712 \| 20.0 \| 120 \| 1.2208 \| 0.4667 \| \| 0.7712 \| 21.0 \| 126 \| 1.2263 \| 0.5333 \| \| 0.6949 \| 22.0 \| 132 \| 1.1609 \| 0.4889 \| \| 0.6949 \| 23.0 \| 138 \| 1.1919 \| 0.4222 \| \| 0.7053 \| 24.0 \| 144 \| 1.2190 \| 0.5111 \| \| 0.6439 \| 25.0 \| 150 \| 1.2569 \| 0.5556 \| \| 0.6439 \| 26.0 \| 156 \| 1.3636 \| 0.5333 \| \| 0.6537 \| 27.0 \| 162 \| 1.4293 \| 0.5778 \| \| 0.6537 \| 28.0 \| 168 \| 1.2396 \| 0.5111 \| \| 0.6181 \| 29.0 \| 174 \| 1.3037 \| 0.5556 \| \| 0.5097 \| 30.0 \| 180 \| 1.3049 \| 0.5778 \| \| 0.5097 \| 31.0 \| 186 \| 1.1406 \| 0.5333 \| \| 0.5782 \| 32.0 \| 192 \| 1.2396 \| 0.5333 \| \| 0.5782 \| 33.0 \| 198 \| 1.2877 \| 0.5111 \| \| 0.5897 \| 34.0 \| 204 \| 1.3944 \| 0.5778 \| \| 0.4972 \| 35.0 \| 210 \| 1.2439 \| 0.5556 \| \| 0.4972 \| 36.0 \| 216 \| 1.2993 \| 0.5556 \| \| 0.4729 \| 37.0 \| 222 \| 1.3034 \| 0.5556 \| \| 0.4729 \| 38.0 \| 228 \| 1.3631 \| 0.5556 \| \| 0.3719 \| 39.0 \| 234 \| 1.4220 \| 0.5778 \| \| 0.4329 \| 40.0 \| 240 \| 1.3836 \| 0.5111 \| \| 0.4329 \| 41.0 \| 246 \| 1.3661 \| 0.5556 \| \| 0.3819 \| 42.0 \| 252 \| 1.3645 \| 0.5556 \| \| 0.3819 \| 43.0 \| 258 \| 1.3645 \| 0.5556 \| \| 0.3664 \| 44.0 \| 264 \| 1.3645 \| 0.5556 \| \| 0.4152 \| 45.0 \| 270 \| 1.3645 \| 0.5556 \| \| 0.4152 \| 46.0 \| 276 \| 1.3645 \| 0.5556 \| \| 0.3637 \| 47.0 \| 282 \| 1.3645 \| 0.5556 \| \| 0.3637 \| 48.0 \| 288 \| 1.3645 \| 0.5556 \| \| 0.394 \| 49.0 \| 294 \| 1.3645 \| 0.5556 \| \| 0.3776 \| 50.0 \| 300 \| 1.3645 \| 0.5556 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.8097 - Accuracy: 0.5349 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.4381 \| 0.2326 \| \| 2.0527 \| 2.0 \| 12 \| 1.4022 \| 0.2558 \| \| 2.0527 \| 3.0 \| 18 \| 1.3682 \| 0.3256 \| \| 1.3782 \| 4.0 \| 24 \| 1.3387 \| 0.3953 \| \| 1.2679 \| 5.0 \| 30 \| 1.3721 \| 0.3256 \| \| 1.2679 \| 6.0 \| 36 \| 1.7451 \| 0.3488 \| \| 1.2756 \| 7.0 \| 42 \| 1.3183 \| 0.3953 \| \| 1.2756 \| 8.0 \| 48 \| 1.4225 \| 0.3023 \| \| 1.173 \| 9.0 \| 54 \| 1.4215 \| 0.3953 \| \| 1.1959 \| 10.0 \| 60 \| 1.4072 \| 0.3721 \| \| 1.1959 \| 11.0 \| 66 \| 1.4852 \| 0.4186 \| \| 1.1344 \| 12.0 \| 72 \| 1.4523 \| 0.2791 \| \| 1.1344 \| 13.0 \| 78 \| 1.4043 \| 0.4651 \| \| 1.0854 \| 14.0 \| 84 \| 1.3638 \| 0.3953 \| \| 1.1124 \| 15.0 \| 90 \| 1.4323 \| 0.3953 \| \| 1.1124 \| 16.0 \| 96 \| 1.4664 \| 0.4884 \| \| 1.0108 \| 17.0 \| 102 \| 1.5473 \| 0.3721 \| \| 1.0108 \| 18.0 \| 108 \| 1.2300 \| 0.4651 \| \| 0.9443 \| 19.0 \| 114 \| 1.2523 \| 0.4419 \| \| 0.9125 \| 20.0 \| 120 \| 1.4134 \| 0.3721 \| \| 0.9125 \| 21.0 \| 126 \| 1.1280 \| 0.4884 \| \| 0.8328 \| 22.0 \| 132 \| 1.1054 \| 0.4884 \| \| 0.8328 \| 23.0 \| 138 \| 1.6081 \| 0.4419 \| \| 0.7565 \| 24.0 \| 144 \| 1.0331 \| 0.5349 \| \| 0.7135 \| 25.0 \| 150 \| 1.6384 \| 0.5116 \| \| 0.7135 \| 26.0 \| 156 \| 1.9524 \| 0.4651 \| \| 0.7048 \| 27.0 \| 162 \| 1.1399 \| 0.5349 \| \| 0.7048 \| 28.0 \| 168 \| 1.0504 \| 0.5581 \| \| 0.7074 \| 29.0 \| 174 \| 1.0452 \| 0.5581 \| \| 0.7008 \| 30.0 \| 180 \| 1.4757 \| 0.5581 \| \| 0.7008 \| 31.0 \| 186 \| 1.0663 \| 0.4419 \| \| 0.5976 \| 32.0 \| 192 \| 1.0991 \| 0.5349 \| \| 0.5976 \| 33.0 \| 198 \| 1.5330 \| 0.5814 \| \| 0.5565 \| 34.0 \| 204 \| 1.1511 \| 0.5349 \| \| 0.458 \| 35.0 \| 210 \| 1.5836 \| 0.5349 \| \| 0.458 \| 36.0 \| 216 \| 1.4225 \| 0.5581 \| \| 0.5542 \| 37.0 \| 222 \| 1.4182 \| 0.6047 \| \| 0.5542 \| 38.0 \| 228 \| 1.3407 \| 0.5581 \| \| 0.3706 \| 39.0 \| 234 \| 1.4368 \| 0.5581 \| \| 0.3087 \| 40.0 \| 240 \| 1.6899 \| 0.5814 \| \| 0.3087 \| 41.0 \| 246 \| 1.8110 \| 0.5116 \| \| 0.3001 \| 42.0 \| 252 \| 1.8097 \| 0.5349 \| \| 0.3001 \| 43.0 \| 258 \| 1.8097 \| 0.5349 \| \| 0.3061 \| 44.0 \| 264 \| 1.8097 \| 0.5349 \| \| 0.2986 \| 45.0 \| 270 \| 1.8097 \| 0.5349 \| \| 0.2986 \| 46.0 \| 276 \| 1.8097 \| 0.5349 \| \| 0.2791 \| 47.0 \| 282 \| 1.8097 \| 0.5349 \| \| 0.2791 \| 48.0 \| 288 \| 1.8097 \| 0.5349 \| \| 0.2908 \| 49.0 \| 294 \| 1.8097 \| 0.5349 \| \| 0.2986 \| 50.0 \| 300 \| 1.8097 \| 0.5349 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 4.3503 - Accuracy: 0.4524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.4229 \| 0.2381 \| \| 2.0151 \| 2.0 \| 12 \| 1.3893 \| 0.2619 \| \| 2.0151 \| 3.0 \| 18 \| 1.3408 \| 0.3333 \| \| 1.3963 \| 4.0 \| 24 \| 1.3326 \| 0.3095 \| \| 1.3169 \| 5.0 \| 30 \| 1.2412 \| 0.4762 \| \| 1.3169 \| 6.0 \| 36 \| 1.0247 \| 0.5476 \| \| 1.2588 \| 7.0 \| 42 \| 1.2101 \| 0.3571 \| \| 1.2588 \| 8.0 \| 48 \| 1.0013 \| 0.5238 \| \| 1.1685 \| 9.0 \| 54 \| 1.3288 \| 0.4524 \| \| 1.1624 \| 10.0 \| 60 \| 1.0173 \| 0.5 \| \| 1.1624 \| 11.0 \| 66 \| 1.2213 \| 0.4762 \| \| 1.163 \| 12.0 \| 72 \| 1.3131 \| 0.4286 \| \| 1.163 \| 13.0 \| 78 \| 1.0794 \| 0.5238 \| \| 1.0128 \| 14.0 \| 84 \| 1.2744 \| 0.3810 \| \| 1.1156 \| 15.0 \| 90 \| 1.2253 \| 0.5 \| \| 1.1156 \| 16.0 \| 96 \| 1.2674 \| 0.4048 \| \| 0.9374 \| 17.0 \| 102 \| 1.1623 \| 0.4524 \| \| 0.9374 \| 18.0 \| 108 \| 1.5694 \| 0.4048 \| \| 0.9149 \| 19.0 \| 114 \| 1.0570 \| 0.5476 \| \| 0.912 \| 20.0 \| 120 \| 1.2919 \| 0.4286 \| \| 0.912 \| 21.0 \| 126 \| 1.4307 \| 0.5 \| \| 0.6869 \| 22.0 \| 132 \| 1.5771 \| 0.5238 \| \| 0.6869 \| 23.0 \| 138 \| 2.1692 \| 0.3571 \| \| 0.6883 \| 24.0 \| 144 \| 1.5822 \| 0.5714 \| \| 0.7288 \| 25.0 \| 150 \| 2.0687 \| 0.4524 \| \| 0.7288 \| 26.0 \| 156 \| 2.1992 \| 0.4524 \| \| 0.4823 \| 27.0 \| 162 \| 2.2715 \| 0.5238 \| \| 0.4823 \| 28.0 \| 168 \| 3.3968 \| 0.4286 \| \| 0.4173 \| 29.0 \| 174 \| 2.2538 \| 0.5476 \| \| 0.4253 \| 30.0 \| 180 \| 3.6242 \| 0.3810 \| \| 0.4253 \| 31.0 \| 186 \| 2.4386 \| 0.5952 \| \| 0.3088 \| 32.0 \| 192 \| 3.2728 \| 0.4762 \| \| 0.3088 \| 33.0 \| 198 \| 3.5241 \| 0.5476 \| \| 0.1666 \| 34.0 \| 204 \| 3.5230 \| 0.5 \| \| 0.2645 \| 35.0 \| 210 \| 3.7888 \| 0.4286 \| \| 0.2645 \| 36.0 \| 216 \| 4.2240 \| 0.5238 \| \| 0.1416 \| 37.0 \| 222 \| 4.2393 \| 0.5 \| \| 0.1416 \| 38.0 \| 228 \| 4.0612 \| 0.4762 \| \| 0.1169 \| 39.0 \| 234 \| 4.3686 \| 0.4524 \| \| 0.0781 \| 40.0 \| 240 \| 4.2437 \| 0.4762 \| \| 0.0781 \| 41.0 \| 246 \| 4.2703 \| 0.4286 \| \| 0.06 \| 42.0 \| 252 \| 4.3503 \| 0.4524 \| \| 0.06 \| 43.0 \| 258 \| 4.3503 \| 0.4524 \| \| 0.0264 \| 44.0 \| 264 \| 4.3503 \| 0.4524 \| \| 0.1093 \| 45.0 \| 270 \| 4.3503 \| 0.4524 \| \| 0.1093 \| 46.0 \| 276 \| 4.3503 \| 0.4524 \| \| 0.0479 \| 47.0 \| 282 \| 4.3503 \| 0.4524 \| \| 0.0479 \| 48.0 \| 288 \| 4.3503 \| 0.4524 \| \| 0.0488 \| 49.0 \| 294 \| 4.3503 \| 0.4524 \| \| 0.0619 \| 50.0 \| 300 \| 4.3503 \| 0.4524 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_001_fold5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_001_fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2278 - Accuracy: 0.7317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.4268 \| 0.2439 \| \| 1.7859 \| 2.0 \| 12 \| 1.3982 \| 0.2439 \| \| 1.7859 \| 3.0 \| 18 \| 1.3119 \| 0.4878 \| \| 1.3869 \| 4.0 \| 24 \| 1.2627 \| 0.4146 \| \| 1.329 \| 5.0 \| 30 \| 1.0564 \| 0.5610 \| \| 1.329 \| 6.0 \| 36 \| 1.2486 \| 0.2927 \| \| 1.2971 \| 7.0 \| 42 \| 1.2260 \| 0.3415 \| \| 1.2971 \| 8.0 \| 48 \| 1.1669 \| 0.5122 \| \| 1.2043 \| 9.0 \| 54 \| 1.2078 \| 0.4390 \| \| 1.166 \| 10.0 \| 60 \| 1.1291 \| 0.4390 \| \| 1.166 \| 11.0 \| 66 \| 1.4793 \| 0.2683 \| \| 1.2368 \| 12.0 \| 72 \| 1.1712 \| 0.4390 \| \| 1.2368 \| 13.0 \| 78 \| 1.1600 \| 0.4146 \| \| 1.0841 \| 14.0 \| 84 \| 1.1286 \| 0.4146 \| \| 1.1358 \| 15.0 \| 90 \| 1.0309 \| 0.4878 \| \| 1.1358 \| 16.0 \| 96 \| 1.0536 \| 0.3902 \| \| 1.0304 \| 17.0 \| 102 \| 0.9535 \| 0.4878 \| \| 1.0304 \| 18.0 \| 108 \| 1.1738 \| 0.3659 \| \| 0.9971 \| 19.0 \| 114 \| 0.9220 \| 0.5122 \| \| 0.9482 \| 20.0 \| 120 \| 1.0234 \| 0.6829 \| \| 0.9482 \| 21.0 \| 126 \| 1.0465 \| 0.5366 \| \| 0.9578 \| 22.0 \| 132 \| 1.0713 \| 0.5854 \| \| 0.9578 \| 23.0 \| 138 \| 1.1190 \| 0.5122 \| \| 1.0032 \| 24.0 \| 144 \| 1.0303 \| 0.6341 \| \| 0.9765 \| 25.0 \| 150 \| 0.9143 \| 0.6098 \| \| 0.9765 \| 26.0 \| 156 \| 0.9675 \| 0.6098 \| \| 0.8768 \| 27.0 \| 162 \| 0.8561 \| 0.6341 \| \| 0.8768 \| 28.0 \| 168 \| 1.0406 \| 0.4878 \| \| 0.813 \| 29.0 \| 174 \| 1.2443 \| 0.6098 \| \| 0.8566 \| 30.0 \| 180 \| 0.8255 \| 0.6341 \| \| 0.8566 \| 31.0 \| 186 \| 0.8471 \| 0.6829 \| \| 0.7675 \| 32.0 \| 192 \| 0.9851 \| 0.6829 \| \| 0.7675 \| 33.0 \| 198 \| 1.1042 \| 0.6829 \| \| 0.7167 \| 34.0 \| 204 \| 1.0172 \| 0.6829 \| \| 0.6799 \| 35.0 \| 210 \| 1.1228 \| 0.5366 \| \| 0.6799 \| 36.0 \| 216 \| 1.1880 \| 0.7317 \| \| 0.6558 \| 37.0 \| 222 \| 1.1922 \| 0.7317 \| \| 0.6558 \| 38.0 \| 228 \| 1.4663 \| 0.6585 \| \| 0.5997 \| 39.0 \| 234 \| 1.0459 \| 0.7317 \| \| 0.579 \| 40.0 \| 240 \| 1.1555 \| 0.7073 \| \| 0.579 \| 41.0 \| 246 \| 1.1889 \| 0.7073 \| \| 0.5728 \| 42.0 \| 252 \| 1.2278 \| 0.7317 \| \| 0.5728 \| 43.0 \| 258 \| 1.2278 \| 0.7317 \| \| 0.5177 \| 44.0 \| 264 \| 1.2278 \| 0.7317 \| \| 0.5591 \| 45.0 \| 270 \| 1.2278 \| 0.7317 \| \| 0.5591 \| 46.0 \| 276 \| 1.2278 \| 0.7317 \| \| 0.5528 \| 47.0 \| 282 \| 1.2278 \| 0.7317 \| \| 0.5528 \| 48.0 \| 288 \| 1.2278 \| 0.7317 \| \| 0.575 \| 49.0 \| 294 \| 1.2278 \| 0.7317 \| \| 0.5528 \| 50.0 \| 300 \| 1.2278 \| 0.7317 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_0001_fold1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_0001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1942 - Accuracy: 0.7333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.1737 \| 0.5111 \| \| 1.2996 \| 2.0 \| 12 \| 0.6731 \| 0.7111 \| \| 1.2996 \| 3.0 \| 18 \| 0.5816 \| 0.7778 \| \| 0.3034 \| 4.0 \| 24 \| 0.5950 \| 0.7778 \| \| 0.0484 \| 5.0 \| 30 \| 0.7873 \| 0.7333 \| \| 0.0484 \| 6.0 \| 36 \| 0.7472 \| 0.7556 \| \| 0.0106 \| 7.0 \| 42 \| 0.8528 \| 0.8 \| \| 0.0106 \| 8.0 \| 48 \| 0.7211 \| 0.7778 \| \| 0.0205 \| 9.0 \| 54 \| 0.6347 \| 0.7778 \| \| 0.0012 \| 10.0 \| 60 \| 0.6115 \| 0.8 \| \| 0.0012 \| 11.0 \| 66 \| 0.6050 \| 0.8222 \| \| 0.0005 \| 12.0 \| 72 \| 0.6253 \| 0.8222 \| \| 0.0005 \| 13.0 \| 78 \| 0.7723 \| 0.8 \| \| 0.0021 \| 14.0 \| 84 \| 0.9287 \| 0.8 \| \| 0.0003 \| 15.0 \| 90 \| 1.0136 \| 0.7778 \| \| 0.0003 \| 16.0 \| 96 \| 0.9985 \| 0.7778 \| \| 0.0004 \| 17.0 \| 102 \| 0.9348 \| 0.7778 \| \| 0.0004 \| 18.0 \| 108 \| 0.8985 \| 0.8 \| \| 0.0003 \| 19.0 \| 114 \| 0.8733 \| 0.8222 \| \| 0.0009 \| 20.0 \| 120 \| 0.8790 \| 0.8222 \| \| 0.0009 \| 21.0 \| 126 \| 1.1330 \| 0.7778 \| \| 0.0002 \| 22.0 \| 132 \| 1.2620 \| 0.7556 \| \| 0.0002 \| 23.0 \| 138 \| 1.3184 \| 0.7556 \| \| 0.0003 \| 24.0 \| 144 \| 1.3104 \| 0.7778 \| \| 0.0003 \| 25.0 \| 150 \| 1.2554 \| 0.7556 \| \| 0.0003 \| 26.0 \| 156 \| 1.2162 \| 0.7556 \| \| 0.0002 \| 27.0 \| 162 \| 1.1923 \| 0.7333 \| \| 0.0002 \| 28.0 \| 168 \| 1.1869 \| 0.7333 \| \| 0.0002 \| 29.0 \| 174 \| 1.1546 \| 0.7333 \| \| 0.0002 \| 30.0 \| 180 \| 1.1302 \| 0.7556 \| \| 0.0002 \| 31.0 \| 186 \| 1.1214 \| 0.7556 \| \| 0.0003 \| 32.0 \| 192 \| 1.1205 \| 0.7556 \| \| 0.0003 \| 33.0 \| 198 \| 1.1222 \| 0.7556 \| \| 0.0018 \| 34.0 \| 204 \| 1.1316 \| 0.7556 \| \| 0.0004 \| 35.0 \| 210 \| 1.1630 \| 0.7556 \| \| 0.0004 \| 36.0 \| 216 \| 1.1838 \| 0.7333 \| \| 0.0002 \| 37.0 \| 222 \| 1.1946 \| 0.7333 \| \| 0.0002 \| 38.0 \| 228 \| 1.1949 \| 0.7333 \| \| 0.0004 \| 39.0 \| 234 \| 1.1930 \| 0.7333 \| \| 0.0002 \| 40.0 \| 240 \| 1.1932 \| 0.7333 \| \| 0.0002 \| 41.0 \| 246 \| 1.1940 \| 0.7333 \| \| 0.0002 \| 42.0 \| 252 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 43.0 \| 258 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 44.0 \| 264 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 45.0 \| 270 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 46.0 \| 276 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 47.0 \| 282 \| 1.1942 \| 0.7333 \| \| 0.0002 \| 48.0 \| 288 \| 1.1942 \| 0.7333 \| \| 0.0003 \| 49.0 \| 294 \| 1.1942 \| 0.7333 \| \| 0.0001 \| 50.0 \| 300 \| 1.1942 \| 0.7333 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_0001_fold2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0153 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.2331 \| 0.5556 \| \| 1.3348 \| 2.0 \| 12 \| 0.8218 \| 0.6889 \| \| 1.3348 \| 3.0 \| 18 \| 0.6484 \| 0.7556 \| \| 0.3555 \| 4.0 \| 24 \| 0.8513 \| 0.7556 \| \| 0.1239 \| 5.0 \| 30 \| 0.7326 \| 0.7333 \| \| 0.1239 \| 6.0 \| 36 \| 0.6190 \| 0.8 \| \| 0.0625 \| 7.0 \| 42 \| 1.0407 \| 0.7333 \| \| 0.0625 \| 8.0 \| 48 \| 0.7902 \| 0.8 \| \| 0.0045 \| 9.0 \| 54 \| 0.8103 \| 0.7778 \| \| 0.0021 \| 10.0 \| 60 \| 1.0314 \| 0.8 \| \| 0.0021 \| 11.0 \| 66 \| 1.1219 \| 0.7556 \| \| 0.0013 \| 12.0 \| 72 \| 1.0834 \| 0.7556 \| \| 0.0013 \| 13.0 \| 78 \| 1.0270 \| 0.7333 \| \| 0.0006 \| 14.0 \| 84 \| 1.0518 \| 0.7556 \| \| 0.0005 \| 15.0 \| 90 \| 1.0755 \| 0.7556 \| \| 0.0005 \| 16.0 \| 96 \| 1.1073 \| 0.7556 \| \| 0.0005 \| 17.0 \| 102 \| 1.1726 \| 0.7556 \| \| 0.0005 \| 18.0 \| 108 \| 1.2002 \| 0.7556 \| \| 0.0005 \| 19.0 \| 114 \| 1.1838 \| 0.7556 \| \| 0.0007 \| 20.0 \| 120 \| 1.1860 \| 0.7556 \| \| 0.0007 \| 21.0 \| 126 \| 1.2997 \| 0.7556 \| \| 0.0003 \| 22.0 \| 132 \| 1.3311 \| 0.7556 \| \| 0.0003 \| 23.0 \| 138 \| 1.3197 \| 0.7556 \| \| 0.0002 \| 24.0 \| 144 \| 1.2630 \| 0.7556 \| \| 0.0003 \| 25.0 \| 150 \| 1.1925 \| 0.7556 \| \| 0.0003 \| 26.0 \| 156 \| 1.1444 \| 0.7778 \| \| 0.0002 \| 27.0 \| 162 \| 1.1105 \| 0.7778 \| \| 0.0002 \| 28.0 \| 168 \| 1.0790 \| 0.7778 \| \| 0.0002 \| 29.0 \| 174 \| 1.0616 \| 0.7778 \| \| 0.0002 \| 30.0 \| 180 \| 1.0495 \| 0.7778 \| \| 0.0002 \| 31.0 \| 186 \| 1.0431 \| 0.7778 \| \| 0.0002 \| 32.0 \| 192 \| 1.0407 \| 0.7778 \| \| 0.0002 \| 33.0 \| 198 \| 1.0375 \| 0.8 \| \| 0.0107 \| 34.0 \| 204 \| 1.0331 \| 0.8 \| \| 0.0002 \| 35.0 \| 210 \| 1.0311 \| 0.8 \| \| 0.0002 \| 36.0 \| 216 \| 1.0289 \| 0.8 \| \| 0.0002 \| 37.0 \| 222 \| 1.0264 \| 0.8 \| \| 0.0002 \| 38.0 \| 228 \| 1.0203 \| 0.8 \| \| 0.0003 \| 39.0 \| 234 \| 1.0167 \| 0.8 \| \| 0.0002 \| 40.0 \| 240 \| 1.0146 \| 0.8 \| \| 0.0002 \| 41.0 \| 246 \| 1.0152 \| 0.8 \| \| 0.0002 \| 42.0 \| 252 \| 1.0153 \| 0.8 \| \| 0.0002 \| 43.0 \| 258 \| 1.0153 \| 0.8 \| \| 0.0002 \| 44.0 \| 264 \| 1.0153 \| 0.8 \| \| 0.0002 \| 45.0 \| 270 \| 1.0153 \| 0.8 \| \| 0.0002 \| 46.0 \| 276 \| 1.0153 \| 0.8 \| \| 0.002 \| 47.0 \| 282 \| 1.0153 \| 0.8 \| \| 0.002 \| 48.0 \| 288 \| 1.0153 \| 0.8 \| \| 0.0006 \| 49.0 \| 294 \| 1.0153 \| 0.8 \| \| 0.0001 \| 50.0 \| 300 \| 1.0153 \| 0.8 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_0001_fold3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5855 - Accuracy: 0.8605 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.0849 \| 0.5349 \| \| 1.2852 \| 2.0 \| 12 \| 0.7460 \| 0.7907 \| \| 1.2852 \| 3.0 \| 18 \| 0.5699 \| 0.8140 \| \| 0.4305 \| 4.0 \| 24 \| 0.3649 \| 0.8605 \| \| 0.1805 \| 5.0 \| 30 \| 0.2406 \| 0.9535 \| \| 0.1805 \| 6.0 \| 36 \| 0.4656 \| 0.8837 \| \| 0.0211 \| 7.0 \| 42 \| 0.4915 \| 0.8605 \| \| 0.0211 \| 8.0 \| 48 \| 0.5042 \| 0.8372 \| \| 0.0066 \| 9.0 \| 54 \| 0.6760 \| 0.7907 \| \| 0.0025 \| 10.0 \| 60 \| 0.6098 \| 0.8605 \| \| 0.0025 \| 11.0 \| 66 \| 0.6353 \| 0.9070 \| \| 0.0011 \| 12.0 \| 72 \| 0.6882 \| 0.8837 \| \| 0.0011 \| 13.0 \| 78 \| 0.6437 \| 0.8837 \| \| 0.0022 \| 14.0 \| 84 \| 0.5430 \| 0.8605 \| \| 0.0007 \| 15.0 \| 90 \| 0.5436 \| 0.8605 \| \| 0.0007 \| 16.0 \| 96 \| 0.5847 \| 0.8605 \| \| 0.0007 \| 17.0 \| 102 \| 0.7054 \| 0.8605 \| \| 0.0007 \| 18.0 \| 108 \| 0.7624 \| 0.8372 \| \| 0.0006 \| 19.0 \| 114 \| 0.6619 \| 0.8605 \| \| 0.0007 \| 20.0 \| 120 \| 0.6238 \| 0.8372 \| \| 0.0007 \| 21.0 \| 126 \| 0.6086 \| 0.8372 \| \| 0.0003 \| 22.0 \| 132 \| 0.6074 \| 0.8372 \| \| 0.0003 \| 23.0 \| 138 \| 0.6228 \| 0.8605 \| \| 0.0003 \| 24.0 \| 144 \| 0.6265 \| 0.8605 \| \| 0.0003 \| 25.0 \| 150 \| 0.6139 \| 0.8372 \| \| 0.0003 \| 26.0 \| 156 \| 0.6063 \| 0.8372 \| \| 0.0002 \| 27.0 \| 162 \| 0.5981 \| 0.8372 \| \| 0.0002 \| 28.0 \| 168 \| 0.5901 \| 0.8372 \| \| 0.0002 \| 29.0 \| 174 \| 0.5785 \| 0.8605 \| \| 0.0001 \| 30.0 \| 180 \| 0.5753 \| 0.8605 \| \| 0.0001 \| 31.0 \| 186 \| 0.5775 \| 0.8605 \| \| 0.0002 \| 32.0 \| 192 \| 0.5781 \| 0.8605 \| \| 0.0002 \| 33.0 \| 198 \| 0.5782 \| 0.8605 \| \| 0.0002 \| 34.0 \| 204 \| 0.5804 \| 0.8605 \| \| 0.0003 \| 35.0 \| 210 \| 0.5817 \| 0.8605 \| \| 0.0003 \| 36.0 \| 216 \| 0.5823 \| 0.8605 \| \| 0.0001 \| 37.0 \| 222 \| 0.5831 \| 0.8605 \| \| 0.0001 \| 38.0 \| 228 \| 0.5855 \| 0.8605 \| \| 0.0002 \| 39.0 \| 234 \| 0.5859 \| 0.8605 \| \| 0.0002 \| 40.0 \| 240 \| 0.5858 \| 0.8605 \| \| 0.0002 \| 41.0 \| 246 \| 0.5855 \| 0.8605 \| \| 0.0002 \| 42.0 \| 252 \| 0.5855 \| 0.8605 \| \| 0.0002 \| 43.0 \| 258 \| 0.5855 \| 0.8605 \| \| 0.0002 \| 44.0 \| 264 \| 0.5855 \| 0.8605 \| \| 0.0001 \| 45.0 \| 270 \| 0.5855 \| 0.8605 \| \| 0.0001 \| 46.0 \| 276 \| 0.5855 \| 0.8605 \| \| 0.0005 \| 47.0 \| 282 \| 0.5855 \| 0.8605 \| \| 0.0005 \| 48.0 \| 288 \| 0.5855 \| 0.8605 \| \| 0.0002 \| 49.0 \| 294 \| 0.5855 \| 0.8605 \| \| 0.0001 \| 50.0 \| 300 \| 0.5855 \| 0.8605 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]
hkivancoral/hushem_1x_beit_base_adamax_0001_fold4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_adamax_0001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2926 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.1428 \| 0.6905 \| \| 1.3492 \| 2.0 \| 12 \| 0.5681 \| 0.7857 \| \| 1.3492 \| 3.0 \| 18 \| 0.2529 \| 0.9286 \| \| 0.3166 \| 4.0 \| 24 \| 0.2221 \| 0.9524 \| \| 0.0428 \| 5.0 \| 30 \| 0.2913 \| 0.9048 \| \| 0.0428 \| 6.0 \| 36 \| 0.3814 \| 0.8571 \| \| 0.0093 \| 7.0 \| 42 \| 0.2701 \| 0.9524 \| \| 0.0093 \| 8.0 \| 48 \| 0.2796 \| 0.9286 \| \| 0.0019 \| 9.0 \| 54 \| 0.3043 \| 0.9048 \| \| 0.0029 \| 10.0 \| 60 \| 0.4551 \| 0.8810 \| \| 0.0029 \| 11.0 \| 66 \| 0.3262 \| 0.9286 \| \| 0.001 \| 12.0 \| 72 \| 0.2680 \| 0.9524 \| \| 0.001 \| 13.0 \| 78 \| 0.2601 \| 0.9524 \| \| 0.0006 \| 14.0 \| 84 \| 0.3353 \| 0.9048 \| \| 0.0008 \| 15.0 \| 90 \| 0.3915 \| 0.9048 \| \| 0.0008 \| 16.0 \| 96 \| 0.4398 \| 0.8810 \| \| 0.0004 \| 17.0 \| 102 \| 0.3988 \| 0.9048 \| \| 0.0004 \| 18.0 \| 108 \| 0.3416 \| 0.9048 \| \| 0.0053 \| 19.0 \| 114 \| 0.2975 \| 0.9286 \| \| 0.0004 \| 20.0 \| 120 \| 0.2890 \| 0.9286 \| \| 0.0004 \| 21.0 \| 126 \| 0.2852 \| 0.9286 \| \| 0.0061 \| 22.0 \| 132 \| 0.2652 \| 0.9286 \| \| 0.0061 \| 23.0 \| 138 \| 0.2502 \| 0.9286 \| \| 0.0002 \| 24.0 \| 144 \| 0.2495 \| 0.9286 \| \| 0.0003 \| 25.0 \| 150 \| 0.2641 \| 0.9286 \| \| 0.0003 \| 26.0 \| 156 \| 0.2771 \| 0.9286 \| \| 0.0002 \| 27.0 \| 162 \| 0.2877 \| 0.9286 \| \| 0.0002 \| 28.0 \| 168 \| 0.3003 \| 0.9286 \| \| 0.0002 \| 29.0 \| 174 \| 0.3118 \| 0.9286 \| \| 0.0002 \| 30.0 \| 180 \| 0.3215 \| 0.9286 \| \| 0.0002 \| 31.0 \| 186 \| 0.3282 \| 0.9286 \| \| 0.0003 \| 32.0 \| 192 \| 0.3381 \| 0.9286 \| \| 0.0003 \| 33.0 \| 198 \| 0.3472 \| 0.9048 \| \| 0.0002 \| 34.0 \| 204 \| 0.3491 \| 0.9048 \| \| 0.0049 \| 35.0 \| 210 \| 0.3154 \| 0.9048 \| \| 0.0049 \| 36.0 \| 216 \| 0.2965 \| 0.9048 \| \| 0.0002 \| 37.0 \| 222 \| 0.2887 \| 0.9048 \| \| 0.0002 \| 38.0 \| 228 \| 0.2886 \| 0.9048 \| \| 0.0002 \| 39.0 \| 234 \| 0.2894 \| 0.9048 \| \| 0.0002 \| 40.0 \| 240 \| 0.2903 \| 0.9048 \| \| 0.0002 \| 41.0 \| 246 \| 0.2922 \| 0.9048 \| \| 0.0004 \| 42.0 \| 252 \| 0.2926 \| 0.9048 \| \| 0.0004 \| 43.0 \| 258 \| 0.2926 \| 0.9048 \| \| 0.0002 \| 44.0 \| 264 \| 0.2926 \| 0.9048 \| \| 0.0002 \| 45.0 \| 270 \| 0.2926 \| 0.9048 \| \| 0.0002 \| 46.0 \| 276 \| 0.2926 \| 0.9048 \| \| 0.0009 \| 47.0 \| 282 \| 0.2926 \| 0.9048 \| \| 0.0009 \| 48.0 \| 288 \| 0.2926 \| 0.9048 \| \| 0.0004 \| 49.0 \| 294 \| 0.2926 \| 0.9048 \| \| 0.0001 \| 50.0 \| 300 \| 0.2926 \| 0.9048 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# dit-base_tobacco-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6206 - Accuracy: 0.825 - Brier Loss: 0.2570 - Nll: 0.9939 - F1 Micro: 0.825 - F1 Macro: 0.8166 - Ece: 0.1370 - Aurc: 0.0444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.0511 | 0.22 | 0.9320 | 7.5162 | 0.22 | 0.0792 | 0.3104 | 0.7615 | | No log | 2.0 | 14 | 3.4353 | 0.35 | 0.8214 | 5.1797 | 0.35 | 0.2316 | 0.2589 | 0.6234 | | No log | 3.0 | 21 | 2.6406 | 0.47 | 0.6828 | 2.9202 | 0.47 | 0.3725 | 0.2781 | 0.3277 | | No log | 4.0 | 28 | 2.0027 | 0.57 | 0.5596 | 1.8624 | 0.57 | 0.4971 | 0.2526 | 0.2124 | | No log | 5.0 | 35 | 1.5018 | 0.65 | 0.4518 | 1.7094 | 0.65 | 0.6128 | 0.2242 | 0.1396 | | No log | 6.0 | 42 | 1.3904 | 0.71 | 0.4105 | 1.8765 | 0.7100 | 0.7058 | 0.2202 | 0.1126 | | No log | 7.0 | 49 | 1.1226 | 0.76 | 0.3558 | 1.8024 | 0.76 | 0.7029 | 0.1815 | 0.0841 | | No log | 8.0 | 56 | 1.1810 | 0.73 | 0.3716 | 1.5642 | 0.7300 | 0.7027 | 0.1956 | 0.0834 | | No log | 9.0 | 63 | 1.2131 | 0.73 | 0.3811 | 1.7544 | 0.7300 | 0.6774 | 0.2070 | 0.0872 | | No log | 10.0 | 70 | 1.3986 | 0.72 | 0.4043 | 2.0161 | 0.72 | 0.7259 | 0.2021 | 0.1098 | | No log | 11.0 | 77 | 1.1001 | 0.765 | 0.3202 | 1.9113 | 0.765 | 0.7578 | 0.1859 | 0.0678 | | No log | 12.0 | 84 | 1.0429 | 0.77 | 0.3487 | 1.2955 | 0.7700 | 0.7663 | 0.1910 | 0.0827 | | No log | 13.0 | 91 | 0.9864 | 0.77 | 0.3227 | 1.3721 | 0.7700 | 0.7734 | 0.1692 | 0.0710 | | No log | 14.0 | 98 | 1.0068 | 0.74 | 0.3581 | 1.3362 | 0.74 | 0.7271 | 0.1848 | 0.0804 | | No log | 15.0 | 105 | 0.8635 | 0.795 | 0.3009 | 1.4785 | 0.795 | 0.7810 | 0.1646 | 0.0538 | | No log | 16.0 | 112 | 0.8157 | 0.81 | 0.2845 | 1.2525 | 0.81 | 0.7931 | 0.1545 | 0.0519 | | No log | 17.0 | 119 | 0.8616 | 0.78 | 0.3186 | 1.4230 | 0.78 | 0.7705 | 0.1610 | 0.0647 | | No log | 18.0 | 126 | 0.8034 | 0.8 | 0.2784 | 1.4410 | 0.8000 | 0.7811 | 0.1576 | 0.0489 | | No log | 19.0 | 133 | 0.7601 | 0.805 | 0.2697 | 1.2885 | 0.805 | 0.7823 | 0.1499 | 0.0494 | | No log | 20.0 | 140 | 0.7598 | 0.82 | 0.2709 | 1.3643 | 0.82 | 0.8090 | 0.1542 | 0.0516 | | No log | 21.0 | 147 | 0.8221 | 0.79 | 0.2905 | 1.4031 | 0.79 | 0.7640 | 0.1612 | 0.0585 | | No log | 22.0 | 154 | 0.7271 | 0.825 | 0.2599 | 1.0950 | 0.825 | 0.8147 | 0.1381 | 0.0454 | | No log | 23.0 | 161 | 0.7556 | 0.795 | 0.2891 | 1.1111 | 0.795 | 0.7822 | 0.1413 | 0.0558 | | No log | 24.0 | 168 | 0.7197 | 0.81 | 0.2759 | 1.1361 | 0.81 | 0.7905 | 0.1617 | 0.0500 | | No log | 25.0 | 175 | 0.7192 | 0.83 | 0.2620 | 1.3395 | 0.83 | 0.8155 | 0.1459 | 0.0433 | | No log | 26.0 | 182 | 0.7347 | 0.805 | 0.2821 | 1.1396 | 0.805 | 0.7868 | 0.1512 | 0.0541 | | No log | 27.0 | 189 | 0.7402 | 0.815 | 0.2805 | 1.3562 | 0.815 | 0.7928 | 0.1489 | 0.0519 | | No log | 28.0 | 196 | 0.6986 | 0.815 | 0.2562 | 1.1454 | 0.815 | 0.7944 | 0.1467 | 0.0443 | | No log | 29.0 | 203 | 0.7148 | 0.81 | 0.2718 | 1.1404 | 0.81 | 0.7944 | 0.1440 | 0.0513 | | No log | 30.0 | 210 | 0.7041 | 0.81 | 0.2796 | 1.3773 | 0.81 | 0.7998 | 0.1484 | 0.0494 | | No log | 31.0 | 217 | 0.7428 | 0.815 | 0.2823 | 1.1146 | 0.815 | 0.7967 | 0.1626 | 0.0542 | | No log | 32.0 | 224 | 0.6941 | 0.82 | 0.2682 | 1.1921 | 0.82 | 0.8098 | 0.1427 | 0.0478 | | No log | 33.0 | 231 | 0.7170 | 0.81 | 0.2794 | 1.2244 | 0.81 | 0.7875 | 0.1407 | 0.0511 | | No log | 34.0 | 238 | 0.7024 | 0.815 | 0.2805 | 1.0423 | 0.815 | 0.8043 | 0.1560 | 0.0512 | | No log | 35.0 | 245 | 0.7299 | 0.81 | 0.2710 | 1.1835 | 0.81 | 0.7964 | 0.1475 | 0.0530 | | No log | 36.0 | 252 | 0.6488 | 0.83 | 0.2500 | 1.1662 | 0.83 | 0.8117 | 0.1315 | 0.0431 | | No log | 37.0 | 259 | 0.6877 | 0.815 | 0.2751 | 1.0878 | 0.815 | 0.7973 | 0.1381 | 0.0489 | | No log | 38.0 | 266 | 0.7019 | 0.84 | 0.2620 | 1.2709 | 0.8400 | 0.8282 | 0.1607 | 0.0498 | | No log | 39.0 | 273 | 0.6687 | 0.81 | 0.2680 | 1.3004 | 0.81 | 0.7959 | 0.1346 | 0.0465 | | No log | 40.0 | 280 | 0.6813 | 0.81 | 0.2809 | 1.0539 | 0.81 | 0.7929 | 0.1628 | 0.0500 | | No log | 41.0 | 287 | 0.6525 | 0.83 | 0.2493 | 1.1496 | 0.83 | 0.8176 | 0.1413 | 0.0437 | | No log | 42.0 | 294 | 0.6526 | 0.835 | 0.2547 | 1.2429 | 0.835 | 0.8253 | 0.1420 | 0.0450 | | No log | 43.0 | 301 | 0.6696 | 0.82 | 0.2717 | 1.0446 | 0.82 | 0.8118 | 0.1486 | 0.0501 | | No log | 44.0 | 308 | 0.6555 | 0.83 | 0.2626 | 0.9948 | 0.83 | 0.8214 | 0.1366 | 0.0461 | | No log | 45.0 | 315 | 0.6380 | 0.82 | 0.2600 | 1.2151 | 0.82 | 0.8026 | 0.1263 | 0.0428 | | No log | 46.0 | 322 | 0.6356 | 0.82 | 0.2571 | 1.0923 | 0.82 | 0.8114 | 0.1443 | 0.0449 | | No log | 47.0 | 329 | 0.6444 | 0.815 | 0.2638 | 1.0657 | 0.815 | 0.7980 | 0.1503 | 0.0476 | | No log | 48.0 | 336 | 0.6337 | 0.82 | 0.2676 | 1.0650 | 0.82 | 0.8077 | 0.1370 | 0.0442 | | No log | 49.0 | 343 | 0.6271 | 0.84 | 0.2541 | 1.1500 | 0.8400 | 0.8230 | 0.1365 | 0.0422 | | No log | 50.0 | 350 | 0.6284 | 0.81 | 0.2588 | 1.2703 | 0.81 | 0.7964 | 0.1411 | 0.0425 | | No log | 51.0 | 357 | 0.6507 | 0.82 | 0.2612 | 1.1306 | 0.82 | 0.7996 | 0.1558 | 0.0460 | | No log | 52.0 | 364 | 0.6329 | 0.825 | 0.2602 | 1.2060 | 0.825 | 0.8146 | 0.1296 | 0.0439 | | No log | 53.0 | 371 | 0.6342 | 0.825 | 0.2574 | 1.0132 | 0.825 | 0.8158 | 0.1467 | 0.0434 | | No log | 54.0 | 378 | 0.6486 | 0.82 | 0.2633 | 1.1662 | 0.82 | 0.8060 | 0.1445 | 0.0466 | | No log | 55.0 | 385 | 0.6245 | 0.825 | 0.2588 | 1.1358 | 0.825 | 0.8088 | 0.1428 | 0.0429 | | No log | 56.0 | 392 | 0.6303 | 0.815 | 0.2616 | 0.9843 | 0.815 | 0.8013 | 0.1447 | 0.0458 | | No log | 57.0 | 399 | 0.6196 | 0.82 | 0.2545 | 1.1936 | 0.82 | 0.8076 | 0.1516 | 0.0438 | | No log | 58.0 | 406 | 0.6241 | 0.82 | 0.2620 | 1.0557 | 0.82 | 0.8100 | 0.1423 | 0.0450 | | No log | 59.0 | 413 | 0.6278 | 0.82 | 0.2579 | 1.0777 | 0.82 | 0.8076 | 0.1382 | 0.0451 | | No log | 60.0 | 420 | 0.6385 | 0.81 | 0.2651 | 0.9962 | 0.81 | 0.7910 | 0.1565 | 0.0467 | | No log | 61.0 | 427 | 0.6328 | 0.82 | 0.2619 | 0.9968 | 0.82 | 0.8103 | 0.1299 | 0.0469 | | No log | 62.0 | 434 | 0.6195 | 0.82 | 0.2571 | 0.9997 | 0.82 | 0.8062 | 0.1471 | 0.0438 | | No log | 63.0 | 441 | 0.6150 | 0.825 | 0.2560 | 1.0061 | 0.825 | 0.8166 | 0.1498 | 0.0430 | | No log | 64.0 | 448 | 0.6201 | 0.825 | 0.2574 | 1.0592 | 0.825 | 0.8166 | 0.1369 | 0.0442 | | No log | 65.0 | 455 | 0.6281 | 0.815 | 0.2601 | 0.9990 | 0.815 | 0.8013 | 0.1449 | 0.0459 | | No log | 66.0 | 462 | 0.6232 | 0.825 | 0.2538 | 1.0657 | 0.825 | 0.8166 | 0.1341 | 0.0442 | | No log | 67.0 | 469 | 0.6242 | 0.82 | 0.2567 | 1.0622 | 0.82 | 0.8100 | 0.1432 | 0.0445 | | No log | 68.0 | 476 | 0.6213 | 0.82 | 0.2598 | 1.0666 | 0.82 | 0.8100 | 0.1517 | 0.0447 | | No log | 69.0 | 483 | 0.6268 | 0.82 | 0.2577 | 1.0106 | 0.82 | 0.8100 | 0.1365 | 0.0455 | | No log | 70.0 | 490 | 0.6252 | 0.82 | 0.2579 | 0.9979 | 0.82 | 0.8100 | 0.1395 | 0.0451 | | No log | 71.0 | 497 | 0.6251 | 0.82 | 0.2589 | 1.0606 | 0.82 | 0.8100 | 0.1485 | 0.0448 | | 0.3286 | 72.0 | 504 | 0.6212 | 0.825 | 0.2571 | 1.0034 | 0.825 | 0.8166 | 0.1448 | 0.0443 | | 0.3286 | 73.0 | 511 | 0.6212 | 0.82 | 0.2584 | 0.9940 | 0.82 | 0.8100 | 0.1499 | 0.0444 | | 0.3286 | 74.0 | 518 | 0.6214 | 0.82 | 0.2576 | 0.9914 | 0.82 | 0.8100 | 0.1411 | 0.0448 | | 0.3286 | 75.0 | 525 | 0.6233 | 0.82 | 0.2580 | 0.9966 | 0.82 | 0.8100 | 0.1592 | 0.0450 | | 0.3286 | 76.0 | 532 | 0.6214 | 0.82 | 0.2568 | 0.9952 | 0.82 | 0.8100 | 0.1404 | 0.0448 | | 0.3286 | 77.0 | 539 | 0.6217 | 0.825 | 0.2575 | 0.9951 | 0.825 | 0.8166 | 0.1361 | 0.0445 | | 0.3286 | 78.0 | 546 | 0.6220 | 0.82 | 0.2569 | 0.9964 | 0.82 | 0.8100 | 0.1385 | 0.0450 | | 0.3286 | 79.0 | 553 | 0.6225 | 0.82 | 0.2581 | 0.9950 | 0.82 | 0.8100 | 0.1485 | 0.0450 | | 0.3286 | 80.0 | 560 | 0.6213 | 0.82 | 0.2578 | 0.9912 | 0.82 | 0.8100 | 0.1381 | 0.0446 | | 0.3286 | 81.0 | 567 | 0.6209 | 0.82 | 0.2572 | 0.9948 | 0.82 | 0.8100 | 0.1415 | 0.0447 | | 0.3286 | 82.0 | 574 | 0.6213 | 0.82 | 0.2578 | 0.9958 | 0.82 | 0.8100 | 0.1422 | 0.0449 | | 0.3286 | 83.0 | 581 | 0.6220 | 0.82 | 0.2579 | 0.9947 | 0.82 | 0.8100 | 0.1553 | 0.0448 | | 0.3286 | 84.0 | 588 | 0.6212 | 0.82 | 0.2574 | 0.9915 | 0.82 | 0.8100 | 0.1418 | 0.0447 | | 0.3286 | 85.0 | 595 | 0.6220 | 0.82 | 0.2579 | 0.9937 | 0.82 | 0.8100 | 0.1628 | 0.0450 | | 0.3286 | 86.0 | 602 | 0.6207 | 0.82 | 0.2572 | 0.9945 | 0.82 | 0.8100 | 0.1412 | 0.0447 | | 0.3286 | 87.0 | 609 | 0.6212 | 0.82 | 0.2573 | 0.9940 | 0.82 | 0.8100 | 0.1414 | 0.0447 | | 0.3286 | 88.0 | 616 | 0.6201 | 0.825 | 0.2570 | 0.9943 | 0.825 | 0.8166 | 0.1366 | 0.0443 | | 0.3286 | 89.0 | 623 | 0.6210 | 0.82 | 0.2573 | 0.9944 | 0.82 | 0.8100 | 0.1414 | 0.0448 | | 0.3286 | 90.0 | 630 | 0.6207 | 0.82 | 0.2572 | 0.9942 | 0.82 | 0.8100 | 0.1414 | 0.0447 | | 0.3286 | 91.0 | 637 | 0.6210 | 0.82 | 0.2572 | 0.9952 | 0.82 | 0.8100 | 0.1415 | 0.0447 | | 0.3286 | 92.0 | 644 | 0.6205 | 0.82 | 0.2572 | 0.9939 | 0.82 | 0.8100 | 0.1414 | 0.0447 | | 0.3286 | 93.0 | 651 | 0.6207 | 0.825 | 0.2570 | 0.9938 | 0.825 | 0.8166 | 0.1373 | 0.0445 | | 0.3286 | 94.0 | 658 | 0.6206 | 0.82 | 0.2572 | 0.9945 | 0.82 | 0.8100 | 0.1414 | 0.0447 | | 0.3286 | 95.0 | 665 | 0.6203 | 0.825 | 0.2568 | 0.9951 | 0.825 | 0.8166 | 0.1370 | 0.0444 | | 0.3286 | 96.0 | 672 | 0.6205 | 0.82 | 0.2571 | 0.9942 | 0.82 | 0.8100 | 0.1413 | 0.0448 | | 0.3286 | 97.0 | 679 | 0.6206 | 0.825 | 0.2570 | 0.9943 | 0.825 | 0.8166 | 0.1370 | 0.0445 | | 0.3286 | 98.0 | 686 | 0.6206 | 0.825 | 0.2570 | 0.9942 | 0.825 | 0.8166 | 0.1370 | 0.0445 | | 0.3286 | 99.0 | 693 | 0.6206 | 0.825 | 0.2570 | 0.9940 | 0.825 | 0.8166 | 0.1370 | 0.0445 | | 0.3286 | 100.0 | 700 | 0.6206 | 0.825 | 0.2570 | 0.9939 | 0.825 | 0.8166 | 0.1370 | 0.0444 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_MSE

# dit-base_tobacco-tiny_tobacco3482_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0108 - Accuracy: 0.815 - Brier Loss: 0.2593 - Nll: 1.1011 - F1 Micro: 0.815 - F1 Macro: 0.8014 - Ece: 0.1462 - Aurc: 0.0442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 7.1562 | 0.195 | 0.9158 | 7.6908 | 0.195 | 0.1043 | 0.2991 | 0.7927 | | No log | 2.0 | 14 | 6.3485 | 0.245 | 0.8600 | 4.2261 | 0.245 | 0.1794 | 0.2960 | 0.7445 | | No log | 3.0 | 21 | 5.2488 | 0.41 | 0.7090 | 3.6293 | 0.41 | 0.3494 | 0.2826 | 0.3786 | | No log | 4.0 | 28 | 4.0484 | 0.565 | 0.5698 | 1.7703 | 0.565 | 0.5209 | 0.2622 | 0.2372 | | No log | 5.0 | 35 | 3.0368 | 0.655 | 0.4710 | 1.5921 | 0.655 | 0.6368 | 0.2222 | 0.1598 | | No log | 6.0 | 42 | 2.6191 | 0.695 | 0.4219 | 1.6919 | 0.695 | 0.6535 | 0.2041 | 0.1200 | | No log | 7.0 | 49 | 2.0941 | 0.725 | 0.3913 | 1.3852 | 0.7250 | 0.6844 | 0.2046 | 0.0966 | | No log | 8.0 | 56 | 2.0668 | 0.725 | 0.4119 | 1.3829 | 0.7250 | 0.6811 | 0.1890 | 0.1045 | | No log | 9.0 | 63 | 1.7456 | 0.79 | 0.3138 | 1.5258 | 0.79 | 0.7539 | 0.1521 | 0.0651 | | No log | 10.0 | 70 | 1.5815 | 0.77 | 0.3391 | 1.2461 | 0.7700 | 0.7323 | 0.1593 | 0.0725 | | No log | 11.0 | 77 | 1.5720 | 0.785 | 0.2895 | 1.3282 | 0.785 | 0.7659 | 0.1408 | 0.0522 | | No log | 12.0 | 84 | 1.8886 | 0.78 | 0.3692 | 1.6238 | 0.78 | 0.7717 | 0.2015 | 0.0917 | | No log | 13.0 | 91 | 1.6164 | 0.785 | 0.2918 | 1.6303 | 0.785 | 0.7925 | 0.1545 | 0.0564 | | No log | 14.0 | 98 | 1.4318 | 0.785 | 0.3220 | 1.3070 | 0.785 | 0.7606 | 0.1430 | 0.0639 | | No log | 15.0 | 105 | 1.2774 | 0.81 | 0.2807 | 1.2877 | 0.81 | 0.7939 | 0.1532 | 0.0595 | | No log | 16.0 | 112 | 1.3797 | 0.8 | 0.2993 | 1.2409 | 0.8000 | 0.7759 | 0.1565 | 0.0700 | | No log | 17.0 | 119 | 1.3629 | 0.795 | 0.3091 | 1.1781 | 0.795 | 0.7670 | 0.1712 | 0.0567 | | No log | 18.0 | 126 | 1.5101 | 0.8 | 0.3192 | 1.3586 | 0.8000 | 0.7878 | 0.1919 | 0.0665 | | No log | 19.0 | 133 | 1.3897 | 0.805 | 0.2857 | 1.4983 | 0.805 | 0.7851 | 0.1356 | 0.0516 | | No log | 20.0 | 140 | 1.3821 | 0.795 | 0.3204 | 1.0916 | 0.795 | 0.7745 | 0.1678 | 0.0651 | | No log | 21.0 | 147 | 1.2852 | 0.83 | 0.2621 | 1.5182 | 0.83 | 0.8246 | 0.1483 | 0.0486 | | No log | 22.0 | 154 | 1.2080 | 0.815 | 0.2744 | 1.1921 | 0.815 | 0.7957 | 0.1319 | 0.0500 | | No log | 23.0 | 161 | 1.4016 | 0.805 | 0.3165 | 1.3364 | 0.805 | 0.7844 | 0.1534 | 0.0624 | | No log | 24.0 | 168 | 1.2883 | 0.825 | 0.2592 | 1.4946 | 0.825 | 0.8119 | 0.1549 | 0.0481 | | No log | 25.0 | 175 | 1.1715 | 0.815 | 0.2676 | 1.3363 | 0.815 | 0.8054 | 0.1464 | 0.0494 | | No log | 26.0 | 182 | 1.1844 | 0.825 | 0.2585 | 1.4938 | 0.825 | 0.8045 | 0.1572 | 0.0469 | | No log | 27.0 | 189 | 1.1739 | 0.81 | 0.2959 | 1.0692 | 0.81 | 0.7909 | 0.1625 | 0.0550 | | No log | 28.0 | 196 | 1.1944 | 0.815 | 0.2891 | 1.1811 | 0.815 | 0.7971 | 0.1430 | 0.0572 | | No log | 29.0 | 203 | 1.2115 | 0.83 | 0.2597 | 1.4809 | 0.83 | 0.8101 | 0.1289 | 0.0469 | | No log | 30.0 | 210 | 1.1622 | 0.81 | 0.2825 | 1.1104 | 0.81 | 0.7931 | 0.1463 | 0.0511 | | No log | 31.0 | 217 | 1.2591 | 0.8 | 0.3096 | 1.2310 | 0.8000 | 0.7789 | 0.1719 | 0.0591 | | No log | 32.0 | 224 | 1.1752 | 0.82 | 0.2687 | 1.4091 | 0.82 | 0.7959 | 0.1581 | 0.0504 | | No log | 33.0 | 231 | 1.1114 | 0.815 | 0.2719 | 1.0945 | 0.815 | 0.7885 | 0.1492 | 0.0485 | | No log | 34.0 | 238 | 1.1105 | 0.815 | 0.2727 | 1.1239 | 0.815 | 0.7962 | 0.1300 | 0.0479 | | No log | 35.0 | 245 | 1.1662 | 0.825 | 0.2748 | 1.3396 | 0.825 | 0.8100 | 0.1571 | 0.0554 | | No log | 36.0 | 252 | 1.1023 | 0.815 | 0.2757 | 1.1805 | 0.815 | 0.8031 | 0.1428 | 0.0504 | | No log | 37.0 | 259 | 1.1060 | 0.84 | 0.2604 | 1.3305 | 0.8400 | 0.8319 | 0.1596 | 0.0487 | | No log | 38.0 | 266 | 1.1123 | 0.81 | 0.2682 | 1.1122 | 0.81 | 0.7922 | 0.1310 | 0.0482 | | No log | 39.0 | 273 | 1.0820 | 0.815 | 0.2669 | 1.1629 | 0.815 | 0.7955 | 0.1479 | 0.0490 | | No log | 40.0 | 280 | 1.0972 | 0.805 | 0.2784 | 1.2442 | 0.805 | 0.7858 | 0.1576 | 0.0483 | | No log | 41.0 | 287 | 1.0845 | 0.83 | 0.2705 | 1.1180 | 0.83 | 0.8221 | 0.1504 | 0.0468 | | No log | 42.0 | 294 | 1.0769 | 0.82 | 0.2602 | 1.1173 | 0.82 | 0.8066 | 0.1458 | 0.0451 | | No log | 43.0 | 301 | 1.1366 | 0.81 | 0.2939 | 1.0722 | 0.81 | 0.7958 | 0.1532 | 0.0526 | | No log | 44.0 | 308 | 1.0716 | 0.82 | 0.2635 | 1.1839 | 0.82 | 0.8043 | 0.1403 | 0.0451 | | No log | 45.0 | 315 | 1.0865 | 0.81 | 0.2770 | 1.3595 | 0.81 | 0.7929 | 0.1501 | 0.0528 | | No log | 46.0 | 322 | 1.0768 | 0.82 | 0.2638 | 1.1161 | 0.82 | 0.8067 | 0.1462 | 0.0457 | | No log | 47.0 | 329 | 1.0644 | 0.825 | 0.2552 | 1.2086 | 0.825 | 0.8098 | 0.1579 | 0.0439 | | No log | 48.0 | 336 | 1.0511 | 0.815 | 0.2656 | 1.1019 | 0.815 | 0.8014 | 0.1518 | 0.0471 | | No log | 49.0 | 343 | 1.0517 | 0.82 | 0.2717 | 1.0881 | 0.82 | 0.8044 | 0.1559 | 0.0473 | | No log | 50.0 | 350 | 1.0824 | 0.81 | 0.2813 | 1.1022 | 0.81 | 0.7968 | 0.1538 | 0.0505 | | No log | 51.0 | 357 | 1.1439 | 0.835 | 0.2634 | 1.3483 | 0.835 | 0.8206 | 0.1471 | 0.0496 | | No log | 52.0 | 364 | 1.0444 | 0.83 | 0.2500 | 1.0999 | 0.83 | 0.8156 | 0.1310 | 0.0423 | | No log | 53.0 | 371 | 1.0426 | 0.825 | 0.2644 | 1.1112 | 0.825 | 0.8053 | 0.1295 | 0.0474 | | No log | 54.0 | 378 | 1.0341 | 0.825 | 0.2635 | 1.1053 | 0.825 | 0.8092 | 0.1467 | 0.0465 | | No log | 55.0 | 385 | 1.0900 | 0.815 | 0.2762 | 1.1021 | 0.815 | 0.7990 | 0.1439 | 0.0480 | | No log | 56.0 | 392 | 1.0423 | 0.845 | 0.2517 | 1.2594 | 0.845 | 0.8444 | 0.1497 | 0.0428 | | No log | 57.0 | 399 | 1.0246 | 0.825 | 0.2634 | 1.0927 | 0.825 | 0.8130 | 0.1260 | 0.0454 | | No log | 58.0 | 406 | 1.0365 | 0.835 | 0.2649 | 1.0825 | 0.835 | 0.8232 | 0.1291 | 0.0448 | | No log | 59.0 | 413 | 1.0394 | 0.82 | 0.2668 | 1.0968 | 0.82 | 0.8045 | 0.1458 | 0.0460 | | No log | 60.0 | 420 | 1.0261 | 0.815 | 0.2720 | 1.0883 | 0.815 | 0.8011 | 0.1409 | 0.0472 | | No log | 61.0 | 427 | 1.0503 | 0.83 | 0.2543 | 1.3230 | 0.83 | 0.8132 | 0.1378 | 0.0455 | | No log | 62.0 | 434 | 1.0400 | 0.82 | 0.2637 | 1.0958 | 0.82 | 0.8043 | 0.1397 | 0.0456 | | No log | 63.0 | 441 | 1.0338 | 0.82 | 0.2629 | 1.0960 | 0.82 | 0.8042 | 0.1338 | 0.0435 | | No log | 64.0 | 448 | 1.0373 | 0.84 | 0.2508 | 1.2817 | 0.8400 | 0.8260 | 0.1325 | 0.0433 | | No log | 65.0 | 455 | 1.0266 | 0.83 | 0.2663 | 1.1057 | 0.83 | 0.8163 | 0.1383 | 0.0460 | | No log | 66.0 | 462 | 1.0303 | 0.825 | 0.2549 | 1.1906 | 0.825 | 0.8098 | 0.1399 | 0.0450 | | No log | 67.0 | 469 | 1.0224 | 0.82 | 0.2668 | 1.0920 | 0.82 | 0.8042 | 0.1252 | 0.0433 | | No log | 68.0 | 476 | 1.0274 | 0.845 | 0.2526 | 1.1948 | 0.845 | 0.8368 | 0.1423 | 0.0442 | | No log | 69.0 | 483 | 1.0145 | 0.82 | 0.2647 | 1.0884 | 0.82 | 0.8070 | 0.1345 | 0.0449 | | No log | 70.0 | 490 | 1.0194 | 0.815 | 0.2606 | 1.1076 | 0.815 | 0.8014 | 0.1529 | 0.0446 | | No log | 71.0 | 497 | 1.0153 | 0.825 | 0.2572 | 1.2484 | 0.825 | 0.8142 | 0.1425 | 0.0445 | | 0.6377 | 72.0 | 504 | 1.0265 | 0.815 | 0.2607 | 1.1109 | 0.815 | 0.8039 | 0.1457 | 0.0445 | | 0.6377 | 73.0 | 511 | 1.0081 | 0.82 | 0.2567 | 1.1031 | 0.82 | 0.8040 | 0.1321 | 0.0440 | | 0.6377 | 74.0 | 518 | 1.0135 | 0.825 | 0.2600 | 1.1036 | 0.825 | 0.8074 | 0.1477 | 0.0450 | | 0.6377 | 75.0 | 525 | 1.0053 | 0.82 | 0.2616 | 1.1012 | 0.82 | 0.8044 | 0.1542 | 0.0442 | | 0.6377 | 76.0 | 532 | 1.0187 | 0.82 | 0.2598 | 1.1115 | 0.82 | 0.8069 | 0.1566 | 0.0445 | | 0.6377 | 77.0 | 539 | 1.0127 | 0.82 | 0.2610 | 1.1024 | 0.82 | 0.8097 | 0.1489 | 0.0443 | | 0.6377 | 78.0 | 546 | 1.0079 | 0.82 | 0.2581 | 1.1034 | 0.82 | 0.8069 | 0.1463 | 0.0434 | | 0.6377 | 79.0 | 553 | 1.0097 | 0.815 | 0.2592 | 1.1030 | 0.815 | 0.8014 | 0.1478 | 0.0438 | | 0.6377 | 80.0 | 560 | 1.0131 | 0.835 | 0.2556 | 1.1048 | 0.835 | 0.8281 | 0.1508 | 0.0441 | | 0.6377 | 81.0 | 567 | 1.0183 | 0.82 | 0.2602 | 1.1057 | 0.82 | 0.8044 | 0.1417 | 0.0446 | | 0.6377 | 82.0 | 574 | 1.0190 | 0.815 | 0.2665 | 1.0966 | 0.815 | 0.7987 | 0.1370 | 0.0462 | | 0.6377 | 83.0 | 581 | 1.0117 | 0.815 | 0.2619 | 1.0974 | 0.815 | 0.8014 | 0.1614 | 0.0442 | | 0.6377 | 84.0 | 588 | 1.0099 | 0.82 | 0.2557 | 1.1070 | 0.82 | 0.8044 | 0.1327 | 0.0436 | | 0.6377 | 85.0 | 595 | 1.0088 | 0.82 | 0.2569 | 1.1037 | 0.82 | 0.8044 | 0.1446 | 0.0437 | | 0.6377 | 86.0 | 602 | 1.0110 | 0.82 | 0.2596 | 1.0945 | 0.82 | 0.8043 | 0.1505 | 0.0442 | | 0.6377 | 87.0 | 609 | 1.0151 | 0.815 | 0.2606 | 1.1046 | 0.815 | 0.8014 | 0.1416 | 0.0451 | | 0.6377 | 88.0 | 616 | 1.0101 | 0.815 | 0.2587 | 1.1025 | 0.815 | 0.8014 | 0.1435 | 0.0440 | | 0.6377 | 89.0 | 623 | 1.0106 | 0.815 | 0.2613 | 1.0976 | 0.815 | 0.8014 | 0.1489 | 0.0443 | | 0.6377 | 90.0 | 630 | 1.0097 | 0.815 | 0.2590 | 1.0993 | 0.815 | 0.8014 | 0.1490 | 0.0439 | | 0.6377 | 91.0 | 637 | 1.0098 | 0.815 | 0.2593 | 1.1024 | 0.815 | 0.8014 | 0.1510 | 0.0440 | | 0.6377 | 92.0 | 644 | 1.0116 | 0.815 | 0.2600 | 1.1004 | 0.815 | 0.8014 | 0.1465 | 0.0442 | | 0.6377 | 93.0 | 651 | 1.0107 | 0.815 | 0.2596 | 1.1005 | 0.815 | 0.8014 | 0.1548 | 0.0442 | | 0.6377 | 94.0 | 658 | 1.0110 | 0.815 | 0.2599 | 1.0993 | 0.815 | 0.8014 | 0.1463 | 0.0440 | | 0.6377 | 95.0 | 665 | 1.0106 | 0.815 | 0.2593 | 1.1011 | 0.815 | 0.8014 | 0.1409 | 0.0441 | | 0.6377 | 96.0 | 672 | 1.0106 | 0.815 | 0.2596 | 1.1011 | 0.815 | 0.8014 | 0.1496 | 0.0442 | | 0.6377 | 97.0 | 679 | 1.0109 | 0.815 | 0.2595 | 1.1007 | 0.815 | 0.8014 | 0.1462 | 0.0442 | | 0.6377 | 98.0 | 686 | 1.0107 | 0.815 | 0.2593 | 1.1013 | 0.815 | 0.8014 | 0.1409 | 0.0441 | | 0.6377 | 99.0 | 693 | 1.0107 | 0.815 | 0.2594 | 1.1009 | 0.815 | 0.8014 | 0.1462 | 0.0441 | | 0.6377 | 100.0 | 700 | 1.0108 | 0.815 | 0.2593 | 1.1011 | 0.815 | 0.8014 | 0.1462 | 0.0442 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# dit-base_tobacco-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1418 - Accuracy: 0.84 - Brier Loss: 0.2718 - Nll: 0.9778 - F1 Micro: 0.8400 - F1 Macro: 0.8296 - Ece: 0.1553 - Aurc: 0.0479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 5.6749 | 0.2 | 0.9075 | 8.1551 | 0.2000 | 0.1380 | 0.2949 | 0.8075 | | No log | 2.0 | 14 | 5.1602 | 0.15 | 0.8781 | 7.4212 | 0.15 | 0.1415 | 0.2402 | 0.7606 | | No log | 3.0 | 21 | 4.4947 | 0.46 | 0.7066 | 2.8419 | 0.46 | 0.4202 | 0.3230 | 0.3244 | | No log | 4.0 | 28 | 3.9789 | 0.555 | 0.5827 | 1.6986 | 0.555 | 0.5525 | 0.2796 | 0.2218 | | No log | 5.0 | 35 | 3.6991 | 0.65 | 0.4828 | 1.7197 | 0.65 | 0.6491 | 0.2315 | 0.1491 | | No log | 6.0 | 42 | 3.6495 | 0.68 | 0.4691 | 1.8258 | 0.68 | 0.6586 | 0.2555 | 0.1358 | | No log | 7.0 | 49 | 3.3912 | 0.75 | 0.3899 | 1.8385 | 0.75 | 0.7276 | 0.2237 | 0.0920 | | No log | 8.0 | 56 | 3.3055 | 0.71 | 0.3792 | 1.5754 | 0.7100 | 0.6922 | 0.2130 | 0.1023 | | No log | 9.0 | 63 | 3.3535 | 0.72 | 0.3836 | 1.7076 | 0.72 | 0.7195 | 0.2015 | 0.0978 | | No log | 10.0 | 70 | 3.1877 | 0.785 | 0.3190 | 1.5736 | 0.785 | 0.7582 | 0.1905 | 0.0693 | | No log | 11.0 | 77 | 3.5578 | 0.72 | 0.3812 | 2.2613 | 0.72 | 0.7241 | 0.1684 | 0.0846 | | No log | 12.0 | 84 | 3.3589 | 0.775 | 0.3389 | 1.3228 | 0.775 | 0.7540 | 0.1665 | 0.0764 | | No log | 13.0 | 91 | 3.1097 | 0.805 | 0.2856 | 1.4183 | 0.805 | 0.7929 | 0.1603 | 0.0484 | | No log | 14.0 | 98 | 3.2661 | 0.815 | 0.3146 | 1.9097 | 0.815 | 0.8066 | 0.1753 | 0.0636 | | No log | 15.0 | 105 | 3.3637 | 0.755 | 0.3361 | 1.5166 | 0.755 | 0.7492 | 0.1804 | 0.0720 | | No log | 16.0 | 112 | 3.1495 | 0.8 | 0.2994 | 1.4586 | 0.8000 | 0.7926 | 0.1714 | 0.0604 | | No log | 17.0 | 119 | 3.1573 | 0.8 | 0.2941 | 1.6755 | 0.8000 | 0.7899 | 0.1545 | 0.0577 | | No log | 18.0 | 126 | 3.4445 | 0.77 | 0.3416 | 1.4075 | 0.7700 | 0.7503 | 0.1620 | 0.0807 | | No log | 19.0 | 133 | 3.1292 | 0.805 | 0.2816 | 1.3835 | 0.805 | 0.7815 | 0.1768 | 0.0526 | | No log | 20.0 | 140 | 3.4253 | 0.75 | 0.3459 | 2.0430 | 0.75 | 0.7591 | 0.1697 | 0.0706 | | No log | 21.0 | 147 | 3.1645 | 0.81 | 0.3000 | 1.7363 | 0.81 | 0.8113 | 0.1711 | 0.0614 | | No log | 22.0 | 154 | 3.0823 | 0.815 | 0.2791 | 1.5997 | 0.815 | 0.8020 | 0.1417 | 0.0556 | | No log | 23.0 | 161 | 2.9898 | 0.83 | 0.2521 | 1.4274 | 0.83 | 0.8189 | 0.1589 | 0.0434 | | No log | 24.0 | 168 | 3.0915 | 0.83 | 0.2770 | 1.3516 | 0.83 | 0.8173 | 0.1495 | 0.0538 | | No log | 25.0 | 175 | 3.0405 | 0.825 | 0.2621 | 1.5191 | 0.825 | 0.8048 | 0.1329 | 0.0494 | | No log | 26.0 | 182 | 3.0621 | 0.815 | 0.2735 | 1.0698 | 0.815 | 0.7955 | 0.1617 | 0.0522 | | No log | 27.0 | 189 | 3.0228 | 0.835 | 0.2650 | 1.4235 | 0.835 | 0.8315 | 0.1565 | 0.0502 | | No log | 28.0 | 196 | 3.0677 | 0.82 | 0.2778 | 1.5299 | 0.82 | 0.8165 | 0.1660 | 0.0557 | | No log | 29.0 | 203 | 3.0272 | 0.825 | 0.2699 | 1.4726 | 0.825 | 0.8204 | 0.1643 | 0.0491 | | No log | 30.0 | 210 | 3.1090 | 0.815 | 0.2892 | 1.3258 | 0.815 | 0.8026 | 0.1585 | 0.0536 | | No log | 31.0 | 217 | 3.1069 | 0.81 | 0.2866 | 1.5638 | 0.81 | 0.8050 | 0.1473 | 0.0557 | | No log | 32.0 | 224 | 3.0374 | 0.815 | 0.2765 | 1.2895 | 0.815 | 0.8045 | 0.1476 | 0.0527 | | No log | 33.0 | 231 | 3.0503 | 0.815 | 0.2750 | 1.3113 | 0.815 | 0.7975 | 0.1531 | 0.0517 | | No log | 34.0 | 238 | 2.9852 | 0.82 | 0.2613 | 1.4575 | 0.82 | 0.8110 | 0.1600 | 0.0448 | | No log | 35.0 | 245 | 3.0437 | 0.83 | 0.2724 | 1.3491 | 0.83 | 0.8205 | 0.1622 | 0.0571 | | No log | 36.0 | 252 | 3.0098 | 0.82 | 0.2717 | 1.2671 | 0.82 | 0.8055 | 0.1567 | 0.0519 | | No log | 37.0 | 259 | 3.0025 | 0.845 | 0.2599 | 1.2628 | 0.845 | 0.8255 | 0.1342 | 0.0481 | | No log | 38.0 | 266 | 3.1854 | 0.805 | 0.3015 | 1.2550 | 0.805 | 0.7956 | 0.1560 | 0.0601 | | No log | 39.0 | 273 | 3.0704 | 0.82 | 0.2793 | 1.2393 | 0.82 | 0.8057 | 0.1566 | 0.0557 | | No log | 40.0 | 280 | 3.0739 | 0.825 | 0.2842 | 1.2701 | 0.825 | 0.8169 | 0.1371 | 0.0513 | | No log | 41.0 | 287 | 3.0465 | 0.835 | 0.2747 | 1.2598 | 0.835 | 0.8302 | 0.1449 | 0.0538 | | No log | 42.0 | 294 | 3.0691 | 0.825 | 0.2773 | 1.1796 | 0.825 | 0.8137 | 0.1372 | 0.0511 | | No log | 43.0 | 301 | 3.0734 | 0.84 | 0.2732 | 1.1765 | 0.8400 | 0.8282 | 0.1564 | 0.0565 | | No log | 44.0 | 308 | 3.0262 | 0.845 | 0.2622 | 1.2152 | 0.845 | 0.8306 | 0.1457 | 0.0541 | | No log | 45.0 | 315 | 3.0610 | 0.835 | 0.2727 | 1.2249 | 0.835 | 0.8261 | 0.1606 | 0.0544 | | No log | 46.0 | 322 | 3.0358 | 0.84 | 0.2767 | 1.1020 | 0.8400 | 0.8323 | 0.1416 | 0.0527 | | No log | 47.0 | 329 | 2.9893 | 0.835 | 0.2650 | 1.1536 | 0.835 | 0.8252 | 0.1386 | 0.0493 | | No log | 48.0 | 336 | 3.0498 | 0.84 | 0.2726 | 1.1253 | 0.8400 | 0.8320 | 0.1302 | 0.0535 | | No log | 49.0 | 343 | 2.9816 | 0.845 | 0.2585 | 1.2068 | 0.845 | 0.8355 | 0.1455 | 0.0451 | | No log | 50.0 | 350 | 3.0431 | 0.835 | 0.2686 | 1.0596 | 0.835 | 0.8238 | 0.1542 | 0.0540 | | No log | 51.0 | 357 | 3.0200 | 0.835 | 0.2639 | 1.1806 | 0.835 | 0.8290 | 0.1434 | 0.0501 | | No log | 52.0 | 364 | 3.0217 | 0.845 | 0.2664 | 1.0846 | 0.845 | 0.8324 | 0.1671 | 0.0503 | | No log | 53.0 | 371 | 3.0255 | 0.84 | 0.2649 | 1.1803 | 0.8400 | 0.8318 | 0.1350 | 0.0488 | | No log | 54.0 | 378 | 3.0069 | 0.835 | 0.2616 | 1.2057 | 0.835 | 0.8190 | 0.1284 | 0.0496 | | No log | 55.0 | 385 | 3.0609 | 0.815 | 0.2746 | 1.0378 | 0.815 | 0.7970 | 0.1422 | 0.0490 | | No log | 56.0 | 392 | 3.0111 | 0.84 | 0.2622 | 1.1806 | 0.8400 | 0.8341 | 0.1428 | 0.0513 | | No log | 57.0 | 399 | 3.0050 | 0.84 | 0.2643 | 1.1898 | 0.8400 | 0.8299 | 0.1452 | 0.0494 | | No log | 58.0 | 406 | 3.0426 | 0.84 | 0.2662 | 1.0337 | 0.8400 | 0.8307 | 0.1397 | 0.0514 | | No log | 59.0 | 413 | 3.0427 | 0.835 | 0.2682 | 1.0309 | 0.835 | 0.8247 | 0.1453 | 0.0491 | | No log | 60.0 | 420 | 3.0449 | 0.83 | 0.2744 | 1.0039 | 0.83 | 0.8141 | 0.1436 | 0.0484 | | No log | 61.0 | 427 | 3.0524 | 0.83 | 0.2729 | 1.1480 | 0.83 | 0.8162 | 0.1454 | 0.0477 | | No log | 62.0 | 434 | 3.0290 | 0.835 | 0.2610 | 1.1757 | 0.835 | 0.8264 | 0.1476 | 0.0506 | | No log | 63.0 | 441 | 3.0574 | 0.83 | 0.2712 | 1.0242 | 0.83 | 0.8161 | 0.1464 | 0.0485 | | No log | 64.0 | 448 | 3.0436 | 0.835 | 0.2684 | 1.1326 | 0.835 | 0.8267 | 0.1417 | 0.0470 | | No log | 65.0 | 455 | 3.0170 | 0.84 | 0.2610 | 1.1095 | 0.8400 | 0.8289 | 0.1520 | 0.0492 | | No log | 66.0 | 462 | 3.0176 | 0.835 | 0.2623 | 1.1140 | 0.835 | 0.8225 | 0.1262 | 0.0459 | | No log | 67.0 | 469 | 3.0712 | 0.84 | 0.2735 | 1.0884 | 0.8400 | 0.8296 | 0.1421 | 0.0516 | | No log | 68.0 | 476 | 3.0258 | 0.84 | 0.2670 | 1.1388 | 0.8400 | 0.8279 | 0.1478 | 0.0461 | | No log | 69.0 | 483 | 3.0838 | 0.835 | 0.2707 | 1.0937 | 0.835 | 0.8232 | 0.1425 | 0.0477 | | No log | 70.0 | 490 | 3.1076 | 0.82 | 0.2819 | 1.0030 | 0.82 | 0.7998 | 0.1507 | 0.0480 | | No log | 71.0 | 497 | 3.0696 | 0.84 | 0.2725 | 1.0175 | 0.8400 | 0.8349 | 0.1567 | 0.0501 | | 2.6485 | 72.0 | 504 | 3.0535 | 0.84 | 0.2676 | 1.0079 | 0.8400 | 0.8253 | 0.1351 | 0.0477 | | 2.6485 | 73.0 | 511 | 3.0326 | 0.83 | 0.2667 | 0.9792 | 0.83 | 0.8093 | 0.1334 | 0.0464 | | 2.6485 | 74.0 | 518 | 3.0271 | 0.835 | 0.2616 | 1.0865 | 0.835 | 0.8193 | 0.1223 | 0.0454 | | 2.6485 | 75.0 | 525 | 3.0894 | 0.83 | 0.2732 | 0.9764 | 0.83 | 0.8123 | 0.1446 | 0.0489 | | 2.6485 | 76.0 | 532 | 3.0905 | 0.835 | 0.2730 | 1.0736 | 0.835 | 0.8232 | 0.1578 | 0.0485 | | 2.6485 | 77.0 | 539 | 3.0507 | 0.84 | 0.2646 | 1.0716 | 0.8400 | 0.8279 | 0.1424 | 0.0469 | | 2.6485 | 78.0 | 546 | 3.0981 | 0.845 | 0.2712 | 0.9916 | 0.845 | 0.8324 | 0.1452 | 0.0508 | | 2.6485 | 79.0 | 553 | 3.0820 | 0.84 | 0.2728 | 0.9791 | 0.8400 | 0.8296 | 0.1403 | 0.0473 | | 2.6485 | 80.0 | 560 | 3.0978 | 0.84 | 0.2733 | 0.9864 | 0.8400 | 0.8296 | 0.1480 | 0.0485 | | 2.6485 | 81.0 | 567 | 3.0936 | 0.84 | 0.2716 | 0.9955 | 0.8400 | 0.8296 | 0.1483 | 0.0474 | | 2.6485 | 82.0 | 574 | 3.0937 | 0.845 | 0.2685 | 0.9875 | 0.845 | 0.8324 | 0.1459 | 0.0486 | | 2.6485 | 83.0 | 581 | 3.0940 | 0.84 | 0.2719 | 0.9863 | 0.8400 | 0.8296 | 0.1481 | 0.0470 | | 2.6485 | 84.0 | 588 | 3.0745 | 0.84 | 0.2656 | 1.0795 | 0.8400 | 0.8323 | 0.1460 | 0.0476 | | 2.6485 | 85.0 | 595 | 3.1089 | 0.845 | 0.2681 | 1.0050 | 0.845 | 0.8324 | 0.1568 | 0.0492 | | 2.6485 | 86.0 | 602 | 3.0880 | 0.84 | 0.2695 | 1.0607 | 0.8400 | 0.8296 | 0.1409 | 0.0474 | | 2.6485 | 87.0 | 609 | 3.0848 | 0.84 | 0.2666 | 0.9996 | 0.8400 | 0.8296 | 0.1425 | 0.0470 | | 2.6485 | 88.0 | 616 | 3.1144 | 0.84 | 0.2682 | 0.9937 | 0.8400 | 0.8296 | 0.1380 | 0.0482 | | 2.6485 | 89.0 | 623 | 3.1316 | 0.84 | 0.2711 | 0.9884 | 0.8400 | 0.8296 | 0.1484 | 0.0490 | | 2.6485 | 90.0 | 630 | 3.1312 | 0.84 | 0.2726 | 0.9732 | 0.8400 | 0.8296 | 0.1525 | 0.0488 | | 2.6485 | 91.0 | 637 | 3.1312 | 0.84 | 0.2723 | 0.9794 | 0.8400 | 0.8296 | 0.1475 | 0.0481 | | 2.6485 | 92.0 | 644 | 3.1426 | 0.84 | 0.2731 | 0.9728 | 0.8400 | 0.8296 | 0.1478 | 0.0491 | | 2.6485 | 93.0 | 651 | 3.1351 | 0.84 | 0.2709 | 0.9741 | 0.8400 | 0.8296 | 0.1438 | 0.0483 | | 2.6485 | 94.0 | 658 | 3.1390 | 0.84 | 0.2716 | 0.9764 | 0.8400 | 0.8296 | 0.1576 | 0.0483 | | 2.6485 | 95.0 | 665 | 3.1366 | 0.84 | 0.2711 | 0.9795 | 0.8400 | 0.8296 | 0.1480 | 0.0484 | | 2.6485 | 96.0 | 672 | 3.1337 | 0.84 | 0.2710 | 0.9828 | 0.8400 | 0.8296 | 0.1475 | 0.0478 | | 2.6485 | 97.0 | 679 | 3.1431 | 0.84 | 0.2723 | 0.9767 | 0.8400 | 0.8296 | 0.1587 | 0.0480 | | 2.6485 | 98.0 | 686 | 3.1388 | 0.84 | 0.2713 | 0.9808 | 0.8400 | 0.8296 | 0.1476 | 0.0480 | | 2.6485 | 99.0 | 693 | 3.1420 | 0.84 | 0.2718 | 0.9778 | 0.8400 | 0.8296 | 0.1560 | 0.0480 | | 2.6485 | 100.0 | 700 | 3.1418 | 0.84 | 0.2718 | 0.9778 | 0.8400 | 0.8296 | 0.1553 | 0.0479 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_hint

# dit-base_tobacco-tiny_tobacco3482_hint This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1196 - Accuracy: 0.805 - Brier Loss: 0.3299 - Nll: 1.3687 - F1 Micro: 0.805 - F1 Macro: 0.7917 - Ece: 0.1606 - Aurc: 0.0741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 3.7183 | 0.225 | 0.9191 | 7.9210 | 0.225 | 0.1037 | 0.3197 | 0.7539 | | No log | 2.0 | 14 | 3.2602 | 0.365 | 0.7808 | 4.4817 | 0.3650 | 0.2558 | 0.2908 | 0.5116 | | No log | 3.0 | 21 | 2.8475 | 0.53 | 0.6305 | 2.7026 | 0.53 | 0.4263 | 0.2496 | 0.2812 | | No log | 4.0 | 28 | 2.6118 | 0.575 | 0.5382 | 1.8007 | 0.575 | 0.5478 | 0.2435 | 0.2022 | | No log | 5.0 | 35 | 2.4289 | 0.66 | 0.4404 | 1.6436 | 0.66 | 0.6317 | 0.2124 | 0.1236 | | No log | 6.0 | 42 | 2.4007 | 0.69 | 0.4123 | 1.9255 | 0.69 | 0.6896 | 0.2038 | 0.1195 | | No log | 7.0 | 49 | 2.3239 | 0.745 | 0.3775 | 1.9428 | 0.745 | 0.7365 | 0.1838 | 0.0957 | | No log | 8.0 | 56 | 2.3109 | 0.735 | 0.3660 | 1.6472 | 0.735 | 0.7370 | 0.1729 | 0.0842 | | No log | 9.0 | 63 | 2.3263 | 0.74 | 0.3867 | 1.5935 | 0.74 | 0.7422 | 0.1803 | 0.0944 | | No log | 10.0 | 70 | 2.3878 | 0.735 | 0.4037 | 1.6604 | 0.735 | 0.7039 | 0.2057 | 0.0909 | | No log | 11.0 | 77 | 2.5009 | 0.715 | 0.4220 | 1.8526 | 0.715 | 0.6918 | 0.2184 | 0.1017 | | No log | 12.0 | 84 | 2.5428 | 0.72 | 0.4169 | 1.8799 | 0.72 | 0.6894 | 0.2000 | 0.0979 | | No log | 13.0 | 91 | 2.5197 | 0.765 | 0.3884 | 1.8802 | 0.765 | 0.7268 | 0.1895 | 0.0933 | | No log | 14.0 | 98 | 2.4928 | 0.725 | 0.4147 | 1.6096 | 0.7250 | 0.6923 | 0.2078 | 0.0940 | | No log | 15.0 | 105 | 2.4172 | 0.765 | 0.3748 | 1.6754 | 0.765 | 0.7317 | 0.1874 | 0.0824 | | No log | 16.0 | 112 | 2.4116 | 0.76 | 0.3873 | 1.4155 | 0.76 | 0.7280 | 0.1972 | 0.0890 | | No log | 17.0 | 119 | 2.9196 | 0.73 | 0.4500 | 1.8493 | 0.7300 | 0.7220 | 0.2290 | 0.1657 | | No log | 18.0 | 126 | 2.5815 | 0.765 | 0.4175 | 2.0326 | 0.765 | 0.7467 | 0.2125 | 0.0990 | | No log | 19.0 | 133 | 2.7076 | 0.735 | 0.4475 | 1.8526 | 0.735 | 0.7152 | 0.2062 | 0.1049 | | No log | 20.0 | 140 | 2.6951 | 0.71 | 0.4709 | 2.0521 | 0.7100 | 0.7258 | 0.2380 | 0.1188 | | No log | 21.0 | 147 | 2.4037 | 0.765 | 0.4013 | 1.8740 | 0.765 | 0.7691 | 0.2009 | 0.0949 | | No log | 22.0 | 154 | 2.6585 | 0.73 | 0.4303 | 2.0299 | 0.7300 | 0.7170 | 0.2110 | 0.1004 | | No log | 23.0 | 161 | 2.4320 | 0.75 | 0.3950 | 1.8720 | 0.75 | 0.7340 | 0.2004 | 0.0895 | | No log | 24.0 | 168 | 2.4891 | 0.74 | 0.4199 | 1.6458 | 0.74 | 0.7458 | 0.2227 | 0.1128 | | No log | 25.0 | 175 | 2.6550 | 0.705 | 0.4833 | 1.7755 | 0.705 | 0.7042 | 0.2478 | 0.1145 | | No log | 26.0 | 182 | 2.3191 | 0.765 | 0.3965 | 1.6941 | 0.765 | 0.7415 | 0.1954 | 0.0731 | | No log | 27.0 | 189 | 2.3000 | 0.785 | 0.3763 | 1.3143 | 0.785 | 0.7416 | 0.2026 | 0.0712 | | No log | 28.0 | 196 | 2.2047 | 0.78 | 0.3409 | 1.4818 | 0.78 | 0.7694 | 0.1759 | 0.0688 | | No log | 29.0 | 203 | 2.3587 | 0.77 | 0.3781 | 1.6779 | 0.7700 | 0.7571 | 0.1937 | 0.0766 | | No log | 30.0 | 210 | 2.5027 | 0.75 | 0.4400 | 1.8454 | 0.75 | 0.7338 | 0.2232 | 0.0817 | | No log | 31.0 | 217 | 2.4092 | 0.77 | 0.3899 | 1.5010 | 0.7700 | 0.7498 | 0.1987 | 0.0710 | | No log | 32.0 | 224 | 2.7655 | 0.74 | 0.4520 | 2.0720 | 0.74 | 0.7177 | 0.2266 | 0.0855 | | No log | 33.0 | 231 | 2.3814 | 0.76 | 0.3979 | 1.4053 | 0.76 | 0.7352 | 0.1982 | 0.0754 | | No log | 34.0 | 238 | 2.3946 | 0.775 | 0.3790 | 1.6969 | 0.775 | 0.7387 | 0.1908 | 0.0876 | | No log | 35.0 | 245 | 2.5158 | 0.775 | 0.4064 | 1.4329 | 0.775 | 0.7428 | 0.2068 | 0.0929 | | No log | 36.0 | 252 | 2.4920 | 0.75 | 0.4281 | 1.5724 | 0.75 | 0.7470 | 0.2161 | 0.1028 | | No log | 37.0 | 259 | 2.4541 | 0.765 | 0.3842 | 1.5272 | 0.765 | 0.7163 | 0.1918 | 0.0853 | | No log | 38.0 | 266 | 2.3785 | 0.82 | 0.3319 | 1.6360 | 0.82 | 0.7944 | 0.1732 | 0.0811 | | No log | 39.0 | 273 | 2.3721 | 0.77 | 0.3831 | 1.2724 | 0.7700 | 0.7649 | 0.1880 | 0.0818 | | No log | 40.0 | 280 | 2.5684 | 0.795 | 0.3494 | 1.7291 | 0.795 | 0.7724 | 0.1771 | 0.1126 | | No log | 41.0 | 287 | 2.4835 | 0.78 | 0.3799 | 1.7700 | 0.78 | 0.7594 | 0.2024 | 0.0821 | | No log | 42.0 | 294 | 2.4690 | 0.795 | 0.3678 | 1.6379 | 0.795 | 0.7804 | 0.1836 | 0.0840 | | No log | 43.0 | 301 | 2.3069 | 0.77 | 0.3809 | 1.4970 | 0.7700 | 0.7621 | 0.1807 | 0.0784 | | No log | 44.0 | 308 | 2.4424 | 0.795 | 0.3558 | 1.7162 | 0.795 | 0.7857 | 0.1879 | 0.0667 | | No log | 45.0 | 315 | 2.0018 | 0.86 | 0.2387 | 1.4924 | 0.8600 | 0.8558 | 0.1180 | 0.0526 | | No log | 46.0 | 322 | 2.4174 | 0.785 | 0.3593 | 1.7688 | 0.785 | 0.7662 | 0.1859 | 0.0773 | | No log | 47.0 | 329 | 2.1816 | 0.84 | 0.2862 | 1.5093 | 0.8400 | 0.8230 | 0.1459 | 0.0679 | | No log | 48.0 | 336 | 2.3006 | 0.78 | 0.3601 | 1.7675 | 0.78 | 0.7496 | 0.1847 | 0.0752 | | No log | 49.0 | 343 | 2.2952 | 0.81 | 0.3231 | 1.4851 | 0.81 | 0.7865 | 0.1691 | 0.0656 | | No log | 50.0 | 350 | 2.1346 | 0.8 | 0.3132 | 1.4625 | 0.8000 | 0.7937 | 0.1660 | 0.0789 | | No log | 51.0 | 357 | 2.2935 | 0.81 | 0.3383 | 1.5764 | 0.81 | 0.7914 | 0.1770 | 0.0717 | | No log | 52.0 | 364 | 2.1792 | 0.825 | 0.3116 | 1.5652 | 0.825 | 0.8163 | 0.1454 | 0.0654 | | No log | 53.0 | 371 | 2.1231 | 0.81 | 0.3066 | 1.3012 | 0.81 | 0.8062 | 0.1552 | 0.0604 | | No log | 54.0 | 378 | 1.9712 | 0.825 | 0.2854 | 1.2891 | 0.825 | 0.8137 | 0.1430 | 0.0521 | | No log | 55.0 | 385 | 2.0133 | 0.825 | 0.2839 | 1.3994 | 0.825 | 0.8086 | 0.1433 | 0.0560 | | No log | 56.0 | 392 | 1.9978 | 0.835 | 0.2800 | 1.4348 | 0.835 | 0.8232 | 0.1415 | 0.0573 | | No log | 57.0 | 399 | 1.9847 | 0.83 | 0.2825 | 1.3907 | 0.83 | 0.8153 | 0.1421 | 0.0560 | | No log | 58.0 | 406 | 1.9892 | 0.83 | 0.2832 | 1.4502 | 0.83 | 0.8153 | 0.1503 | 0.0566 | | No log | 59.0 | 413 | 1.9848 | 0.83 | 0.2851 | 1.4506 | 0.83 | 0.8156 | 0.1462 | 0.0560 | | No log | 60.0 | 420 | 1.9871 | 0.835 | 0.2910 | 1.4527 | 0.835 | 0.8191 | 0.1608 | 0.0566 | | No log | 61.0 | 427 | 1.9914 | 0.825 | 0.2932 | 1.4490 | 0.825 | 0.8095 | 0.1464 | 0.0587 | | No log | 62.0 | 434 | 1.9908 | 0.825 | 0.2958 | 1.4459 | 0.825 | 0.8095 | 0.1493 | 0.0597 | | No log | 63.0 | 441 | 1.9954 | 0.825 | 0.3012 | 1.4480 | 0.825 | 0.8095 | 0.1469 | 0.0606 | | No log | 64.0 | 448 | 2.0111 | 0.82 | 0.3050 | 1.4487 | 0.82 | 0.8026 | 0.1507 | 0.0619 | | No log | 65.0 | 455 | 2.0212 | 0.82 | 0.3046 | 1.4469 | 0.82 | 0.8026 | 0.1604 | 0.0634 | | No log | 66.0 | 462 | 2.0170 | 0.82 | 0.3059 | 1.4443 | 0.82 | 0.8040 | 0.1539 | 0.0639 | | No log | 67.0 | 469 | 2.0170 | 0.815 | 0.3056 | 1.4496 | 0.815 | 0.8019 | 0.1534 | 0.0643 | | No log | 68.0 | 476 | 2.0316 | 0.82 | 0.3115 | 1.4522 | 0.82 | 0.8026 | 0.1606 | 0.0645 | | No log | 69.0 | 483 | 2.0335 | 0.805 | 0.3132 | 1.3831 | 0.805 | 0.7855 | 0.1607 | 0.0654 | | No log | 70.0 | 490 | 2.0362 | 0.815 | 0.3106 | 1.3834 | 0.815 | 0.7989 | 0.1614 | 0.0655 | | No log | 71.0 | 497 | 2.0318 | 0.815 | 0.3105 | 1.3893 | 0.815 | 0.7947 | 0.1541 | 0.0662 | | 1.3661 | 72.0 | 504 | 2.0434 | 0.815 | 0.3135 | 1.4473 | 0.815 | 0.7955 | 0.1579 | 0.0653 | | 1.3661 | 73.0 | 511 | 2.0517 | 0.81 | 0.3139 | 1.3838 | 0.81 | 0.7917 | 0.1564 | 0.0680 | | 1.3661 | 74.0 | 518 | 2.0594 | 0.82 | 0.3162 | 1.3783 | 0.82 | 0.7975 | 0.1626 | 0.0681 | | 1.3661 | 75.0 | 525 | 2.0628 | 0.815 | 0.3210 | 1.3752 | 0.815 | 0.7944 | 0.1598 | 0.0706 | | 1.3661 | 76.0 | 532 | 2.0605 | 0.81 | 0.3158 | 1.3711 | 0.81 | 0.7886 | 0.1639 | 0.0684 | | 1.3661 | 77.0 | 539 | 2.0718 | 0.815 | 0.3187 | 1.3860 | 0.815 | 0.7944 | 0.1710 | 0.0705 | | 1.3661 | 78.0 | 546 | 2.0749 | 0.815 | 0.3168 | 1.3658 | 0.815 | 0.7958 | 0.1569 | 0.0713 | | 1.3661 | 79.0 | 553 | 2.0796 | 0.83 | 0.3188 | 1.3016 | 0.83 | 0.8147 | 0.1646 | 0.0722 | | 1.3661 | 80.0 | 560 | 2.0746 | 0.81 | 0.3210 | 1.3758 | 0.81 | 0.7916 | 0.1580 | 0.0729 | | 1.3661 | 81.0 | 567 | 2.0819 | 0.815 | 0.3194 | 1.3686 | 0.815 | 0.7913 | 0.1576 | 0.0722 | | 1.3661 | 82.0 | 574 | 2.0866 | 0.825 | 0.3182 | 1.3627 | 0.825 | 0.8085 | 0.1602 | 0.0718 | | 1.3661 | 83.0 | 581 | 2.0942 | 0.815 | 0.3246 | 1.3249 | 0.815 | 0.8008 | 0.1591 | 0.0727 | | 1.3661 | 84.0 | 588 | 2.0938 | 0.815 | 0.3246 | 1.3680 | 0.815 | 0.7984 | 0.1848 | 0.0727 | | 1.3661 | 85.0 | 595 | 2.0912 | 0.82 | 0.3222 | 1.3662 | 0.82 | 0.8012 | 0.1594 | 0.0702 | | 1.3661 | 86.0 | 602 | 2.0941 | 0.82 | 0.3234 | 1.3764 | 0.82 | 0.8012 | 0.1576 | 0.0738 | | 1.3661 | 87.0 | 609 | 2.1037 | 0.8 | 0.3304 | 1.3821 | 0.8000 | 0.7821 | 0.1599 | 0.0740 | | 1.3661 | 88.0 | 616 | 2.1098 | 0.805 | 0.3288 | 1.3587 | 0.805 | 0.7932 | 0.1678 | 0.0718 | | 1.3661 | 89.0 | 623 | 2.1119 | 0.81 | 0.3276 | 1.3636 | 0.81 | 0.7945 | 0.1622 | 0.0728 | | 1.3661 | 90.0 | 630 | 2.1078 | 0.805 | 0.3279 | 1.3641 | 0.805 | 0.7914 | 0.1734 | 0.0737 | | 1.3661 | 91.0 | 637 | 2.1110 | 0.8 | 0.3296 | 1.3686 | 0.8000 | 0.7879 | 0.1636 | 0.0744 | | 1.3661 | 92.0 | 644 | 2.1150 | 0.8 | 0.3317 | 1.3685 | 0.8000 | 0.7879 | 0.1730 | 0.0742 | | 1.3661 | 93.0 | 651 | 2.1146 | 0.8 | 0.3303 | 1.3693 | 0.8000 | 0.7881 | 0.1631 | 0.0742 | | 1.3661 | 94.0 | 658 | 2.1153 | 0.805 | 0.3292 | 1.3657 | 0.805 | 0.7917 | 0.1676 | 0.0734 | | 1.3661 | 95.0 | 665 | 2.1188 | 0.805 | 0.3298 | 1.3683 | 0.805 | 0.7917 | 0.1690 | 0.0735 | | 1.3661 | 96.0 | 672 | 2.1183 | 0.805 | 0.3291 | 1.3691 | 0.805 | 0.7914 | 0.1687 | 0.0742 | | 1.3661 | 97.0 | 679 | 2.1155 | 0.81 | 0.3271 | 1.3664 | 0.81 | 0.7942 | 0.1599 | 0.0743 | | 1.3661 | 98.0 | 686 | 2.1183 | 0.805 | 0.3285 | 1.3673 | 0.805 | 0.7914 | 0.1638 | 0.0740 | | 1.3661 | 99.0 | 693 | 2.1179 | 0.805 | 0.3297 | 1.3686 | 0.805 | 0.7917 | 0.1613 | 0.0741 | | 1.3661 | 100.0 | 700 | 2.1196 | 0.805 | 0.3299 | 1.3687 | 0.805 | 0.7917 | 0.1606 | 0.0741 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_small_sgd_00001_fold1

# hushem_5x_deit_small_sgd_00001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6074 - Accuracy: 0.2222 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4194 | 1.0 | 27 | 1.6162 | 0.2222 | | 1.4315 | 2.0 | 54 | 1.6158 | 0.2222 | | 1.4532 | 3.0 | 81 | 1.6154 | 0.2222 | | 1.4652 | 4.0 | 108 | 1.6150 | 0.2222 | | 1.4244 | 5.0 | 135 | 1.6147 | 0.2222 | | 1.4622 | 6.0 | 162 | 1.6143 | 0.2222 | | 1.4528 | 7.0 | 189 | 1.6140 | 0.2222 | | 1.4262 | 8.0 | 216 | 1.6136 | 0.2222 | | 1.4181 | 9.0 | 243 | 1.6133 | 0.2222 | | 1.4163 | 10.0 | 270 | 1.6130 | 0.2222 | | 1.4463 | 11.0 | 297 | 1.6127 | 0.2222 | | 1.4137 | 12.0 | 324 | 1.6124 | 0.2222 | | 1.4131 | 13.0 | 351 | 1.6121 | 0.2222 | | 1.4148 | 14.0 | 378 | 1.6118 | 0.2222 | | 1.444 | 15.0 | 405 | 1.6115 | 0.2222 | | 1.4135 | 16.0 | 432 | 1.6113 | 0.2222 | | 1.4356 | 17.0 | 459 | 1.6110 | 0.2222 | | 1.4146 | 18.0 | 486 | 1.6108 | 0.2222 | | 1.4096 | 19.0 | 513 | 1.6105 | 0.2222 | | 1.4038 | 20.0 | 540 | 1.6103 | 0.2222 | | 1.3926 | 21.0 | 567 | 1.6101 | 0.2222 | | 1.4332 | 22.0 | 594 | 1.6099 | 0.2222 | | 1.4214 | 23.0 | 621 | 1.6097 | 0.2222 | | 1.4083 | 24.0 | 648 | 1.6095 | 0.2222 | | 1.4271 | 25.0 | 675 | 1.6093 | 0.2222 | | 1.4496 | 26.0 | 702 | 1.6091 | 0.2222 | | 1.4117 | 27.0 | 729 | 1.6090 | 0.2222 | | 1.403 | 28.0 | 756 | 1.6088 | 0.2222 | | 1.3913 | 29.0 | 783 | 1.6087 | 0.2222 | | 1.4302 | 30.0 | 810 | 1.6085 | 0.2222 | | 1.4037 | 31.0 | 837 | 1.6084 | 0.2222 | | 1.4442 | 32.0 | 864 | 1.6083 | 0.2222 | | 1.4272 | 33.0 | 891 | 1.6082 | 0.2222 | | 1.4095 | 34.0 | 918 | 1.6080 | 0.2222 | | 1.4234 | 35.0 | 945 | 1.6079 | 0.2222 | | 1.4343 | 36.0 | 972 | 1.6079 | 0.2222 | | 1.4253 | 37.0 | 999 | 1.6078 | 0.2222 | | 1.4109 | 38.0 | 1026 | 1.6077 | 0.2222 | | 1.4096 | 39.0 | 1053 | 1.6076 | 0.2222 | | 1.3772 | 40.0 | 1080 | 1.6076 | 0.2222 | | 1.4046 | 41.0 | 1107 | 1.6075 | 0.2222 | | 1.384 | 42.0 | 1134 | 1.6075 | 0.2222 | | 1.4202 | 43.0 | 1161 | 1.6075 | 0.2222 | | 1.3963 | 44.0 | 1188 | 1.6074 | 0.2222 | | 1.4183 | 45.0 | 1215 | 1.6074 | 0.2222 | | 1.3888 | 46.0 | 1242 | 1.6074 | 0.2222 | | 1.4088 | 47.0 | 1269 | 1.6074 | 0.2222 | | 1.393 | 48.0 | 1296 | 1.6074 | 0.2222 | | 1.4397 | 49.0 | 1323 | 1.6074 | 0.2222 | | 1.4472 | 50.0 | 1350 | 1.6074 | 0.2222 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_00001_fold2

# hushem_5x_deit_small_sgd_00001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4921 - Accuracy: 0.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5247 | 1.0 | 27 | 1.5122 | 0.1778 | | 1.5208 | 2.0 | 54 | 1.5113 | 0.1778 | | 1.5431 | 3.0 | 81 | 1.5104 | 0.1778 | | 1.5874 | 4.0 | 108 | 1.5095 | 0.1778 | | 1.5185 | 5.0 | 135 | 1.5086 | 0.1778 | | 1.5124 | 6.0 | 162 | 1.5078 | 0.1778 | | 1.4656 | 7.0 | 189 | 1.5070 | 0.1778 | | 1.5113 | 8.0 | 216 | 1.5062 | 0.1778 | | 1.5043 | 9.0 | 243 | 1.5054 | 0.1778 | | 1.505 | 10.0 | 270 | 1.5047 | 0.1778 | | 1.4599 | 11.0 | 297 | 1.5040 | 0.1778 | | 1.5036 | 12.0 | 324 | 1.5033 | 0.1778 | | 1.5237 | 13.0 | 351 | 1.5026 | 0.1778 | | 1.511 | 14.0 | 378 | 1.5019 | 0.1778 | | 1.5324 | 15.0 | 405 | 1.5013 | 0.1778 | | 1.5272 | 16.0 | 432 | 1.5007 | 0.1778 | | 1.5263 | 17.0 | 459 | 1.5002 | 0.1778 | | 1.4937 | 18.0 | 486 | 1.4996 | 0.1778 | | 1.5117 | 19.0 | 513 | 1.4991 | 0.1778 | | 1.516 | 20.0 | 540 | 1.4985 | 0.1778 | | 1.5298 | 21.0 | 567 | 1.4981 | 0.1778 | | 1.5031 | 22.0 | 594 | 1.4976 | 0.1778 | | 1.496 | 23.0 | 621 | 1.4971 | 0.1778 | | 1.4984 | 24.0 | 648 | 1.4967 | 0.2 | | 1.4849 | 25.0 | 675 | 1.4963 | 0.2 | | 1.5277 | 26.0 | 702 | 1.4959 | 0.2 | | 1.4813 | 27.0 | 729 | 1.4955 | 0.2 | | 1.5008 | 28.0 | 756 | 1.4952 | 0.2 | | 1.5143 | 29.0 | 783 | 1.4948 | 0.2 | | 1.5063 | 30.0 | 810 | 1.4945 | 0.2 | | 1.5197 | 31.0 | 837 | 1.4942 | 0.2 | | 1.4689 | 32.0 | 864 | 1.4940 | 0.2 | | 1.5261 | 33.0 | 891 | 1.4937 | 0.2 | | 1.5047 | 34.0 | 918 | 1.4935 | 0.2 | | 1.4608 | 35.0 | 945 | 1.4933 | 0.2 | | 1.5134 | 36.0 | 972 | 1.4931 | 0.2 | | 1.4999 | 37.0 | 999 | 1.4929 | 0.2 | | 1.4901 | 38.0 | 1026 | 1.4928 | 0.2 | | 1.4933 | 39.0 | 1053 | 1.4926 | 0.2 | | 1.5285 | 40.0 | 1080 | 1.4925 | 0.2 | | 1.5189 | 41.0 | 1107 | 1.4924 | 0.2 | | 1.5357 | 42.0 | 1134 | 1.4923 | 0.2 | | 1.5726 | 43.0 | 1161 | 1.4923 | 0.2 | | 1.4926 | 44.0 | 1188 | 1.4922 | 0.2 | | 1.4915 | 45.0 | 1215 | 1.4922 | 0.2 | | 1.4934 | 46.0 | 1242 | 1.4921 | 0.2 | | 1.5214 | 47.0 | 1269 | 1.4921 | 0.2 | | 1.5071 | 48.0 | 1296 | 1.4921 | 0.2 | | 1.5711 | 49.0 | 1323 | 1.4921 | 0.2 | | 1.4665 | 50.0 | 1350 | 1.4921 | 0.2 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_00001_fold3

# hushem_5x_deit_small_sgd_00001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5763 - Accuracy: 0.2093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5114 | 1.0 | 28 | 1.6002 | 0.1860 | | 1.516 | 2.0 | 56 | 1.5991 | 0.2093 | | 1.448 | 3.0 | 84 | 1.5981 | 0.2093 | | 1.5009 | 4.0 | 112 | 1.5971 | 0.2093 | | 1.646 | 5.0 | 140 | 1.5960 | 0.2093 | | 1.55 | 6.0 | 168 | 1.5950 | 0.2093 | | 1.5383 | 7.0 | 196 | 1.5941 | 0.2093 | | 1.4892 | 8.0 | 224 | 1.5932 | 0.2093 | | 1.5021 | 9.0 | 252 | 1.5922 | 0.2093 | | 1.5057 | 10.0 | 280 | 1.5913 | 0.2093 | | 1.5326 | 11.0 | 308 | 1.5905 | 0.2093 | | 1.5462 | 12.0 | 336 | 1.5897 | 0.2093 | | 1.5032 | 13.0 | 364 | 1.5889 | 0.2093 | | 1.4978 | 14.0 | 392 | 1.5881 | 0.2093 | | 1.4757 | 15.0 | 420 | 1.5874 | 0.2093 | | 1.4813 | 16.0 | 448 | 1.5867 | 0.2093 | | 1.4635 | 17.0 | 476 | 1.5860 | 0.2093 | | 1.511 | 18.0 | 504 | 1.5853 | 0.2093 | | 1.4905 | 19.0 | 532 | 1.5847 | 0.2093 | | 1.4302 | 20.0 | 560 | 1.5841 | 0.2093 | | 1.5108 | 21.0 | 588 | 1.5835 | 0.2093 | | 1.504 | 22.0 | 616 | 1.5829 | 0.2093 | | 1.4924 | 23.0 | 644 | 1.5824 | 0.2093 | | 1.5112 | 24.0 | 672 | 1.5819 | 0.2093 | | 1.5256 | 25.0 | 700 | 1.5814 | 0.2093 | | 1.4493 | 26.0 | 728 | 1.5809 | 0.2093 | | 1.4989 | 27.0 | 756 | 1.5805 | 0.2093 | | 1.5477 | 28.0 | 784 | 1.5801 | 0.2093 | | 1.5116 | 29.0 | 812 | 1.5797 | 0.2093 | | 1.4275 | 30.0 | 840 | 1.5793 | 0.2093 | | 1.453 | 31.0 | 868 | 1.5790 | 0.2093 | | 1.524 | 32.0 | 896 | 1.5786 | 0.2093 | | 1.5026 | 33.0 | 924 | 1.5783 | 0.2093 | | 1.4458 | 34.0 | 952 | 1.5780 | 0.2093 | | 1.4708 | 35.0 | 980 | 1.5778 | 0.2093 | | 1.5117 | 36.0 | 1008 | 1.5776 | 0.2093 | | 1.5594 | 37.0 | 1036 | 1.5773 | 0.2093 | | 1.5028 | 38.0 | 1064 | 1.5771 | 0.2093 | | 1.4691 | 39.0 | 1092 | 1.5770 | 0.2093 | | 1.5214 | 40.0 | 1120 | 1.5768 | 0.2093 | | 1.5285 | 41.0 | 1148 | 1.5767 | 0.2093 | | 1.4667 | 42.0 | 1176 | 1.5766 | 0.2093 | | 1.4652 | 43.0 | 1204 | 1.5765 | 0.2093 | | 1.4952 | 44.0 | 1232 | 1.5765 | 0.2093 | | 1.4825 | 45.0 | 1260 | 1.5764 | 0.2093 | | 1.4816 | 46.0 | 1288 | 1.5764 | 0.2093 | | 1.4911 | 47.0 | 1316 | 1.5764 | 0.2093 | | 1.5027 | 48.0 | 1344 | 1.5763 | 0.2093 | | 1.4424 | 49.0 | 1372 | 1.5763 | 0.2093 | | 1.4881 | 50.0 | 1400 | 1.5763 | 0.2093 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_simkd

# dit-base_tobacco-tiny_tobacco3482_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7298 - Accuracy: 0.8 - Brier Loss: 0.3356 - Nll: 1.1950 - F1 Micro: 0.8000 - F1 Macro: 0.7677 - Ece: 0.2868 - Aurc: 0.0614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 1.0044 | 0.11 | 0.8970 | 5.3755 | 0.11 | 0.0297 | 0.1810 | 0.9082 | | No log | 2.0 | 100 | 0.9997 | 0.27 | 0.8946 | 5.6759 | 0.27 | 0.1038 | 0.2752 | 0.7229 | | No log | 3.0 | 150 | 0.9946 | 0.345 | 0.8902 | 4.6234 | 0.345 | 0.1969 | 0.3377 | 0.6577 | | No log | 4.0 | 200 | 0.9814 | 0.4 | 0.8686 | 3.0912 | 0.4000 | 0.2605 | 0.3687 | 0.3808 | | No log | 5.0 | 250 | 0.9618 | 0.56 | 0.8277 | 2.9065 | 0.56 | 0.4439 | 0.4769 | 0.2239 | | No log | 6.0 | 300 | 0.9225 | 0.58 | 0.7429 | 2.5647 | 0.58 | 0.4408 | 0.4561 | 0.1944 | | No log | 7.0 | 350 | 0.8843 | 0.705 | 0.6414 | 2.4145 | 0.705 | 0.5531 | 0.4493 | 0.1261 | | No log | 8.0 | 400 | 0.8627 | 0.685 | 0.5773 | 2.4171 | 0.685 | 0.5755 | 0.3710 | 0.1378 | | No log | 9.0 | 450 | 0.8252 | 0.73 | 0.5158 | 1.6133 | 0.7300 | 0.6403 | 0.3706 | 0.1066 | | 0.9306 | 10.0 | 500 | 0.8164 | 0.74 | 0.4861 | 1.9299 | 0.74 | 0.6672 | 0.3352 | 0.1090 | | 0.9306 | 11.0 | 550 | 0.8350 | 0.67 | 0.5078 | 2.0291 | 0.67 | 0.6083 | 0.3271 | 0.1514 | | 0.9306 | 12.0 | 600 | 0.8089 | 0.695 | 0.4680 | 1.6726 | 0.695 | 0.6065 | 0.3049 | 0.1040 | | 0.9306 | 13.0 | 650 | 0.7847 | 0.78 | 0.4097 | 1.3710 | 0.78 | 0.7067 | 0.3090 | 0.0825 | | 0.9306 | 14.0 | 700 | 0.7793 | 0.8 | 0.3952 | 1.4382 | 0.8000 | 0.7351 | 0.3189 | 0.0823 | | 0.9306 | 15.0 | 750 | 0.7756 | 0.775 | 0.3979 | 1.2640 | 0.775 | 0.6997 | 0.2950 | 0.0835 | | 0.9306 | 16.0 | 800 | 0.7888 | 0.765 | 0.3927 | 1.2499 | 0.765 | 0.6894 | 0.3175 | 0.0719 | | 0.9306 | 17.0 | 850 | 0.7596 | 0.795 | 0.3603 | 1.1834 | 0.795 | 0.7250 | 0.2930 | 0.0673 | | 0.9306 | 18.0 | 900 | 0.7581 | 0.795 | 0.3580 | 1.1902 | 0.795 | 0.7241 | 0.3104 | 0.0665 | | 0.9306 | 19.0 | 950 | 0.7546 | 0.81 | 0.3547 | 1.1055 | 0.81 | 0.7583 | 0.3024 | 0.0621 | | 0.7329 | 20.0 | 1000 | 0.7520 | 0.81 | 0.3547 | 1.1284 | 0.81 | 0.7533 | 0.3209 | 0.0581 | | 0.7329 | 21.0 | 1050 | 0.7669 | 0.775 | 0.3906 | 1.3812 | 0.775 | 0.7502 | 0.3212 | 0.0794 | | 0.7329 | 22.0 | 1100 | 0.7532 | 0.81 | 0.3591 | 1.0982 | 0.81 | 0.7836 | 0.3035 | 0.0708 | | 0.7329 | 23.0 | 1150 | 0.7519 | 0.805 | 0.3643 | 1.0628 | 0.805 | 0.7742 | 0.2813 | 0.0732 | | 0.7329 | 24.0 | 1200 | 0.7494 | 0.795 | 0.3614 | 1.1123 | 0.795 | 0.7618 | 0.2988 | 0.0699 | | 0.7329 | 25.0 | 1250 | 0.7517 | 0.79 | 0.3696 | 1.0703 | 0.79 | 0.7606 | 0.3081 | 0.0800 | | 0.7329 | 26.0 | 1300 | 0.7513 | 0.795 | 0.3629 | 1.1020 | 0.795 | 0.7769 | 0.2797 | 0.0722 | | 0.7329 | 27.0 | 1350 | 0.7485 | 0.795 | 0.3552 | 1.0352 | 0.795 | 0.7671 | 0.2678 | 0.0684 | | 0.7329 | 28.0 | 1400 | 0.7442 | 0.805 | 0.3471 | 1.0956 | 0.805 | 0.7706 | 0.2807 | 0.0630 | | 0.7329 | 29.0 | 1450 | 0.7473 | 0.795 | 0.3592 | 1.1204 | 0.795 | 0.7685 | 0.2897 | 0.0722 | | 0.6917 | 30.0 | 1500 | 0.7449 | 0.815 | 0.3482 | 1.0584 | 0.815 | 0.7862 | 0.2949 | 0.0629 | | 0.6917 | 31.0 | 1550 | 0.7443 | 0.8 | 0.3512 | 1.1010 | 0.8000 | 0.7770 | 0.2954 | 0.0622 | | 0.6917 | 32.0 | 1600 | 0.7454 | 0.785 | 0.3543 | 1.0994 | 0.785 | 0.7631 | 0.2957 | 0.0639 | | 0.6917 | 33.0 | 1650 | 0.7421 | 0.815 | 0.3449 | 1.1826 | 0.815 | 0.7853 | 0.2996 | 0.0592 | | 0.6917 | 34.0 | 1700 | 0.7454 | 0.79 | 0.3559 | 1.1000 | 0.79 | 0.7597 | 0.2964 | 0.0659 | | 0.6917 | 35.0 | 1750 | 0.7418 | 0.815 | 0.3477 | 1.1616 | 0.815 | 0.7867 | 0.3133 | 0.0617 | | 0.6917 | 36.0 | 1800 | 0.7425 | 0.815 | 0.3464 | 1.1274 | 0.815 | 0.7949 | 0.3173 | 0.0578 | | 0.6917 | 37.0 | 1850 | 0.7421 | 0.8 | 0.3448 | 1.1909 | 0.8000 | 0.7732 | 0.2900 | 0.0639 | | 0.6917 | 38.0 | 1900 | 0.7415 | 0.795 | 0.3471 | 1.1816 | 0.795 | 0.7594 | 0.2860 | 0.0655 | | 0.6917 | 39.0 | 1950 | 0.7405 | 0.78 | 0.3502 | 1.1084 | 0.78 | 0.7491 | 0.2709 | 0.0650 | | 0.6764 | 40.0 | 2000 | 0.7398 | 0.81 | 0.3457 | 1.1746 | 0.81 | 0.7797 | 0.2973 | 0.0603 | | 0.6764 | 41.0 | 2050 | 0.7394 | 0.805 | 0.3437 | 1.1201 | 0.805 | 0.7764 | 0.2915 | 0.0626 | | 0.6764 | 42.0 | 2100 | 0.7380 | 0.81 | 0.3420 | 1.0987 | 0.81 | 0.7861 | 0.2815 | 0.0583 | | 0.6764 | 43.0 | 2150 | 0.7386 | 0.8 | 0.3437 | 1.1855 | 0.8000 | 0.7667 | 0.2804 | 0.0617 | | 0.6764 | 44.0 | 2200 | 0.7398 | 0.795 | 0.3437 | 1.1138 | 0.795 | 0.7660 | 0.2719 | 0.0614 | | 0.6764 | 45.0 | 2250 | 0.7384 | 0.805 | 0.3441 | 1.1100 | 0.805 | 0.7699 | 0.3065 | 0.0628 | | 0.6764 | 46.0 | 2300 | 0.7389 | 0.79 | 0.3488 | 1.1079 | 0.79 | 0.7552 | 0.2615 | 0.0647 | | 0.6764 | 47.0 | 2350 | 0.7368 | 0.8 | 0.3440 | 1.1095 | 0.8000 | 0.7698 | 0.2908 | 0.0624 | | 0.6764 | 48.0 | 2400 | 0.7365 | 0.8 | 0.3452 | 1.0995 | 0.8000 | 0.7739 | 0.2838 | 0.0645 | | 0.6764 | 49.0 | 2450 | 0.7365 | 0.8 | 0.3367 | 1.0442 | 0.8000 | 0.7712 | 0.2735 | 0.0585 | | 0.6662 | 50.0 | 2500 | 0.7342 | 0.815 | 0.3379 | 1.1009 | 0.815 | 0.7815 | 0.2964 | 0.0584 | | 0.6662 | 51.0 | 2550 | 0.7340 | 0.805 | 0.3358 | 1.0985 | 0.805 | 0.7723 | 0.2635 | 0.0593 | | 0.6662 | 52.0 | 2600 | 0.7370 | 0.8 | 0.3429 | 1.1227 | 0.8000 | 0.7709 | 0.2841 | 0.0603 | | 0.6662 | 53.0 | 2650 | 0.7325 | 0.81 | 0.3380 | 1.1110 | 0.81 | 0.7790 | 0.3022 | 0.0601 | | 0.6662 | 54.0 | 2700 | 0.7320 | 0.8 | 0.3363 | 1.0621 | 0.8000 | 0.7647 | 0.2815 | 0.0607 | | 0.6662 | 55.0 | 2750 | 0.7324 | 0.805 | 0.3321 | 0.9926 | 0.805 | 0.7693 | 0.2972 | 0.0600 | | 0.6662 | 56.0 | 2800 | 0.7318 | 0.805 | 0.3364 | 1.0537 | 0.805 | 0.7681 | 0.2554 | 0.0612 | | 0.6662 | 57.0 | 2850 | 0.7311 | 0.82 | 0.3355 | 1.1133 | 0.82 | 0.7862 | 0.2776 | 0.0594 | | 0.6662 | 58.0 | 2900 | 0.7317 | 0.81 | 0.3331 | 1.0662 | 0.81 | 0.7797 | 0.2600 | 0.0579 | | 0.6662 | 59.0 | 2950 | 0.7327 | 0.805 | 0.3382 | 1.1876 | 0.805 | 0.7735 | 0.2797 | 0.0621 | | 0.6577 | 60.0 | 3000 | 0.7322 | 0.8 | 0.3356 | 1.1864 | 0.8000 | 0.7680 | 0.2797 | 0.0612 | | 0.6577 | 61.0 | 3050 | 0.7327 | 0.795 | 0.3391 | 1.1347 | 0.795 | 0.7614 | 0.2883 | 0.0641 | | 0.6577 | 62.0 | 3100 | 0.7315 | 0.815 | 0.3364 | 1.1227 | 0.815 | 0.7848 | 0.2681 | 0.0599 | | 0.6577 | 63.0 | 3150 | 0.7316 | 0.805 | 0.3392 | 1.0608 | 0.805 | 0.7717 | 0.2742 | 0.0632 | | 0.6577 | 64.0 | 3200 | 0.7313 | 0.82 | 0.3341 | 1.0601 | 0.82 | 0.7878 | 0.2950 | 0.0583 | | 0.6577 | 65.0 | 3250 | 0.7322 | 0.805 | 0.3388 | 1.1837 | 0.805 | 0.7747 | 0.2806 | 0.0638 | | 0.6577 | 66.0 | 3300 | 0.7311 | 0.805 | 0.3373 | 1.0157 | 0.805 | 0.7757 | 0.2880 | 0.0629 | | 0.6577 | 67.0 | 3350 | 0.7310 | 0.805 | 0.3344 | 1.1878 | 0.805 | 0.7766 | 0.2499 | 0.0609 | | 0.6577 | 68.0 | 3400 | 0.7326 | 0.805 | 0.3391 | 1.0847 | 0.805 | 0.7729 | 0.2824 | 0.0636 | | 0.6577 | 69.0 | 3450 | 0.7302 | 0.805 | 0.3376 | 1.1932 | 0.805 | 0.7778 | 0.2789 | 0.0617 | | 0.6528 | 70.0 | 3500 | 0.7305 | 0.81 | 0.3359 | 0.9988 | 0.81 | 0.7787 | 0.2769 | 0.0622 | | 0.6528 | 71.0 | 3550 | 0.7300 | 0.81 | 0.3328 | 1.0833 | 0.81 | 0.7776 | 0.2914 | 0.0594 | | 0.6528 | 72.0 | 3600 | 0.7300 | 0.81 | 0.3343 | 1.1426 | 0.81 | 0.7776 | 0.2843 | 0.0594 | | 0.6528 | 73.0 | 3650 | 0.7285 | 0.805 | 0.3341 | 1.1237 | 0.805 | 0.7701 | 0.2723 | 0.0614 | | 0.6528 | 74.0 | 3700 | 0.7303 | 0.81 | 0.3368 | 1.1928 | 0.81 | 0.7768 | 0.2926 | 0.0612 | | 0.6528 | 75.0 | 3750 | 0.7290 | 0.805 | 0.3318 | 1.0669 | 0.805 | 0.7709 | 0.2810 | 0.0603 | | 0.6528 | 76.0 | 3800 | 0.7316 | 0.8 | 0.3382 | 1.1392 | 0.8000 | 0.7687 | 0.2505 | 0.0636 | | 0.6528 | 77.0 | 3850 | 0.7284 | 0.8 | 0.3337 | 1.1338 | 0.8000 | 0.7720 | 0.2677 | 0.0610 | | 0.6528 | 78.0 | 3900 | 0.7303 | 0.805 | 0.3373 | 1.1969 | 0.805 | 0.7729 | 0.2745 | 0.0618 | | 0.6528 | 79.0 | 3950 | 0.7297 | 0.805 | 0.3369 | 1.1970 | 0.805 | 0.7743 | 0.2731 | 0.0606 | | 0.6489 | 80.0 | 4000 | 0.7296 | 0.795 | 0.3362 | 1.1328 | 0.795 | 0.7656 | 0.2620 | 0.0627 | | 0.6489 | 81.0 | 4050 | 0.7295 | 0.805 | 0.3363 | 1.1358 | 0.805 | 0.7726 | 0.2540 | 0.0608 | | 0.6489 | 82.0 | 4100 | 0.7290 | 0.795 | 0.3341 | 1.1389 | 0.795 | 0.7668 | 0.2661 | 0.0630 | | 0.6489 | 83.0 | 4150 | 0.7289 | 0.8 | 0.3364 | 1.0597 | 0.8000 | 0.7678 | 0.2838 | 0.0615 | | 0.6489 | 84.0 | 4200 | 0.7291 | 0.805 | 0.3351 | 1.1277 | 0.805 | 0.7743 | 0.2621 | 0.0608 | | 0.6489 | 85.0 | 4250 | 0.7297 | 0.795 | 0.3353 | 1.1953 | 0.795 | 0.7668 | 0.2666 | 0.0622 | | 0.6489 | 86.0 | 4300 | 0.7286 | 0.805 | 0.3339 | 1.1278 | 0.805 | 0.7735 | 0.2668 | 0.0608 | | 0.6489 | 87.0 | 4350 | 0.7298 | 0.8 | 0.3361 | 1.1423 | 0.8000 | 0.7677 | 0.2613 | 0.0614 | | 0.6489 | 88.0 | 4400 | 0.7296 | 0.805 | 0.3346 | 1.1927 | 0.805 | 0.7743 | 0.2789 | 0.0612 | | 0.6489 | 89.0 | 4450 | 0.7299 | 0.8 | 0.3359 | 1.1950 | 0.8000 | 0.7686 | 0.2500 | 0.0613 | | 0.6462 | 90.0 | 4500 | 0.7297 | 0.805 | 0.3354 | 1.1934 | 0.805 | 0.7743 | 0.2939 | 0.0613 | | 0.6462 | 91.0 | 4550 | 0.7294 | 0.8 | 0.3353 | 1.1313 | 0.8000 | 0.7685 | 0.2808 | 0.0610 | | 0.6462 | 92.0 | 4600 | 0.7297 | 0.805 | 0.3356 | 1.1349 | 0.805 | 0.7765 | 0.2668 | 0.0614 | | 0.6462 | 93.0 | 4650 | 0.7298 | 0.8 | 0.3354 | 1.1954 | 0.8000 | 0.7685 | 0.2700 | 0.0613 | | 0.6462 | 94.0 | 4700 | 0.7301 | 0.8 | 0.3362 | 1.1951 | 0.8000 | 0.7677 | 0.2722 | 0.0616 | | 0.6462 | 95.0 | 4750 | 0.7299 | 0.805 | 0.3360 | 1.1957 | 0.805 | 0.7743 | 0.2619 | 0.0614 | | 0.6462 | 96.0 | 4800 | 0.7299 | 0.805 | 0.3357 | 1.1946 | 0.805 | 0.7743 | 0.2892 | 0.0611 | | 0.6462 | 97.0 | 4850 | 0.7297 | 0.8 | 0.3355 | 1.1954 | 0.8000 | 0.7686 | 0.2703 | 0.0613 | | 0.6462 | 98.0 | 4900 | 0.7298 | 0.8 | 0.3359 | 1.1952 | 0.8000 | 0.7677 | 0.2892 | 0.0615 | | 0.6462 | 99.0 | 4950 | 0.7298 | 0.8 | 0.3357 | 1.1951 | 0.8000 | 0.7677 | 0.2720 | 0.0614 | | 0.645 | 100.0 | 5000 | 0.7298 | 0.8 | 0.3356 | 1.1950 | 0.8000 | 0.7677 | 0.2868 | 0.0614 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-tiny_tobacco3482_og_simkd

# dit-base_tobacco-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 318.4368 - Accuracy: 0.805 - Brier Loss: 0.3825 - Nll: 1.1523 - F1 Micro: 0.805 - F1 Macro: 0.7673 - Ece: 0.2987 - Aurc: 0.0702 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 328.9614 | 0.155 | 0.8984 | 7.4608 | 0.155 | 0.0353 | 0.2035 | 0.8760 | | No log | 2.0 | 14 | 328.8199 | 0.235 | 0.8940 | 6.4907 | 0.235 | 0.1148 | 0.2643 | 0.7444 | | No log | 3.0 | 21 | 328.4224 | 0.38 | 0.8711 | 2.8184 | 0.38 | 0.3279 | 0.3440 | 0.4817 | | No log | 4.0 | 28 | 327.5357 | 0.51 | 0.8072 | 2.0744 | 0.51 | 0.4221 | 0.4111 | 0.3319 | | No log | 5.0 | 35 | 326.2037 | 0.53 | 0.6860 | 2.0669 | 0.53 | 0.4313 | 0.3619 | 0.2744 | | No log | 6.0 | 42 | 324.8763 | 0.565 | 0.6008 | 1.9437 | 0.565 | 0.4477 | 0.3009 | 0.2469 | | No log | 7.0 | 49 | 323.9205 | 0.6 | 0.5390 | 1.7694 | 0.6 | 0.4647 | 0.2365 | 0.1978 | | No log | 8.0 | 56 | 323.2227 | 0.65 | 0.4632 | 1.7803 | 0.65 | 0.5195 | 0.2313 | 0.1422 | | No log | 9.0 | 63 | 322.5265 | 0.74 | 0.4177 | 1.7538 | 0.74 | 0.6302 | 0.2442 | 0.1113 | | No log | 10.0 | 70 | 322.1928 | 0.705 | 0.4013 | 1.5880 | 0.705 | 0.5864 | 0.2147 | 0.1118 | | No log | 11.0 | 77 | 322.2687 | 0.795 | 0.4006 | 1.2854 | 0.795 | 0.7476 | 0.2719 | 0.0942 | | No log | 12.0 | 84 | 321.6652 | 0.725 | 0.3754 | 1.3462 | 0.7250 | 0.6521 | 0.2238 | 0.0920 | | No log | 13.0 | 91 | 322.3688 | 0.785 | 0.3951 | 1.3209 | 0.785 | 0.7260 | 0.2712 | 0.0805 | | No log | 14.0 | 98 | 321.7083 | 0.72 | 0.3915 | 1.4854 | 0.72 | 0.6220 | 0.1963 | 0.0986 | | No log | 15.0 | 105 | 321.6171 | 0.8 | 0.3614 | 1.3397 | 0.8000 | 0.7427 | 0.2531 | 0.0741 | | No log | 16.0 | 112 | 321.0427 | 0.77 | 0.3502 | 1.1461 | 0.7700 | 0.7082 | 0.1976 | 0.0769 | | No log | 17.0 | 119 | 321.1529 | 0.735 | 0.3827 | 1.5751 | 0.735 | 0.6769 | 0.1926 | 0.0973 | | No log | 18.0 | 126 | 321.0808 | 0.78 | 0.3611 | 1.2529 | 0.78 | 0.7199 | 0.2242 | 0.0762 | | No log | 19.0 | 133 | 321.6684 | 0.795 | 0.3835 | 1.1789 | 0.795 | 0.7506 | 0.2823 | 0.0712 | | No log | 20.0 | 140 | 321.2322 | 0.78 | 0.3682 | 1.1715 | 0.78 | 0.7356 | 0.2532 | 0.0752 | | No log | 21.0 | 147 | 320.4927 | 0.795 | 0.3458 | 1.3764 | 0.795 | 0.7504 | 0.2178 | 0.0710 | | No log | 22.0 | 154 | 320.8896 | 0.8 | 0.3568 | 1.0908 | 0.8000 | 0.7536 | 0.2709 | 0.0677 | | No log | 23.0 | 161 | 320.9060 | 0.785 | 0.3774 | 1.1571 | 0.785 | 0.7414 | 0.2712 | 0.0719 | | No log | 24.0 | 168 | 320.9026 | 0.795 | 0.3718 | 1.0871 | 0.795 | 0.7465 | 0.2718 | 0.0690 | | No log | 25.0 | 175 | 320.7932 | 0.805 | 0.3601 | 1.0998 | 0.805 | 0.7699 | 0.2620 | 0.0614 | | No log | 26.0 | 182 | 321.2285 | 0.735 | 0.4164 | 1.8530 | 0.735 | 0.7051 | 0.2814 | 0.0889 | | No log | 27.0 | 189 | 320.8364 | 0.775 | 0.4028 | 1.4063 | 0.775 | 0.7412 | 0.2687 | 0.0836 | | No log | 28.0 | 196 | 320.0800 | 0.785 | 0.3548 | 1.2123 | 0.785 | 0.7394 | 0.2055 | 0.0740 | | No log | 29.0 | 203 | 319.9995 | 0.79 | 0.3526 | 1.2296 | 0.79 | 0.7381 | 0.2363 | 0.0691 | | No log | 30.0 | 210 | 320.0685 | 0.795 | 0.3588 | 1.2765 | 0.795 | 0.7447 | 0.2310 | 0.0725 | | No log | 31.0 | 217 | 320.0981 | 0.805 | 0.3699 | 1.0128 | 0.805 | 0.7690 | 0.2868 | 0.0701 | | No log | 32.0 | 224 | 320.5063 | 0.8 | 0.3900 | 1.1437 | 0.8000 | 0.7650 | 0.3141 | 0.0679 | | No log | 33.0 | 231 | 319.8609 | 0.795 | 0.3549 | 1.2051 | 0.795 | 0.7526 | 0.2485 | 0.0697 | | No log | 34.0 | 238 | 319.6974 | 0.81 | 0.3600 | 1.0124 | 0.81 | 0.7724 | 0.2671 | 0.0672 | | No log | 35.0 | 245 | 319.5988 | 0.795 | 0.3513 | 1.1480 | 0.795 | 0.7540 | 0.2425 | 0.0679 | | No log | 36.0 | 252 | 319.6317 | 0.8 | 0.3544 | 1.2190 | 0.8000 | 0.7607 | 0.2449 | 0.0674 | | No log | 37.0 | 259 | 319.6821 | 0.81 | 0.3531 | 1.0714 | 0.81 | 0.7672 | 0.2590 | 0.0662 | | No log | 38.0 | 266 | 319.7618 | 0.805 | 0.3754 | 1.0421 | 0.805 | 0.7625 | 0.2973 | 0.0701 | | No log | 39.0 | 273 | 319.9920 | 0.775 | 0.3843 | 1.0821 | 0.775 | 0.7374 | 0.2801 | 0.0723 | | No log | 40.0 | 280 | 319.3407 | 0.765 | 0.3633 | 1.2213 | 0.765 | 0.7041 | 0.2274 | 0.0767 | | No log | 41.0 | 287 | 319.2732 | 0.765 | 0.3696 | 1.2638 | 0.765 | 0.7184 | 0.2315 | 0.0835 | | No log | 42.0 | 294 | 319.5948 | 0.805 | 0.3685 | 1.0782 | 0.805 | 0.7625 | 0.2678 | 0.0661 | | No log | 43.0 | 301 | 319.7181 | 0.8 | 0.3776 | 1.0004 | 0.8000 | 0.7507 | 0.2598 | 0.0672 | | No log | 44.0 | 308 | 319.1170 | 0.77 | 0.3619 | 1.2129 | 0.7700 | 0.7159 | 0.2557 | 0.0787 | | No log | 45.0 | 315 | 319.5949 | 0.8 | 0.3809 | 1.1448 | 0.8000 | 0.7670 | 0.2868 | 0.0688 | | No log | 46.0 | 322 | 319.0327 | 0.79 | 0.3675 | 1.2386 | 0.79 | 0.7315 | 0.2546 | 0.0790 | | No log | 47.0 | 329 | 319.3806 | 0.805 | 0.3665 | 1.1368 | 0.805 | 0.7620 | 0.2737 | 0.0700 | | No log | 48.0 | 336 | 319.4999 | 0.795 | 0.3836 | 1.0256 | 0.795 | 0.7550 | 0.2800 | 0.0748 | | No log | 49.0 | 343 | 319.2553 | 0.8 | 0.3660 | 1.2011 | 0.8000 | 0.7573 | 0.2698 | 0.0679 | | No log | 50.0 | 350 | 319.3495 | 0.805 | 0.3836 | 1.1055 | 0.805 | 0.7634 | 0.3004 | 0.0671 | | No log | 51.0 | 357 | 319.1643 | 0.8 | 0.3660 | 1.1980 | 0.8000 | 0.7497 | 0.2641 | 0.0709 | | No log | 52.0 | 364 | 319.1483 | 0.795 | 0.3651 | 1.0776 | 0.795 | 0.7561 | 0.2856 | 0.0683 | | No log | 53.0 | 371 | 319.0104 | 0.79 | 0.3724 | 1.1653 | 0.79 | 0.7422 | 0.2512 | 0.0724 | | No log | 54.0 | 378 | 319.1622 | 0.795 | 0.3814 | 1.2807 | 0.795 | 0.7456 | 0.2644 | 0.0759 | | No log | 55.0 | 385 | 319.1554 | 0.8 | 0.3694 | 1.2710 | 0.8000 | 0.7570 | 0.2877 | 0.0667 | | No log | 56.0 | 392 | 319.2158 | 0.79 | 0.3795 | 1.1678 | 0.79 | 0.7509 | 0.2942 | 0.0692 | | No log | 57.0 | 399 | 319.1813 | 0.795 | 0.3839 | 1.1243 | 0.795 | 0.7529 | 0.2835 | 0.0733 | | No log | 58.0 | 406 | 318.7599 | 0.81 | 0.3632 | 1.1484 | 0.81 | 0.7738 | 0.3030 | 0.0691 | | No log | 59.0 | 413 | 319.0827 | 0.805 | 0.3792 | 1.2070 | 0.805 | 0.7685 | 0.2901 | 0.0674 | | No log | 60.0 | 420 | 318.6928 | 0.805 | 0.3661 | 1.1517 | 0.805 | 0.7534 | 0.2492 | 0.0719 | | No log | 61.0 | 427 | 318.8309 | 0.805 | 0.3714 | 1.2785 | 0.805 | 0.7517 | 0.2674 | 0.0699 | | No log | 62.0 | 434 | 318.9468 | 0.8 | 0.3794 | 1.1549 | 0.8000 | 0.7566 | 0.2862 | 0.0707 | | No log | 63.0 | 441 | 318.8059 | 0.785 | 0.3774 | 1.2460 | 0.785 | 0.7487 | 0.2721 | 0.0752 | | No log | 64.0 | 448 | 318.7155 | 0.81 | 0.3659 | 1.1963 | 0.81 | 0.7660 | 0.2676 | 0.0680 | | No log | 65.0 | 455 | 318.8439 | 0.795 | 0.3799 | 1.0230 | 0.795 | 0.7464 | 0.2797 | 0.0700 | | No log | 66.0 | 462 | 318.7784 | 0.79 | 0.3783 | 1.3168 | 0.79 | 0.7503 | 0.2618 | 0.0804 | | No log | 67.0 | 469 | 318.9019 | 0.795 | 0.3802 | 1.2003 | 0.795 | 0.7503 | 0.2934 | 0.0702 | | No log | 68.0 | 476 | 318.6647 | 0.8 | 0.3728 | 1.1395 | 0.8000 | 0.7590 | 0.2718 | 0.0699 | | No log | 69.0 | 483 | 318.3780 | 0.8 | 0.3688 | 1.2812 | 0.8000 | 0.7602 | 0.2690 | 0.0728 | | No log | 70.0 | 490 | 318.8004 | 0.8 | 0.3779 | 1.0682 | 0.8000 | 0.7607 | 0.2887 | 0.0682 | | No log | 71.0 | 497 | 318.7021 | 0.8 | 0.3748 | 1.1101 | 0.8000 | 0.7545 | 0.2977 | 0.0691 | | 322.4844 | 72.0 | 504 | 318.3595 | 0.79 | 0.3779 | 1.2333 | 0.79 | 0.7386 | 0.2617 | 0.0843 | | 322.4844 | 73.0 | 511 | 318.5725 | 0.805 | 0.3740 | 1.2108 | 0.805 | 0.7674 | 0.2762 | 0.0677 | | 322.4844 | 74.0 | 518 | 318.7131 | 0.81 | 0.3822 | 1.2048 | 0.81 | 0.7660 | 0.2971 | 0.0696 | | 322.4844 | 75.0 | 525 | 318.6258 | 0.775 | 0.3806 | 1.1511 | 0.775 | 0.7228 | 0.2824 | 0.0743 | | 322.4844 | 76.0 | 532 | 318.5414 | 0.8 | 0.3746 | 1.2136 | 0.8000 | 0.7563 | 0.2872 | 0.0708 | | 322.4844 | 77.0 | 539 | 318.5404 | 0.795 | 0.3765 | 1.1414 | 0.795 | 0.7551 | 0.2905 | 0.0707 | | 322.4844 | 78.0 | 546 | 318.5820 | 0.8 | 0.3806 | 1.1653 | 0.8000 | 0.7573 | 0.2888 | 0.0707 | | 322.4844 | 79.0 | 553 | 318.5909 | 0.8 | 0.3838 | 1.2343 | 0.8000 | 0.7563 | 0.2778 | 0.0754 | | 322.4844 | 80.0 | 560 | 318.6398 | 0.795 | 0.3874 | 1.1097 | 0.795 | 0.7520 | 0.3045 | 0.0727 | | 322.4844 | 81.0 | 567 | 318.6250 | 0.795 | 0.3860 | 1.1612 | 0.795 | 0.7542 | 0.3079 | 0.0727 | | 322.4844 | 82.0 | 574 | 318.5269 | 0.795 | 0.3825 | 1.2812 | 0.795 | 0.7451 | 0.2723 | 0.0737 | | 322.4844 | 83.0 | 581 | 318.5790 | 0.795 | 0.3846 | 1.1575 | 0.795 | 0.7455 | 0.2984 | 0.0723 | | 322.4844 | 84.0 | 588 | 318.4343 | 0.795 | 0.3826 | 1.2088 | 0.795 | 0.7532 | 0.2852 | 0.0746 | | 322.4844 | 85.0 | 595 | 318.3853 | 0.795 | 0.3792 | 1.2784 | 0.795 | 0.7456 | 0.3003 | 0.0729 | | 322.4844 | 86.0 | 602 | 318.5143 | 0.805 | 0.3854 | 1.1745 | 0.805 | 0.7636 | 0.3071 | 0.0705 | | 322.4844 | 87.0 | 609 | 318.3533 | 0.805 | 0.3763 | 1.1579 | 0.805 | 0.7679 | 0.2805 | 0.0694 | | 322.4844 | 88.0 | 616 | 318.4745 | 0.795 | 0.3860 | 1.0964 | 0.795 | 0.7539 | 0.2952 | 0.0712 | | 322.4844 | 89.0 | 623 | 318.4909 | 0.805 | 0.3829 | 1.1544 | 0.805 | 0.7673 | 0.3035 | 0.0700 | | 322.4844 | 90.0 | 630 | 318.4910 | 0.8 | 0.3828 | 1.1537 | 0.8000 | 0.7497 | 0.2730 | 0.0717 | | 322.4844 | 91.0 | 637 | 318.5176 | 0.8 | 0.3855 | 1.1613 | 0.8000 | 0.7552 | 0.2815 | 0.0718 | | 322.4844 | 92.0 | 644 | 318.4100 | 0.795 | 0.3810 | 1.2215 | 0.795 | 0.7532 | 0.2696 | 0.0731 | | 322.4844 | 93.0 | 651 | 318.3500 | 0.805 | 0.3765 | 1.2181 | 0.805 | 0.7702 | 0.2790 | 0.0705 | | 322.4844 | 94.0 | 658 | 318.3257 | 0.805 | 0.3785 | 1.2218 | 0.805 | 0.7678 | 0.3114 | 0.0704 | | 322.4844 | 95.0 | 665 | 318.3990 | 0.8 | 0.3823 | 1.1485 | 0.8000 | 0.7585 | 0.2901 | 0.0710 | | 322.4844 | 96.0 | 672 | 318.5006 | 0.81 | 0.3862 | 1.1518 | 0.81 | 0.7724 | 0.2925 | 0.0698 | | 322.4844 | 97.0 | 679 | 318.3142 | 0.8 | 0.3780 | 1.1608 | 0.8000 | 0.7557 | 0.2916 | 0.0716 | | 322.4844 | 98.0 | 686 | 318.3767 | 0.795 | 0.3819 | 1.2208 | 0.795 | 0.7526 | 0.2764 | 0.0731 | | 322.4844 | 99.0 | 693 | 318.4233 | 0.8 | 0.3810 | 1.1532 | 0.8000 | 0.7557 | 0.2786 | 0.0706 | | 322.4844 | 100.0 | 700 | 318.4368 | 0.805 | 0.3825 | 1.1523 | 0.805 | 0.7673 | 0.2987 | 0.0702 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_small_sgd_00001_fold4

# hushem_5x_deit_small_sgd_00001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4657 - Accuracy: 0.2857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5422 | 1.0 | 28 | 1.4834 | 0.2619 | | 1.6237 | 2.0 | 56 | 1.4826 | 0.2619 | | 1.5562 | 3.0 | 84 | 1.4817 | 0.2619 | | 1.5833 | 4.0 | 112 | 1.4810 | 0.2619 | | 1.5467 | 5.0 | 140 | 1.4803 | 0.2619 | | 1.5372 | 6.0 | 168 | 1.4795 | 0.2857 | | 1.5683 | 7.0 | 196 | 1.4788 | 0.2857 | | 1.5057 | 8.0 | 224 | 1.4781 | 0.2857 | | 1.5994 | 9.0 | 252 | 1.4774 | 0.2857 | | 1.5076 | 10.0 | 280 | 1.4768 | 0.2857 | | 1.5466 | 11.0 | 308 | 1.4762 | 0.2857 | | 1.544 | 12.0 | 336 | 1.4756 | 0.2857 | | 1.5866 | 13.0 | 364 | 1.4750 | 0.2857 | | 1.5384 | 14.0 | 392 | 1.4744 | 0.2857 | | 1.6111 | 15.0 | 420 | 1.4739 | 0.2857 | | 1.5625 | 16.0 | 448 | 1.4733 | 0.2857 | | 1.547 | 17.0 | 476 | 1.4728 | 0.2857 | | 1.5362 | 18.0 | 504 | 1.4723 | 0.2857 | | 1.5318 | 19.0 | 532 | 1.4718 | 0.2857 | | 1.5453 | 20.0 | 560 | 1.4714 | 0.2857 | | 1.5434 | 21.0 | 588 | 1.4709 | 0.2857 | | 1.548 | 22.0 | 616 | 1.4705 | 0.2857 | | 1.5105 | 23.0 | 644 | 1.4701 | 0.2857 | | 1.5176 | 24.0 | 672 | 1.4697 | 0.2857 | | 1.5194 | 25.0 | 700 | 1.4694 | 0.2857 | | 1.5543 | 26.0 | 728 | 1.4690 | 0.2857 | | 1.5727 | 27.0 | 756 | 1.4687 | 0.2857 | | 1.5476 | 28.0 | 784 | 1.4684 | 0.2857 | | 1.5163 | 29.0 | 812 | 1.4681 | 0.2857 | | 1.4767 | 30.0 | 840 | 1.4678 | 0.2857 | | 1.5623 | 31.0 | 868 | 1.4676 | 0.2857 | | 1.4924 | 32.0 | 896 | 1.4674 | 0.2857 | | 1.5673 | 33.0 | 924 | 1.4672 | 0.2857 | | 1.4842 | 34.0 | 952 | 1.4670 | 0.2857 | | 1.4908 | 35.0 | 980 | 1.4668 | 0.2857 | | 1.5184 | 36.0 | 1008 | 1.4666 | 0.2857 | | 1.5315 | 37.0 | 1036 | 1.4664 | 0.2857 | | 1.4892 | 38.0 | 1064 | 1.4663 | 0.2857 | | 1.5241 | 39.0 | 1092 | 1.4662 | 0.2857 | | 1.5587 | 40.0 | 1120 | 1.4661 | 0.2857 | | 1.5867 | 41.0 | 1148 | 1.4660 | 0.2857 | | 1.5357 | 42.0 | 1176 | 1.4659 | 0.2857 | | 1.479 | 43.0 | 1204 | 1.4659 | 0.2857 | | 1.4798 | 44.0 | 1232 | 1.4658 | 0.2857 | | 1.5998 | 45.0 | 1260 | 1.4658 | 0.2857 | | 1.5487 | 46.0 | 1288 | 1.4658 | 0.2857 | | 1.5234 | 47.0 | 1316 | 1.4657 | 0.2857 | | 1.5142 | 48.0 | 1344 | 1.4657 | 0.2857 | | 1.5259 | 49.0 | 1372 | 1.4657 | 0.2857 | | 1.5344 | 50.0 | 1400 | 1.4657 | 0.2857 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_00001_fold5

# hushem_5x_deit_small_sgd_00001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4968 - Accuracy: 0.2439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5975 | 1.0 | 28 | 1.5197 | 0.2195 | | 1.5191 | 2.0 | 56 | 1.5186 | 0.2195 | | 1.5652 | 3.0 | 84 | 1.5176 | 0.2195 | | 1.5368 | 4.0 | 112 | 1.5166 | 0.2195 | | 1.5533 | 5.0 | 140 | 1.5156 | 0.2195 | | 1.5934 | 6.0 | 168 | 1.5147 | 0.2195 | | 1.5997 | 7.0 | 196 | 1.5138 | 0.2195 | | 1.543 | 8.0 | 224 | 1.5129 | 0.2195 | | 1.5785 | 9.0 | 252 | 1.5120 | 0.2195 | | 1.5476 | 10.0 | 280 | 1.5112 | 0.2195 | | 1.5374 | 11.0 | 308 | 1.5104 | 0.2195 | | 1.5776 | 12.0 | 336 | 1.5096 | 0.2195 | | 1.552 | 13.0 | 364 | 1.5088 | 0.2195 | | 1.5084 | 14.0 | 392 | 1.5081 | 0.2195 | | 1.5475 | 15.0 | 420 | 1.5073 | 0.2195 | | 1.5527 | 16.0 | 448 | 1.5067 | 0.2195 | | 1.5461 | 17.0 | 476 | 1.5060 | 0.2195 | | 1.553 | 18.0 | 504 | 1.5054 | 0.2195 | | 1.5466 | 19.0 | 532 | 1.5047 | 0.2195 | | 1.5068 | 20.0 | 560 | 1.5041 | 0.2195 | | 1.5792 | 21.0 | 588 | 1.5036 | 0.2195 | | 1.5408 | 22.0 | 616 | 1.5030 | 0.2195 | | 1.4869 | 23.0 | 644 | 1.5025 | 0.2195 | | 1.5203 | 24.0 | 672 | 1.5020 | 0.2439 | | 1.5205 | 25.0 | 700 | 1.5016 | 0.2439 | | 1.5334 | 26.0 | 728 | 1.5011 | 0.2439 | | 1.5195 | 27.0 | 756 | 1.5007 | 0.2439 | | 1.555 | 28.0 | 784 | 1.5003 | 0.2439 | | 1.5231 | 29.0 | 812 | 1.4999 | 0.2439 | | 1.5521 | 30.0 | 840 | 1.4996 | 0.2439 | | 1.5405 | 31.0 | 868 | 1.4992 | 0.2439 | | 1.5223 | 32.0 | 896 | 1.4989 | 0.2439 | | 1.533 | 33.0 | 924 | 1.4986 | 0.2439 | | 1.5569 | 34.0 | 952 | 1.4984 | 0.2439 | | 1.5415 | 35.0 | 980 | 1.4981 | 0.2439 | | 1.5242 | 36.0 | 1008 | 1.4979 | 0.2439 | | 1.5342 | 37.0 | 1036 | 1.4977 | 0.2439 | | 1.51 | 38.0 | 1064 | 1.4975 | 0.2439 | | 1.4915 | 39.0 | 1092 | 1.4974 | 0.2439 | | 1.533 | 40.0 | 1120 | 1.4972 | 0.2439 | | 1.559 | 41.0 | 1148 | 1.4971 | 0.2439 | | 1.5496 | 42.0 | 1176 | 1.4970 | 0.2439 | | 1.5368 | 43.0 | 1204 | 1.4969 | 0.2439 | | 1.5602 | 44.0 | 1232 | 1.4969 | 0.2439 | | 1.5291 | 45.0 | 1260 | 1.4968 | 0.2439 | | 1.5316 | 46.0 | 1288 | 1.4968 | 0.2439 | | 1.5518 | 47.0 | 1316 | 1.4968 | 0.2439 | | 1.5141 | 48.0 | 1344 | 1.4968 | 0.2439 | | 1.515 | 49.0 | 1372 | 1.4968 | 0.2439 | | 1.544 | 50.0 | 1400 | 1.4968 | 0.2439 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-small_tobacco3482_kd

# dit-base_tobacco-small_tobacco3482_kd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5105 - Accuracy: 0.815 - Brier Loss: 0.2790 - Nll: 1.4944 - F1 Micro: 0.815 - F1 Macro: 0.7942 - Ece: 0.1287 - Aurc: 0.0524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.2378 | 0.17 | 0.8975 | 4.4036 | 0.17 | 0.1418 | 0.2519 | 0.8078 | | No log | 2.0 | 14 | 1.7484 | 0.38 | 0.7667 | 4.1809 | 0.38 | 0.2513 | 0.3132 | 0.4423 | | No log | 3.0 | 21 | 1.1417 | 0.55 | 0.5683 | 1.8669 | 0.55 | 0.4592 | 0.2551 | 0.2287 | | No log | 4.0 | 28 | 0.8020 | 0.685 | 0.4327 | 1.7476 | 0.685 | 0.6393 | 0.2274 | 0.1292 | | No log | 5.0 | 35 | 0.8347 | 0.645 | 0.4502 | 1.6809 | 0.645 | 0.6306 | 0.1939 | 0.1346 | | No log | 6.0 | 42 | 0.6546 | 0.735 | 0.3657 | 1.5210 | 0.735 | 0.7191 | 0.1995 | 0.0901 | | No log | 7.0 | 49 | 0.6447 | 0.76 | 0.3375 | 1.5117 | 0.76 | 0.7450 | 0.1781 | 0.0875 | | No log | 8.0 | 56 | 0.7089 | 0.775 | 0.3650 | 1.4823 | 0.775 | 0.7554 | 0.2026 | 0.0971 | | No log | 9.0 | 63 | 0.5721 | 0.785 | 0.3083 | 1.4053 | 0.785 | 0.7633 | 0.1647 | 0.0651 | | No log | 10.0 | 70 | 0.5953 | 0.795 | 0.3130 | 1.4301 | 0.795 | 0.7971 | 0.1661 | 0.0701 | | No log | 11.0 | 77 | 0.6352 | 0.79 | 0.3131 | 1.5018 | 0.79 | 0.7607 | 0.1503 | 0.0789 | | No log | 12.0 | 84 | 0.7999 | 0.735 | 0.3916 | 1.7141 | 0.735 | 0.7065 | 0.2143 | 0.1178 | | No log | 13.0 | 91 | 0.6602 | 0.8 | 0.3099 | 1.8022 | 0.8000 | 0.7746 | 0.1709 | 0.0805 | | No log | 14.0 | 98 | 0.6529 | 0.785 | 0.3298 | 1.3607 | 0.785 | 0.7658 | 0.1771 | 0.0858 | | No log | 15.0 | 105 | 0.6170 | 0.8 | 0.3098 | 1.3676 | 0.8000 | 0.7838 | 0.1630 | 0.0723 | | No log | 16.0 | 112 | 0.6484 | 0.775 | 0.3342 | 1.2826 | 0.775 | 0.7752 | 0.1837 | 0.0827 | | No log | 17.0 | 119 | 0.5817 | 0.78 | 0.3019 | 1.6577 | 0.78 | 0.7730 | 0.1566 | 0.0582 | | No log | 18.0 | 126 | 0.6528 | 0.78 | 0.3376 | 1.5044 | 0.78 | 0.7788 | 0.1687 | 0.0768 | | No log | 19.0 | 133 | 0.6241 | 0.805 | 0.3038 | 1.3465 | 0.805 | 0.7796 | 0.1498 | 0.0759 | | No log | 20.0 | 140 | 0.5610 | 0.79 | 0.2948 | 1.4395 | 0.79 | 0.7716 | 0.1515 | 0.0708 | | No log | 21.0 | 147 | 0.6829 | 0.78 | 0.3241 | 1.3252 | 0.78 | 0.7687 | 0.1782 | 0.0852 | | No log | 22.0 | 154 | 0.5443 | 0.795 | 0.3117 | 1.4374 | 0.795 | 0.7822 | 0.1730 | 0.0679 | | No log | 23.0 | 161 | 0.6968 | 0.78 | 0.3474 | 1.7830 | 0.78 | 0.7880 | 0.1745 | 0.0813 | | No log | 24.0 | 168 | 0.7422 | 0.75 | 0.3639 | 1.5379 | 0.75 | 0.7238 | 0.1982 | 0.0940 | | No log | 25.0 | 175 | 0.5756 | 0.785 | 0.3150 | 1.4739 | 0.785 | 0.7723 | 0.1615 | 0.0675 | | No log | 26.0 | 182 | 0.6127 | 0.805 | 0.3036 | 1.5553 | 0.805 | 0.7990 | 0.1416 | 0.0659 | | No log | 27.0 | 189 | 0.5852 | 0.795 | 0.3104 | 1.5149 | 0.795 | 0.7808 | 0.1583 | 0.0625 | | No log | 28.0 | 196 | 0.5421 | 0.83 | 0.2808 | 1.4320 | 0.83 | 0.8147 | 0.1475 | 0.0558 | | No log | 29.0 | 203 | 0.5588 | 0.79 | 0.2888 | 1.5801 | 0.79 | 0.7723 | 0.1465 | 0.0580 | | No log | 30.0 | 210 | 0.5532 | 0.795 | 0.2892 | 1.5724 | 0.795 | 0.7790 | 0.1453 | 0.0576 | | No log | 31.0 | 217 | 0.5050 | 0.835 | 0.2685 | 1.4206 | 0.835 | 0.8221 | 0.1459 | 0.0549 | | No log | 32.0 | 224 | 0.5067 | 0.82 | 0.2762 | 1.4460 | 0.82 | 0.8017 | 0.1494 | 0.0538 | | No log | 33.0 | 231 | 0.5200 | 0.815 | 0.2798 | 1.5300 | 0.815 | 0.7973 | 0.1442 | 0.0541 | | No log | 34.0 | 238 | 0.5110 | 0.825 | 0.2802 | 1.6009 | 0.825 | 0.8095 | 0.1462 | 0.0537 | | No log | 35.0 | 245 | 0.5125 | 0.815 | 0.2804 | 1.5209 | 0.815 | 0.8013 | 0.1555 | 0.0540 | | No log | 36.0 | 252 | 0.4981 | 0.82 | 0.2728 | 1.4498 | 0.82 | 0.8032 | 0.1557 | 0.0522 | | No log | 37.0 | 259 | 0.5196 | 0.82 | 0.2796 | 1.5297 | 0.82 | 0.8057 | 0.1396 | 0.0523 | | No log | 38.0 | 266 | 0.5034 | 0.82 | 0.2755 | 1.4577 | 0.82 | 0.8000 | 0.1449 | 0.0524 | | No log | 39.0 | 273 | 0.5190 | 0.815 | 0.2810 | 1.5240 | 0.815 | 0.8003 | 0.1516 | 0.0533 | | No log | 40.0 | 280 | 0.4926 | 0.83 | 0.2697 | 1.4598 | 0.83 | 0.8161 | 0.1248 | 0.0514 | | No log | 41.0 | 287 | 0.5117 | 0.815 | 0.2808 | 1.5168 | 0.815 | 0.7965 | 0.1306 | 0.0525 | | No log | 42.0 | 294 | 0.5034 | 0.825 | 0.2721 | 1.5263 | 0.825 | 0.8143 | 0.1389 | 0.0533 | | No log | 43.0 | 301 | 0.5073 | 0.815 | 0.2762 | 1.5308 | 0.815 | 0.7916 | 0.1452 | 0.0511 | | No log | 44.0 | 308 | 0.5017 | 0.825 | 0.2751 | 1.5202 | 0.825 | 0.8095 | 0.1473 | 0.0525 | | No log | 45.0 | 315 | 0.5052 | 0.815 | 0.2783 | 1.5143 | 0.815 | 0.7965 | 0.1451 | 0.0525 | | No log | 46.0 | 322 | 0.5043 | 0.83 | 0.2743 | 1.5172 | 0.83 | 0.8172 | 0.1481 | 0.0517 | | No log | 47.0 | 329 | 0.5057 | 0.825 | 0.2767 | 1.5164 | 0.825 | 0.8089 | 0.1325 | 0.0520 | | No log | 48.0 | 336 | 0.5033 | 0.82 | 0.2752 | 1.5168 | 0.82 | 0.8061 | 0.1430 | 0.0523 | | No log | 49.0 | 343 | 0.5042 | 0.82 | 0.2755 | 1.5163 | 0.82 | 0.8061 | 0.1394 | 0.0517 | | No log | 50.0 | 350 | 0.5068 | 0.82 | 0.2767 | 1.5153 | 0.82 | 0.8061 | 0.1471 | 0.0517 | | No log | 51.0 | 357 | 0.5048 | 0.82 | 0.2759 | 1.5137 | 0.82 | 0.8061 | 0.1419 | 0.0519 | | No log | 52.0 | 364 | 0.5044 | 0.825 | 0.2759 | 1.5112 | 0.825 | 0.8064 | 0.1342 | 0.0518 | | No log | 53.0 | 371 | 0.5046 | 0.825 | 0.2756 | 1.5122 | 0.825 | 0.8116 | 0.1388 | 0.0514 | | No log | 54.0 | 378 | 0.5078 | 0.815 | 0.2777 | 1.5111 | 0.815 | 0.7984 | 0.1442 | 0.0519 | | No log | 55.0 | 385 | 0.5059 | 0.815 | 0.2767 | 1.5109 | 0.815 | 0.7984 | 0.1351 | 0.0518 | | No log | 56.0 | 392 | 0.5087 | 0.82 | 0.2779 | 1.5089 | 0.82 | 0.8061 | 0.1391 | 0.0518 | | No log | 57.0 | 399 | 0.5072 | 0.82 | 0.2771 | 1.5094 | 0.82 | 0.8061 | 0.1339 | 0.0517 | | No log | 58.0 | 406 | 0.5079 | 0.82 | 0.2776 | 1.5074 | 0.82 | 0.8061 | 0.1366 | 0.0518 | | No log | 59.0 | 413 | 0.5072 | 0.82 | 0.2771 | 1.5072 | 0.82 | 0.8061 | 0.1308 | 0.0518 | | No log | 60.0 | 420 | 0.5084 | 0.825 | 0.2776 | 1.5059 | 0.825 | 0.8116 | 0.1303 | 0.0520 | | No log | 61.0 | 427 | 0.5074 | 0.82 | 0.2772 | 1.5066 | 0.82 | 0.8038 | 0.1244 | 0.0520 | | No log | 62.0 | 434 | 0.5090 | 0.82 | 0.2781 | 1.5053 | 0.82 | 0.8061 | 0.1367 | 0.0519 | | No log | 63.0 | 441 | 0.5094 | 0.825 | 0.2779 | 1.5050 | 0.825 | 0.8116 | 0.1305 | 0.0520 | | No log | 64.0 | 448 | 0.5098 | 0.82 | 0.2782 | 1.5049 | 0.82 | 0.8038 | 0.1314 | 0.0520 | | No log | 65.0 | 455 | 0.5086 | 0.82 | 0.2780 | 1.5038 | 0.82 | 0.8038 | 0.1249 | 0.0520 | | No log | 66.0 | 462 | 0.5103 | 0.82 | 0.2787 | 1.5023 | 0.82 | 0.8038 | 0.1222 | 0.0522 | | No log | 67.0 | 469 | 0.5095 | 0.82 | 0.2782 | 1.5025 | 0.82 | 0.8038 | 0.1228 | 0.0521 | | No log | 68.0 | 476 | 0.5095 | 0.82 | 0.2783 | 1.5027 | 0.82 | 0.8038 | 0.1330 | 0.0522 | | No log | 69.0 | 483 | 0.5097 | 0.82 | 0.2785 | 1.5015 | 0.82 | 0.8038 | 0.1228 | 0.0521 | | No log | 70.0 | 490 | 0.5109 | 0.82 | 0.2788 | 1.5005 | 0.82 | 0.8038 | 0.1322 | 0.0520 | | No log | 71.0 | 497 | 0.5096 | 0.82 | 0.2784 | 1.5012 | 0.82 | 0.8038 | 0.1320 | 0.0522 | | 0.1366 | 72.0 | 504 | 0.5095 | 0.82 | 0.2784 | 1.5011 | 0.82 | 0.8038 | 0.1219 | 0.0522 | | 0.1366 | 73.0 | 511 | 0.5109 | 0.82 | 0.2791 | 1.4998 | 0.82 | 0.8038 | 0.1249 | 0.0523 | | 0.1366 | 74.0 | 518 | 0.5100 | 0.82 | 0.2786 | 1.5000 | 0.82 | 0.8038 | 0.1219 | 0.0521 | | 0.1366 | 75.0 | 525 | 0.5096 | 0.82 | 0.2784 | 1.5000 | 0.82 | 0.8038 | 0.1238 | 0.0521 | | 0.1366 | 76.0 | 532 | 0.5104 | 0.82 | 0.2787 | 1.4988 | 0.82 | 0.8038 | 0.1341 | 0.0523 | | 0.1366 | 77.0 | 539 | 0.5105 | 0.82 | 0.2788 | 1.4985 | 0.82 | 0.8038 | 0.1340 | 0.0521 | | 0.1366 | 78.0 | 546 | 0.5103 | 0.82 | 0.2788 | 1.4985 | 0.82 | 0.8038 | 0.1338 | 0.0520 | | 0.1366 | 79.0 | 553 | 0.5105 | 0.82 | 0.2788 | 1.4983 | 0.82 | 0.8038 | 0.1317 | 0.0522 | | 0.1366 | 80.0 | 560 | 0.5106 | 0.82 | 0.2789 | 1.4977 | 0.82 | 0.8038 | 0.1337 | 0.0523 | | 0.1366 | 81.0 | 567 | 0.5108 | 0.82 | 0.2790 | 1.4971 | 0.82 | 0.8038 | 0.1339 | 0.0523 | | 0.1366 | 82.0 | 574 | 0.5107 | 0.82 | 0.2790 | 1.4970 | 0.82 | 0.8038 | 0.1317 | 0.0521 | | 0.1366 | 83.0 | 581 | 0.5108 | 0.82 | 0.2790 | 1.4968 | 0.82 | 0.8038 | 0.1339 | 0.0522 | | 0.1366 | 84.0 | 588 | 0.5105 | 0.82 | 0.2789 | 1.4966 | 0.82 | 0.8038 | 0.1340 | 0.0522 | | 0.1366 | 85.0 | 595 | 0.5106 | 0.82 | 0.2789 | 1.4961 | 0.82 | 0.8038 | 0.1338 | 0.0523 | | 0.1366 | 86.0 | 602 | 0.5109 | 0.82 | 0.2790 | 1.4958 | 0.82 | 0.8038 | 0.1336 | 0.0524 | | 0.1366 | 87.0 | 609 | 0.5105 | 0.815 | 0.2789 | 1.4956 | 0.815 | 0.7942 | 0.1290 | 0.0525 | | 0.1366 | 88.0 | 616 | 0.5105 | 0.815 | 0.2790 | 1.4954 | 0.815 | 0.7942 | 0.1290 | 0.0525 | | 0.1366 | 89.0 | 623 | 0.5106 | 0.815 | 0.2790 | 1.4952 | 0.815 | 0.7942 | 0.1290 | 0.0526 | | 0.1366 | 90.0 | 630 | 0.5106 | 0.82 | 0.2790 | 1.4951 | 0.82 | 0.8038 | 0.1338 | 0.0523 | | 0.1366 | 91.0 | 637 | 0.5107 | 0.815 | 0.2790 | 1.4949 | 0.815 | 0.7942 | 0.1289 | 0.0526 | | 0.1366 | 92.0 | 644 | 0.5107 | 0.815 | 0.2790 | 1.4947 | 0.815 | 0.7942 | 0.1289 | 0.0526 | | 0.1366 | 93.0 | 651 | 0.5107 | 0.815 | 0.2790 | 1.4947 | 0.815 | 0.7942 | 0.1289 | 0.0525 | | 0.1366 | 94.0 | 658 | 0.5107 | 0.82 | 0.2790 | 1.4946 | 0.82 | 0.8038 | 0.1335 | 0.0523 | | 0.1366 | 95.0 | 665 | 0.5106 | 0.82 | 0.2790 | 1.4946 | 0.82 | 0.8038 | 0.1335 | 0.0523 | | 0.1366 | 96.0 | 672 | 0.5105 | 0.815 | 0.2790 | 1.4945 | 0.815 | 0.7942 | 0.1289 | 0.0524 | | 0.1366 | 97.0 | 679 | 0.5105 | 0.815 | 0.2790 | 1.4945 | 0.815 | 0.7942 | 0.1289 | 0.0524 | | 0.1366 | 98.0 | 686 | 0.5105 | 0.815 | 0.2790 | 1.4944 | 0.815 | 0.7942 | 0.1289 | 0.0524 | | 0.1366 | 99.0 | 693 | 0.5105 | 0.815 | 0.2790 | 1.4944 | 0.815 | 0.7942 | 0.1287 | 0.0524 | | 0.1366 | 100.0 | 700 | 0.5105 | 0.815 | 0.2790 | 1.4944 | 0.815 | 0.7942 | 0.1287 | 0.0524 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_small_sgd_0001_fold1

# hushem_5x_deit_small_sgd_0001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3695 - Accuracy: 0.3111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5195 | 1.0 | 27 | 1.4999 | 0.2889 | | 1.4914 | 2.0 | 54 | 1.4892 | 0.2889 | | 1.5108 | 3.0 | 81 | 1.4789 | 0.2889 | | 1.5345 | 4.0 | 108 | 1.4698 | 0.2667 | | 1.4684 | 5.0 | 135 | 1.4617 | 0.2667 | | 1.4525 | 6.0 | 162 | 1.4534 | 0.2667 | | 1.4298 | 7.0 | 189 | 1.4465 | 0.2667 | | 1.4569 | 8.0 | 216 | 1.4397 | 0.2444 | | 1.4283 | 9.0 | 243 | 1.4337 | 0.2444 | | 1.4203 | 10.0 | 270 | 1.4280 | 0.2444 | | 1.3871 | 11.0 | 297 | 1.4228 | 0.2444 | | 1.4156 | 12.0 | 324 | 1.4180 | 0.2444 | | 1.4346 | 13.0 | 351 | 1.4134 | 0.2444 | | 1.4076 | 14.0 | 378 | 1.4093 | 0.2444 | | 1.425 | 15.0 | 405 | 1.4059 | 0.2444 | | 1.4406 | 16.0 | 432 | 1.4025 | 0.2444 | | 1.4069 | 17.0 | 459 | 1.3996 | 0.2444 | | 1.3779 | 18.0 | 486 | 1.3968 | 0.2444 | | 1.3991 | 19.0 | 513 | 1.3941 | 0.2667 | | 1.3962 | 20.0 | 540 | 1.3918 | 0.2667 | | 1.3954 | 21.0 | 567 | 1.3897 | 0.2889 | | 1.3886 | 22.0 | 594 | 1.3877 | 0.2889 | | 1.3775 | 23.0 | 621 | 1.3858 | 0.2889 | | 1.3714 | 24.0 | 648 | 1.3842 | 0.2889 | | 1.4056 | 25.0 | 675 | 1.3826 | 0.2889 | | 1.4026 | 26.0 | 702 | 1.3812 | 0.2889 | | 1.359 | 27.0 | 729 | 1.3799 | 0.2889 | | 1.3709 | 28.0 | 756 | 1.3787 | 0.2889 | | 1.3667 | 29.0 | 783 | 1.3776 | 0.2889 | | 1.3672 | 30.0 | 810 | 1.3766 | 0.2889 | | 1.3762 | 31.0 | 837 | 1.3757 | 0.2889 | | 1.3384 | 32.0 | 864 | 1.3749 | 0.2889 | | 1.3698 | 33.0 | 891 | 1.3742 | 0.2889 | | 1.3636 | 34.0 | 918 | 1.3735 | 0.3111 | | 1.3439 | 35.0 | 945 | 1.3729 | 0.3111 | | 1.3571 | 36.0 | 972 | 1.3723 | 0.3111 | | 1.3688 | 37.0 | 999 | 1.3718 | 0.3111 | | 1.3527 | 38.0 | 1026 | 1.3714 | 0.3111 | | 1.3641 | 39.0 | 1053 | 1.3710 | 0.3111 | | 1.3538 | 40.0 | 1080 | 1.3707 | 0.3111 | | 1.3693 | 41.0 | 1107 | 1.3704 | 0.3111 | | 1.3789 | 42.0 | 1134 | 1.3701 | 0.3111 | | 1.3917 | 43.0 | 1161 | 1.3699 | 0.3111 | | 1.3524 | 44.0 | 1188 | 1.3698 | 0.3111 | | 1.367 | 45.0 | 1215 | 1.3696 | 0.3111 | | 1.3553 | 46.0 | 1242 | 1.3696 | 0.3111 | | 1.3523 | 47.0 | 1269 | 1.3695 | 0.3111 | | 1.3646 | 48.0 | 1296 | 1.3695 | 0.3111 | | 1.3891 | 49.0 | 1323 | 1.3695 | 0.3111 | | 1.3396 | 50.0 | 1350 | 1.3695 | 0.3111 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_0001_fold2

# hushem_5x_deit_small_sgd_0001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3896 - Accuracy: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5183 | 1.0 | 27 | 1.5038 | 0.1778 | | 1.502 | 2.0 | 54 | 1.4946 | 0.2 | | 1.5109 | 3.0 | 81 | 1.4859 | 0.2222 | | 1.5446 | 4.0 | 108 | 1.4781 | 0.2444 | | 1.4687 | 5.0 | 135 | 1.4710 | 0.2444 | | 1.4554 | 6.0 | 162 | 1.4641 | 0.2889 | | 1.4113 | 7.0 | 189 | 1.4582 | 0.2889 | | 1.4434 | 8.0 | 216 | 1.4525 | 0.2667 | | 1.4243 | 9.0 | 243 | 1.4473 | 0.2667 | | 1.4268 | 10.0 | 270 | 1.4425 | 0.2889 | | 1.386 | 11.0 | 297 | 1.4382 | 0.2889 | | 1.4235 | 12.0 | 324 | 1.4341 | 0.2667 | | 1.4228 | 13.0 | 351 | 1.4304 | 0.2667 | | 1.4091 | 14.0 | 378 | 1.4269 | 0.2889 | | 1.4135 | 15.0 | 405 | 1.4239 | 0.2667 | | 1.4228 | 16.0 | 432 | 1.4210 | 0.2889 | | 1.4188 | 17.0 | 459 | 1.4184 | 0.2889 | | 1.3824 | 18.0 | 486 | 1.4159 | 0.3333 | | 1.3861 | 19.0 | 513 | 1.4136 | 0.3111 | | 1.393 | 20.0 | 540 | 1.4115 | 0.3111 | | 1.4051 | 21.0 | 567 | 1.4096 | 0.3111 | | 1.373 | 22.0 | 594 | 1.4077 | 0.3333 | | 1.3737 | 23.0 | 621 | 1.4060 | 0.3333 | | 1.3668 | 24.0 | 648 | 1.4044 | 0.3556 | | 1.362 | 25.0 | 675 | 1.4030 | 0.3556 | | 1.3931 | 26.0 | 702 | 1.4016 | 0.3556 | | 1.3504 | 27.0 | 729 | 1.4003 | 0.3556 | | 1.3706 | 28.0 | 756 | 1.3992 | 0.3556 | | 1.359 | 29.0 | 783 | 1.3981 | 0.3556 | | 1.3774 | 30.0 | 810 | 1.3972 | 0.3556 | | 1.3678 | 31.0 | 837 | 1.3963 | 0.3556 | | 1.3418 | 32.0 | 864 | 1.3955 | 0.3556 | | 1.3702 | 33.0 | 891 | 1.3947 | 0.3556 | | 1.3589 | 34.0 | 918 | 1.3940 | 0.3556 | | 1.3212 | 35.0 | 945 | 1.3933 | 0.3333 | | 1.3648 | 36.0 | 972 | 1.3928 | 0.3333 | | 1.3509 | 37.0 | 999 | 1.3922 | 0.3333 | | 1.3506 | 38.0 | 1026 | 1.3917 | 0.3333 | | 1.3673 | 39.0 | 1053 | 1.3913 | 0.3333 | | 1.3657 | 40.0 | 1080 | 1.3910 | 0.3333 | | 1.3651 | 41.0 | 1107 | 1.3906 | 0.3333 | | 1.3688 | 42.0 | 1134 | 1.3904 | 0.3333 | | 1.3871 | 43.0 | 1161 | 1.3901 | 0.3333 | | 1.3307 | 44.0 | 1188 | 1.3899 | 0.3333 | | 1.3505 | 45.0 | 1215 | 1.3898 | 0.3333 | | 1.3367 | 46.0 | 1242 | 1.3897 | 0.3333 | | 1.3605 | 47.0 | 1269 | 1.3896 | 0.3333 | | 1.3556 | 48.0 | 1296 | 1.3896 | 0.3333 | | 1.3876 | 49.0 | 1323 | 1.3896 | 0.3333 | | 1.3357 | 50.0 | 1350 | 1.3896 | 0.3333 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_0001_fold3

# hushem_5x_deit_small_sgd_0001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4264 - Accuracy: 0.2093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5059 | 1.0 | 28 | 1.5906 | 0.2093 | | 1.4969 | 2.0 | 56 | 1.5795 | 0.2093 | | 1.4242 | 3.0 | 84 | 1.5692 | 0.2093 | | 1.4628 | 4.0 | 112 | 1.5599 | 0.2093 | | 1.5851 | 5.0 | 140 | 1.5506 | 0.2093 | | 1.4881 | 6.0 | 168 | 1.5418 | 0.2093 | | 1.4744 | 7.0 | 196 | 1.5338 | 0.2326 | | 1.427 | 8.0 | 224 | 1.5266 | 0.2558 | | 1.4274 | 9.0 | 252 | 1.5192 | 0.2558 | | 1.4168 | 10.0 | 280 | 1.5125 | 0.2558 | | 1.4418 | 11.0 | 308 | 1.5064 | 0.2558 | | 1.4361 | 12.0 | 336 | 1.5007 | 0.2558 | | 1.4139 | 13.0 | 364 | 1.4950 | 0.2558 | | 1.3932 | 14.0 | 392 | 1.4898 | 0.2558 | | 1.4041 | 15.0 | 420 | 1.4850 | 0.2326 | | 1.3745 | 16.0 | 448 | 1.4806 | 0.2326 | | 1.3653 | 17.0 | 476 | 1.4764 | 0.2093 | | 1.3841 | 18.0 | 504 | 1.4723 | 0.2093 | | 1.3735 | 19.0 | 532 | 1.4687 | 0.2093 | | 1.3391 | 20.0 | 560 | 1.4653 | 0.2093 | | 1.3879 | 21.0 | 588 | 1.4620 | 0.2093 | | 1.3861 | 22.0 | 616 | 1.4589 | 0.2093 | | 1.3726 | 23.0 | 644 | 1.4561 | 0.2093 | | 1.3725 | 24.0 | 672 | 1.4534 | 0.2093 | | 1.3587 | 25.0 | 700 | 1.4508 | 0.2093 | | 1.3359 | 26.0 | 728 | 1.4485 | 0.2093 | | 1.3627 | 27.0 | 756 | 1.4462 | 0.2326 | | 1.3855 | 28.0 | 784 | 1.4442 | 0.2326 | | 1.353 | 29.0 | 812 | 1.4424 | 0.2093 | | 1.301 | 30.0 | 840 | 1.4407 | 0.2093 | | 1.3248 | 31.0 | 868 | 1.4390 | 0.2093 | | 1.3654 | 32.0 | 896 | 1.4375 | 0.2093 | | 1.364 | 33.0 | 924 | 1.4361 | 0.2093 | | 1.322 | 34.0 | 952 | 1.4347 | 0.2093 | | 1.3619 | 35.0 | 980 | 1.4335 | 0.2093 | | 1.3562 | 36.0 | 1008 | 1.4324 | 0.2093 | | 1.4034 | 37.0 | 1036 | 1.4314 | 0.2093 | | 1.3401 | 38.0 | 1064 | 1.4304 | 0.2093 | | 1.3307 | 39.0 | 1092 | 1.4297 | 0.2093 | | 1.3736 | 40.0 | 1120 | 1.4290 | 0.2093 | | 1.3675 | 41.0 | 1148 | 1.4284 | 0.2093 | | 1.3234 | 42.0 | 1176 | 1.4279 | 0.2093 | | 1.3321 | 43.0 | 1204 | 1.4274 | 0.2093 | | 1.3436 | 44.0 | 1232 | 1.4270 | 0.2093 | | 1.3719 | 45.0 | 1260 | 1.4268 | 0.2093 | | 1.3462 | 46.0 | 1288 | 1.4266 | 0.2093 | | 1.3448 | 47.0 | 1316 | 1.4265 | 0.2093 | | 1.3465 | 48.0 | 1344 | 1.4264 | 0.2093 | | 1.2951 | 49.0 | 1372 | 1.4264 | 0.2093 | | 1.3665 | 50.0 | 1400 | 1.4264 | 0.2093 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_0001_fold4

# hushem_5x_deit_small_sgd_0001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3721 - Accuracy: 0.2619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5364 | 1.0 | 28 | 1.4759 | 0.2857 | | 1.6032 | 2.0 | 56 | 1.4677 | 0.2857 | | 1.5235 | 3.0 | 84 | 1.4598 | 0.2857 | | 1.5363 | 4.0 | 112 | 1.4530 | 0.2857 | | 1.4963 | 5.0 | 140 | 1.4466 | 0.2857 | | 1.4798 | 6.0 | 168 | 1.4404 | 0.2857 | | 1.4963 | 7.0 | 196 | 1.4349 | 0.2857 | | 1.441 | 8.0 | 224 | 1.4297 | 0.3095 | | 1.5032 | 9.0 | 252 | 1.4249 | 0.3095 | | 1.4231 | 10.0 | 280 | 1.4205 | 0.3095 | | 1.4482 | 11.0 | 308 | 1.4164 | 0.3095 | | 1.4398 | 12.0 | 336 | 1.4127 | 0.3095 | | 1.468 | 13.0 | 364 | 1.4093 | 0.3095 | | 1.4278 | 14.0 | 392 | 1.4061 | 0.3095 | | 1.4624 | 15.0 | 420 | 1.4032 | 0.3095 | | 1.438 | 16.0 | 448 | 1.4004 | 0.2857 | | 1.4401 | 17.0 | 476 | 1.3979 | 0.2857 | | 1.416 | 18.0 | 504 | 1.3956 | 0.3095 | | 1.4033 | 19.0 | 532 | 1.3934 | 0.3333 | | 1.4123 | 20.0 | 560 | 1.3916 | 0.3333 | | 1.4056 | 21.0 | 588 | 1.3899 | 0.3095 | | 1.4089 | 22.0 | 616 | 1.3883 | 0.3333 | | 1.3801 | 23.0 | 644 | 1.3868 | 0.3333 | | 1.3733 | 24.0 | 672 | 1.3854 | 0.3095 | | 1.3798 | 25.0 | 700 | 1.3840 | 0.3095 | | 1.4051 | 26.0 | 728 | 1.3828 | 0.3095 | | 1.4017 | 27.0 | 756 | 1.3817 | 0.3095 | | 1.4006 | 28.0 | 784 | 1.3807 | 0.3095 | | 1.368 | 29.0 | 812 | 1.3797 | 0.3095 | | 1.3628 | 30.0 | 840 | 1.3788 | 0.3333 | | 1.3803 | 31.0 | 868 | 1.3780 | 0.2619 | | 1.3495 | 32.0 | 896 | 1.3773 | 0.2619 | | 1.393 | 33.0 | 924 | 1.3766 | 0.2619 | | 1.3379 | 34.0 | 952 | 1.3760 | 0.2619 | | 1.3609 | 35.0 | 980 | 1.3754 | 0.2619 | | 1.3521 | 36.0 | 1008 | 1.3748 | 0.2619 | | 1.3648 | 37.0 | 1036 | 1.3744 | 0.2619 | | 1.341 | 38.0 | 1064 | 1.3740 | 0.2619 | | 1.3689 | 39.0 | 1092 | 1.3736 | 0.2619 | | 1.3877 | 40.0 | 1120 | 1.3733 | 0.2619 | | 1.4062 | 41.0 | 1148 | 1.3730 | 0.2619 | | 1.3585 | 42.0 | 1176 | 1.3727 | 0.2619 | | 1.3339 | 43.0 | 1204 | 1.3725 | 0.2619 | | 1.3351 | 44.0 | 1232 | 1.3724 | 0.2619 | | 1.3978 | 45.0 | 1260 | 1.3722 | 0.2619 | | 1.3819 | 46.0 | 1288 | 1.3721 | 0.2619 | | 1.3511 | 47.0 | 1316 | 1.3721 | 0.2619 | | 1.3593 | 48.0 | 1344 | 1.3721 | 0.2619 | | 1.3691 | 49.0 | 1372 | 1.3721 | 0.2619 | | 1.3757 | 50.0 | 1400 | 1.3721 | 0.2619 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-small_tobacco3482_kd_CEKD_t2.5_a0.5

# dit-base_tobacco-small_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6146 - Accuracy: 0.8 - Brier Loss: 0.2784 - Nll: 1.4268 - F1 Micro: 0.8000 - F1 Macro: 0.7846 - Ece: 0.1626 - Aurc: 0.0474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.1581 | 0.18 | 0.8974 | 4.2254 | 0.18 | 0.1559 | 0.2651 | 0.8061 | | No log | 2.0 | 14 | 3.2929 | 0.355 | 0.7710 | 4.0541 | 0.3550 | 0.2167 | 0.2742 | 0.4326 | | No log | 3.0 | 21 | 2.2155 | 0.55 | 0.5837 | 2.0462 | 0.55 | 0.4296 | 0.2323 | 0.2481 | | No log | 4.0 | 28 | 1.5197 | 0.7 | 0.4370 | 1.7716 | 0.7 | 0.6411 | 0.2342 | 0.1327 | | No log | 5.0 | 35 | 1.2831 | 0.715 | 0.4289 | 1.7142 | 0.715 | 0.6859 | 0.2047 | 0.1211 | | No log | 6.0 | 42 | 1.2204 | 0.72 | 0.3989 | 1.6102 | 0.72 | 0.6999 | 0.1961 | 0.1066 | | No log | 7.0 | 49 | 0.9767 | 0.755 | 0.3317 | 1.5919 | 0.755 | 0.7148 | 0.1724 | 0.0775 | | No log | 8.0 | 56 | 0.8875 | 0.785 | 0.3049 | 1.4209 | 0.785 | 0.7633 | 0.1478 | 0.0716 | | No log | 9.0 | 63 | 0.9311 | 0.79 | 0.3185 | 1.5420 | 0.79 | 0.7474 | 0.1645 | 0.0741 | | No log | 10.0 | 70 | 0.8116 | 0.835 | 0.2672 | 1.5127 | 0.835 | 0.8232 | 0.1463 | 0.0563 | | No log | 11.0 | 77 | 0.8315 | 0.805 | 0.3054 | 1.6275 | 0.805 | 0.7897 | 0.1695 | 0.0618 | | No log | 12.0 | 84 | 0.7678 | 0.815 | 0.2917 | 1.5009 | 0.815 | 0.8012 | 0.1469 | 0.0542 | | No log | 13.0 | 91 | 0.7249 | 0.81 | 0.2816 | 1.4685 | 0.81 | 0.7880 | 0.1437 | 0.0576 | | No log | 14.0 | 98 | 0.8116 | 0.815 | 0.2894 | 1.5975 | 0.815 | 0.7941 | 0.1481 | 0.0604 | | No log | 15.0 | 105 | 0.7985 | 0.81 | 0.3098 | 1.4721 | 0.81 | 0.7819 | 0.1646 | 0.0662 | | No log | 16.0 | 112 | 0.6839 | 0.815 | 0.2781 | 1.4357 | 0.815 | 0.7992 | 0.1589 | 0.0529 | | No log | 17.0 | 119 | 0.6590 | 0.82 | 0.2670 | 1.4487 | 0.82 | 0.8061 | 0.1336 | 0.0461 | | No log | 18.0 | 126 | 0.7253 | 0.81 | 0.2938 | 1.5163 | 0.81 | 0.7951 | 0.1617 | 0.0558 | | No log | 19.0 | 133 | 0.6935 | 0.795 | 0.2949 | 1.4516 | 0.795 | 0.7758 | 0.1736 | 0.0531 | | No log | 20.0 | 140 | 0.6991 | 0.795 | 0.2875 | 1.3932 | 0.795 | 0.7735 | 0.1584 | 0.0519 | | No log | 21.0 | 147 | 0.7059 | 0.815 | 0.2966 | 1.5011 | 0.815 | 0.7927 | 0.1579 | 0.0565 | | No log | 22.0 | 154 | 0.6754 | 0.79 | 0.2896 | 1.4549 | 0.79 | 0.7742 | 0.1534 | 0.0531 | | No log | 23.0 | 161 | 0.6981 | 0.785 | 0.2989 | 1.4261 | 0.785 | 0.7705 | 0.1490 | 0.0530 | | No log | 24.0 | 168 | 0.6503 | 0.805 | 0.2842 | 1.4998 | 0.805 | 0.7885 | 0.1415 | 0.0512 | | No log | 25.0 | 175 | 0.6680 | 0.79 | 0.2891 | 1.4228 | 0.79 | 0.7742 | 0.1504 | 0.0519 | | No log | 26.0 | 182 | 0.6835 | 0.81 | 0.2948 | 1.4400 | 0.81 | 0.7944 | 0.1545 | 0.0516 | | No log | 27.0 | 189 | 0.6495 | 0.81 | 0.2846 | 1.4433 | 0.81 | 0.7868 | 0.1552 | 0.0503 | | No log | 28.0 | 196 | 0.6450 | 0.81 | 0.2851 | 1.4037 | 0.81 | 0.7913 | 0.1476 | 0.0498 | | No log | 29.0 | 203 | 0.6634 | 0.815 | 0.2861 | 1.4186 | 0.815 | 0.7966 | 0.1397 | 0.0521 | | No log | 30.0 | 210 | 0.6212 | 0.805 | 0.2739 | 1.4265 | 0.805 | 0.7902 | 0.1444 | 0.0482 | | No log | 31.0 | 217 | 0.6271 | 0.815 | 0.2800 | 1.4392 | 0.815 | 0.7986 | 0.1370 | 0.0494 | | No log | 32.0 | 224 | 0.6256 | 0.8 | 0.2786 | 1.3677 | 0.8000 | 0.7811 | 0.1454 | 0.0496 | | No log | 33.0 | 231 | 0.6219 | 0.805 | 0.2779 | 1.4276 | 0.805 | 0.7857 | 0.1580 | 0.0465 | | No log | 34.0 | 238 | 0.6203 | 0.81 | 0.2779 | 1.4392 | 0.81 | 0.7914 | 0.1275 | 0.0470 | | No log | 35.0 | 245 | 0.6193 | 0.81 | 0.2793 | 1.4258 | 0.81 | 0.7934 | 0.1438 | 0.0483 | | No log | 36.0 | 252 | 0.6261 | 0.83 | 0.2743 | 1.4227 | 0.83 | 0.8098 | 0.1482 | 0.0501 | | No log | 37.0 | 259 | 0.6190 | 0.815 | 0.2776 | 1.4301 | 0.815 | 0.7977 | 0.1446 | 0.0484 | | No log | 38.0 | 266 | 0.6210 | 0.805 | 0.2867 | 1.4958 | 0.805 | 0.7878 | 0.1477 | 0.0496 | | No log | 39.0 | 273 | 0.5974 | 0.805 | 0.2771 | 1.5068 | 0.805 | 0.7901 | 0.1381 | 0.0476 | | No log | 40.0 | 280 | 0.6224 | 0.8 | 0.2869 | 1.4325 | 0.8000 | 0.7869 | 0.1443 | 0.0472 | | No log | 41.0 | 287 | 0.6178 | 0.805 | 0.2796 | 1.4316 | 0.805 | 0.7912 | 0.1454 | 0.0471 | | No log | 42.0 | 294 | 0.6194 | 0.825 | 0.2765 | 1.5001 | 0.825 | 0.8059 | 0.1401 | 0.0474 | | No log | 43.0 | 301 | 0.6224 | 0.805 | 0.2769 | 1.4268 | 0.805 | 0.7888 | 0.1398 | 0.0493 | | No log | 44.0 | 308 | 0.6265 | 0.8 | 0.2819 | 1.4401 | 0.8000 | 0.7846 | 0.1422 | 0.0481 | | No log | 45.0 | 315 | 0.6275 | 0.8 | 0.2819 | 1.4206 | 0.8000 | 0.7847 | 0.1465 | 0.0487 | | No log | 46.0 | 322 | 0.6173 | 0.805 | 0.2806 | 1.3618 | 0.805 | 0.7870 | 0.1383 | 0.0478 | | No log | 47.0 | 329 | 0.6177 | 0.81 | 0.2804 | 1.4988 | 0.81 | 0.7906 | 0.1468 | 0.0488 | | No log | 48.0 | 336 | 0.6175 | 0.81 | 0.2788 | 1.4356 | 0.81 | 0.7917 | 0.1460 | 0.0476 | | No log | 49.0 | 343 | 0.6209 | 0.81 | 0.2775 | 1.4290 | 0.81 | 0.7925 | 0.1603 | 0.0478 | | No log | 50.0 | 350 | 0.6244 | 0.815 | 0.2780 | 1.3662 | 0.815 | 0.7974 | 0.1322 | 0.0480 | | No log | 51.0 | 357 | 0.6176 | 0.81 | 0.2777 | 1.4307 | 0.81 | 0.7941 | 0.1258 | 0.0478 | | No log | 52.0 | 364 | 0.6150 | 0.805 | 0.2774 | 1.4310 | 0.805 | 0.7896 | 0.1369 | 0.0477 | | No log | 53.0 | 371 | 0.6164 | 0.81 | 0.2772 | 1.4298 | 0.81 | 0.7941 | 0.1391 | 0.0479 | | No log | 54.0 | 378 | 0.6137 | 0.81 | 0.2766 | 1.4291 | 0.81 | 0.7928 | 0.1358 | 0.0474 | | No log | 55.0 | 385 | 0.6163 | 0.81 | 0.2776 | 1.4298 | 0.81 | 0.7928 | 0.1278 | 0.0475 | | No log | 56.0 | 392 | 0.6148 | 0.81 | 0.2776 | 1.4286 | 0.81 | 0.7928 | 0.1480 | 0.0471 | | No log | 57.0 | 399 | 0.6154 | 0.81 | 0.2773 | 1.4290 | 0.81 | 0.7928 | 0.1485 | 0.0474 | | No log | 58.0 | 406 | 0.6143 | 0.8 | 0.2781 | 1.4281 | 0.8000 | 0.7852 | 0.1405 | 0.0473 | | No log | 59.0 | 413 | 0.6158 | 0.805 | 0.2785 | 1.4295 | 0.805 | 0.7899 | 0.1455 | 0.0473 | | No log | 60.0 | 420 | 0.6146 | 0.805 | 0.2774 | 1.4310 | 0.805 | 0.7899 | 0.1346 | 0.0472 | | No log | 61.0 | 427 | 0.6154 | 0.805 | 0.2780 | 1.4292 | 0.805 | 0.7899 | 0.1451 | 0.0472 | | No log | 62.0 | 434 | 0.6148 | 0.805 | 0.2780 | 1.4304 | 0.805 | 0.7905 | 0.1543 | 0.0473 | | No log | 63.0 | 441 | 0.6150 | 0.8 | 0.2783 | 1.4284 | 0.8000 | 0.7846 | 0.1502 | 0.0473 | | No log | 64.0 | 448 | 0.6143 | 0.805 | 0.2780 | 1.4294 | 0.805 | 0.7899 | 0.1453 | 0.0470 | | No log | 65.0 | 455 | 0.6152 | 0.805 | 0.2782 | 1.4298 | 0.805 | 0.7899 | 0.1373 | 0.0469 | | No log | 66.0 | 462 | 0.6148 | 0.8 | 0.2781 | 1.4287 | 0.8000 | 0.7852 | 0.1492 | 0.0475 | | No log | 67.0 | 469 | 0.6134 | 0.805 | 0.2776 | 1.4286 | 0.805 | 0.7899 | 0.1526 | 0.0470 | | No log | 68.0 | 476 | 0.6150 | 0.8 | 0.2785 | 1.4270 | 0.8000 | 0.7846 | 0.1497 | 0.0474 | | No log | 69.0 | 483 | 0.6145 | 0.8 | 0.2783 | 1.4281 | 0.8000 | 0.7846 | 0.1483 | 0.0471 | | No log | 70.0 | 490 | 0.6145 | 0.805 | 0.2778 | 1.4292 | 0.805 | 0.7899 | 0.1472 | 0.0471 | | No log | 71.0 | 497 | 0.6143 | 0.805 | 0.2779 | 1.4284 | 0.805 | 0.7899 | 0.1529 | 0.0470 | | 0.2616 | 72.0 | 504 | 0.6148 | 0.805 | 0.2780 | 1.4276 | 0.805 | 0.7899 | 0.1414 | 0.0471 | | 0.2616 | 73.0 | 511 | 0.6147 | 0.8 | 0.2781 | 1.4285 | 0.8000 | 0.7852 | 0.1400 | 0.0473 | | 0.2616 | 74.0 | 518 | 0.6147 | 0.8 | 0.2783 | 1.4281 | 0.8000 | 0.7846 | 0.1501 | 0.0473 | | 0.2616 | 75.0 | 525 | 0.6150 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1417 | 0.0473 | | 0.2616 | 76.0 | 532 | 0.6143 | 0.805 | 0.2782 | 1.4273 | 0.805 | 0.7899 | 0.1524 | 0.0470 | | 0.2616 | 77.0 | 539 | 0.6147 | 0.805 | 0.2782 | 1.4277 | 0.805 | 0.7899 | 0.1526 | 0.0470 | | 0.2616 | 78.0 | 546 | 0.6149 | 0.8 | 0.2785 | 1.4277 | 0.8000 | 0.7846 | 0.1572 | 0.0474 | | 0.2616 | 79.0 | 553 | 0.6147 | 0.805 | 0.2782 | 1.4276 | 0.805 | 0.7899 | 0.1529 | 0.0471 | | 0.2616 | 80.0 | 560 | 0.6145 | 0.805 | 0.2783 | 1.4278 | 0.805 | 0.7899 | 0.1527 | 0.0471 | | 0.2616 | 81.0 | 567 | 0.6147 | 0.8 | 0.2783 | 1.4277 | 0.8000 | 0.7846 | 0.1483 | 0.0472 | | 0.2616 | 82.0 | 574 | 0.6146 | 0.8 | 0.2783 | 1.4275 | 0.8000 | 0.7846 | 0.1623 | 0.0473 | | 0.2616 | 83.0 | 581 | 0.6145 | 0.8 | 0.2783 | 1.4274 | 0.8000 | 0.7846 | 0.1571 | 0.0473 | | 0.2616 | 84.0 | 588 | 0.6146 | 0.8 | 0.2782 | 1.4276 | 0.8000 | 0.7846 | 0.1538 | 0.0473 | | 0.2616 | 85.0 | 595 | 0.6146 | 0.805 | 0.2783 | 1.4274 | 0.805 | 0.7899 | 0.1493 | 0.0471 | | 0.2616 | 86.0 | 602 | 0.6147 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1627 | 0.0473 | | 0.2616 | 87.0 | 609 | 0.6146 | 0.8 | 0.2783 | 1.4270 | 0.8000 | 0.7846 | 0.1623 | 0.0472 | | 0.2616 | 88.0 | 616 | 0.6145 | 0.805 | 0.2783 | 1.4272 | 0.805 | 0.7899 | 0.1579 | 0.0470 | | 0.2616 | 89.0 | 623 | 0.6146 | 0.8 | 0.2784 | 1.4272 | 0.8000 | 0.7846 | 0.1627 | 0.0474 | | 0.2616 | 90.0 | 630 | 0.6147 | 0.8 | 0.2783 | 1.4270 | 0.8000 | 0.7846 | 0.1536 | 0.0473 | | 0.2616 | 91.0 | 637 | 0.6147 | 0.8 | 0.2784 | 1.4268 | 0.8000 | 0.7846 | 0.1627 | 0.0475 | | 0.2616 | 92.0 | 644 | 0.6145 | 0.805 | 0.2783 | 1.4268 | 0.805 | 0.7899 | 0.1582 | 0.0471 | | 0.2616 | 93.0 | 651 | 0.6145 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | | 0.2616 | 94.0 | 658 | 0.6146 | 0.8 | 0.2784 | 1.4268 | 0.8000 | 0.7846 | 0.1626 | 0.0473 | | 0.2616 | 95.0 | 665 | 0.6147 | 0.8 | 0.2784 | 1.4268 | 0.8000 | 0.7846 | 0.1626 | 0.0473 | | 0.2616 | 96.0 | 672 | 0.6146 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | | 0.2616 | 97.0 | 679 | 0.6146 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | | 0.2616 | 98.0 | 686 | 0.6146 | 0.8 | 0.2784 | 1.4269 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | | 0.2616 | 99.0 | 693 | 0.6146 | 0.8 | 0.2784 | 1.4268 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | | 0.2616 | 100.0 | 700 | 0.6146 | 0.8 | 0.2784 | 1.4268 | 0.8000 | 0.7846 | 0.1626 | 0.0474 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_small_sgd_0001_fold5

# hushem_5x_deit_small_sgd_0001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3702 - Accuracy: 0.2683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5914 | 1.0 | 28 | 1.5102 | 0.2195 | | 1.502 | 2.0 | 56 | 1.4998 | 0.2439 | | 1.5359 | 3.0 | 84 | 1.4897 | 0.2439 | | 1.4953 | 4.0 | 112 | 1.4806 | 0.2195 | | 1.505 | 5.0 | 140 | 1.4721 | 0.2439 | | 1.5366 | 6.0 | 168 | 1.4645 | 0.2439 | | 1.5251 | 7.0 | 196 | 1.4572 | 0.2439 | | 1.4698 | 8.0 | 224 | 1.4506 | 0.2439 | | 1.4915 | 9.0 | 252 | 1.4443 | 0.2439 | | 1.4618 | 10.0 | 280 | 1.4384 | 0.2439 | | 1.4473 | 11.0 | 308 | 1.4329 | 0.2439 | | 1.4682 | 12.0 | 336 | 1.4279 | 0.2439 | | 1.4426 | 13.0 | 364 | 1.4233 | 0.2439 | | 1.4128 | 14.0 | 392 | 1.4190 | 0.2683 | | 1.4363 | 15.0 | 420 | 1.4150 | 0.2683 | | 1.4383 | 16.0 | 448 | 1.4113 | 0.2683 | | 1.4168 | 17.0 | 476 | 1.4079 | 0.2683 | | 1.4317 | 18.0 | 504 | 1.4047 | 0.2683 | | 1.4208 | 19.0 | 532 | 1.4016 | 0.2927 | | 1.4021 | 20.0 | 560 | 1.3989 | 0.2927 | | 1.4325 | 21.0 | 588 | 1.3963 | 0.2927 | | 1.4072 | 22.0 | 616 | 1.3940 | 0.2927 | | 1.3729 | 23.0 | 644 | 1.3918 | 0.2927 | | 1.3955 | 24.0 | 672 | 1.3898 | 0.2927 | | 1.3868 | 25.0 | 700 | 1.3879 | 0.2927 | | 1.3985 | 26.0 | 728 | 1.3861 | 0.2683 | | 1.3854 | 27.0 | 756 | 1.3845 | 0.2683 | | 1.3968 | 28.0 | 784 | 1.3830 | 0.2683 | | 1.3689 | 29.0 | 812 | 1.3816 | 0.2683 | | 1.4069 | 30.0 | 840 | 1.3803 | 0.2683 | | 1.387 | 31.0 | 868 | 1.3791 | 0.2683 | | 1.3786 | 32.0 | 896 | 1.3780 | 0.2683 | | 1.3773 | 33.0 | 924 | 1.3769 | 0.2683 | | 1.3779 | 34.0 | 952 | 1.3760 | 0.2683 | | 1.3797 | 35.0 | 980 | 1.3751 | 0.2683 | | 1.3671 | 36.0 | 1008 | 1.3744 | 0.2683 | | 1.3638 | 37.0 | 1036 | 1.3737 | 0.2683 | | 1.3614 | 38.0 | 1064 | 1.3731 | 0.2683 | | 1.3646 | 39.0 | 1092 | 1.3725 | 0.2683 | | 1.3609 | 40.0 | 1120 | 1.3720 | 0.2683 | | 1.3899 | 41.0 | 1148 | 1.3716 | 0.2683 | | 1.3896 | 42.0 | 1176 | 1.3712 | 0.2683 | | 1.3725 | 43.0 | 1204 | 1.3709 | 0.2683 | | 1.3896 | 44.0 | 1232 | 1.3706 | 0.2683 | | 1.3695 | 45.0 | 1260 | 1.3704 | 0.2683 | | 1.3698 | 46.0 | 1288 | 1.3703 | 0.2683 | | 1.3813 | 47.0 | 1316 | 1.3702 | 0.2683 | | 1.3636 | 48.0 | 1344 | 1.3702 | 0.2683 | | 1.3528 | 49.0 | 1372 | 1.3702 | 0.2683 | | 1.3747 | 50.0 | 1400 | 1.3702 | 0.2683 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_001_fold1

# hushem_5x_deit_small_sgd_001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2065 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.469 | 1.0 | 27 | 1.4260 | 0.2444 | | 1.3863 | 2.0 | 54 | 1.3825 | 0.2889 | | 1.3604 | 3.0 | 81 | 1.3624 | 0.3333 | | 1.3505 | 4.0 | 108 | 1.3566 | 0.4 | | 1.2837 | 5.0 | 135 | 1.3537 | 0.4 | | 1.2588 | 6.0 | 162 | 1.3503 | 0.4 | | 1.243 | 7.0 | 189 | 1.3481 | 0.4 | | 1.2286 | 8.0 | 216 | 1.3453 | 0.4222 | | 1.1857 | 9.0 | 243 | 1.3417 | 0.4222 | | 1.1652 | 10.0 | 270 | 1.3373 | 0.4222 | | 1.135 | 11.0 | 297 | 1.3328 | 0.4222 | | 1.1257 | 12.0 | 324 | 1.3276 | 0.4222 | | 1.1465 | 13.0 | 351 | 1.3209 | 0.4444 | | 1.0952 | 14.0 | 378 | 1.3151 | 0.4444 | | 1.0919 | 15.0 | 405 | 1.3098 | 0.4444 | | 1.0847 | 16.0 | 432 | 1.3049 | 0.4222 | | 1.0446 | 17.0 | 459 | 1.2985 | 0.4667 | | 1.0054 | 18.0 | 486 | 1.2928 | 0.4444 | | 1.0169 | 19.0 | 513 | 1.2866 | 0.4222 | | 1.0001 | 20.0 | 540 | 1.2827 | 0.4222 | | 0.9603 | 21.0 | 567 | 1.2785 | 0.4222 | | 0.9735 | 22.0 | 594 | 1.2740 | 0.4444 | | 0.9444 | 23.0 | 621 | 1.2697 | 0.4667 | | 0.9156 | 24.0 | 648 | 1.2621 | 0.4444 | | 0.9658 | 25.0 | 675 | 1.2568 | 0.4444 | | 0.9699 | 26.0 | 702 | 1.2527 | 0.4444 | | 0.8985 | 27.0 | 729 | 1.2504 | 0.4444 | | 0.8958 | 28.0 | 756 | 1.2453 | 0.4222 | | 0.9214 | 29.0 | 783 | 1.2429 | 0.4444 | | 0.9068 | 30.0 | 810 | 1.2374 | 0.4222 | | 0.8659 | 31.0 | 837 | 1.2336 | 0.4222 | | 0.8774 | 32.0 | 864 | 1.2303 | 0.4222 | | 0.8587 | 33.0 | 891 | 1.2279 | 0.4222 | | 0.8719 | 34.0 | 918 | 1.2241 | 0.4222 | | 0.8448 | 35.0 | 945 | 1.2216 | 0.4222 | | 0.8563 | 36.0 | 972 | 1.2203 | 0.4222 | | 0.8555 | 37.0 | 999 | 1.2179 | 0.4222 | | 0.8252 | 38.0 | 1026 | 1.2169 | 0.4222 | | 0.83 | 39.0 | 1053 | 1.2146 | 0.4222 | | 0.8062 | 40.0 | 1080 | 1.2129 | 0.4222 | | 0.8472 | 41.0 | 1107 | 1.2110 | 0.4222 | | 0.8075 | 42.0 | 1134 | 1.2099 | 0.4222 | | 0.8415 | 43.0 | 1161 | 1.2087 | 0.4222 | | 0.8064 | 44.0 | 1188 | 1.2081 | 0.4444 | | 0.8219 | 45.0 | 1215 | 1.2076 | 0.4444 | | 0.8297 | 46.0 | 1242 | 1.2069 | 0.4444 | | 0.8108 | 47.0 | 1269 | 1.2066 | 0.4444 | | 0.8128 | 48.0 | 1296 | 1.2065 | 0.4444 | | 0.8385 | 49.0 | 1323 | 1.2065 | 0.4444 | | 0.8247 | 50.0 | 1350 | 1.2065 | 0.4444 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_001_fold2

# hushem_5x_deit_small_sgd_001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3629 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4659 | 1.0 | 27 | 1.4409 | 0.2889 | | 1.3866 | 2.0 | 54 | 1.4026 | 0.3556 | | 1.3486 | 3.0 | 81 | 1.3816 | 0.3333 | | 1.3477 | 4.0 | 108 | 1.3676 | 0.3111 | | 1.2816 | 5.0 | 135 | 1.3557 | 0.3333 | | 1.2558 | 6.0 | 162 | 1.3444 | 0.3556 | | 1.2259 | 7.0 | 189 | 1.3343 | 0.3556 | | 1.2042 | 8.0 | 216 | 1.3245 | 0.3556 | | 1.1683 | 9.0 | 243 | 1.3158 | 0.4 | | 1.1515 | 10.0 | 270 | 1.3086 | 0.4222 | | 1.1156 | 11.0 | 297 | 1.3037 | 0.4222 | | 1.1061 | 12.0 | 324 | 1.2999 | 0.4444 | | 1.0903 | 13.0 | 351 | 1.3002 | 0.4444 | | 1.0661 | 14.0 | 378 | 1.3028 | 0.4444 | | 1.0598 | 15.0 | 405 | 1.3085 | 0.4444 | | 1.0378 | 16.0 | 432 | 1.3130 | 0.4444 | | 1.0191 | 17.0 | 459 | 1.3179 | 0.4444 | | 0.9884 | 18.0 | 486 | 1.3238 | 0.4444 | | 0.9629 | 19.0 | 513 | 1.3282 | 0.4444 | | 0.9575 | 20.0 | 540 | 1.3319 | 0.4222 | | 0.9397 | 21.0 | 567 | 1.3353 | 0.4222 | | 0.9296 | 22.0 | 594 | 1.3380 | 0.4222 | | 0.9149 | 23.0 | 621 | 1.3408 | 0.4222 | | 0.9023 | 24.0 | 648 | 1.3446 | 0.4222 | | 0.8747 | 25.0 | 675 | 1.3454 | 0.4667 | | 0.9184 | 26.0 | 702 | 1.3472 | 0.4444 | | 0.8454 | 27.0 | 729 | 1.3479 | 0.4444 | | 0.8505 | 28.0 | 756 | 1.3510 | 0.4444 | | 0.8567 | 29.0 | 783 | 1.3517 | 0.4444 | | 0.8854 | 30.0 | 810 | 1.3544 | 0.4667 | | 0.834 | 31.0 | 837 | 1.3546 | 0.4444 | | 0.8438 | 32.0 | 864 | 1.3560 | 0.4444 | | 0.8236 | 33.0 | 891 | 1.3564 | 0.4444 | | 0.8208 | 34.0 | 918 | 1.3570 | 0.4444 | | 0.8066 | 35.0 | 945 | 1.3589 | 0.4444 | | 0.8073 | 36.0 | 972 | 1.3591 | 0.4444 | | 0.8089 | 37.0 | 999 | 1.3595 | 0.4444 | | 0.777 | 38.0 | 1026 | 1.3599 | 0.4444 | | 0.7828 | 39.0 | 1053 | 1.3610 | 0.4444 | | 0.787 | 40.0 | 1080 | 1.3609 | 0.4444 | | 0.8016 | 41.0 | 1107 | 1.3612 | 0.4444 | | 0.7822 | 42.0 | 1134 | 1.3619 | 0.4444 | | 0.8105 | 43.0 | 1161 | 1.3621 | 0.4444 | | 0.7646 | 44.0 | 1188 | 1.3622 | 0.4444 | | 0.7928 | 45.0 | 1215 | 1.3624 | 0.4444 | | 0.7714 | 46.0 | 1242 | 1.3625 | 0.4444 | | 0.7741 | 47.0 | 1269 | 1.3627 | 0.4444 | | 0.7688 | 48.0 | 1296 | 1.3629 | 0.4444 | | 0.834 | 49.0 | 1323 | 1.3629 | 0.4444 | | 0.7751 | 50.0 | 1350 | 1.3629 | 0.4444 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_001_fold3

# hushem_5x_deit_small_sgd_001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1692 - Accuracy: 0.4186 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4612 | 1.0 | 28 | 1.5118 | 0.2558 | | 1.3805 | 2.0 | 56 | 1.4497 | 0.2093 | | 1.3041 | 3.0 | 84 | 1.4123 | 0.2093 | | 1.3096 | 4.0 | 112 | 1.3866 | 0.2558 | | 1.3306 | 5.0 | 140 | 1.3677 | 0.2558 | | 1.2628 | 6.0 | 168 | 1.3531 | 0.2791 | | 1.2467 | 7.0 | 196 | 1.3404 | 0.2791 | | 1.2266 | 8.0 | 224 | 1.3306 | 0.3023 | | 1.194 | 9.0 | 252 | 1.3214 | 0.3023 | | 1.1663 | 10.0 | 280 | 1.3122 | 0.2791 | | 1.1821 | 11.0 | 308 | 1.3053 | 0.3023 | | 1.1428 | 12.0 | 336 | 1.2975 | 0.3023 | | 1.1221 | 13.0 | 364 | 1.2914 | 0.3023 | | 1.0981 | 14.0 | 392 | 1.2852 | 0.3023 | | 1.1081 | 15.0 | 420 | 1.2792 | 0.3488 | | 1.0434 | 16.0 | 448 | 1.2755 | 0.3488 | | 1.0661 | 17.0 | 476 | 1.2676 | 0.3721 | | 1.0349 | 18.0 | 504 | 1.2624 | 0.3721 | | 1.0251 | 19.0 | 532 | 1.2563 | 0.3721 | | 0.9878 | 20.0 | 560 | 1.2508 | 0.3721 | | 1.0276 | 21.0 | 588 | 1.2459 | 0.3721 | | 0.9848 | 22.0 | 616 | 1.2419 | 0.3721 | | 0.9825 | 23.0 | 644 | 1.2362 | 0.3721 | | 0.9289 | 24.0 | 672 | 1.2312 | 0.3721 | | 0.9221 | 25.0 | 700 | 1.2274 | 0.3721 | | 0.9187 | 26.0 | 728 | 1.2222 | 0.3721 | | 0.9248 | 27.0 | 756 | 1.2177 | 0.3953 | | 0.9505 | 28.0 | 784 | 1.2135 | 0.3721 | | 0.9022 | 29.0 | 812 | 1.2094 | 0.3721 | | 0.8445 | 30.0 | 840 | 1.2060 | 0.3721 | | 0.861 | 31.0 | 868 | 1.2023 | 0.3953 | | 0.9005 | 32.0 | 896 | 1.1985 | 0.3953 | | 0.8936 | 33.0 | 924 | 1.1956 | 0.3953 | | 0.8469 | 34.0 | 952 | 1.1923 | 0.3953 | | 0.9675 | 35.0 | 980 | 1.1892 | 0.3953 | | 0.8615 | 36.0 | 1008 | 1.1869 | 0.3953 | | 0.8944 | 37.0 | 1036 | 1.1838 | 0.3953 | | 0.8374 | 38.0 | 1064 | 1.1820 | 0.3953 | | 0.8431 | 39.0 | 1092 | 1.1797 | 0.3953 | | 0.8451 | 40.0 | 1120 | 1.1776 | 0.3953 | | 0.8614 | 41.0 | 1148 | 1.1756 | 0.3953 | | 0.8347 | 42.0 | 1176 | 1.1741 | 0.3953 | | 0.8159 | 43.0 | 1204 | 1.1724 | 0.4419 | | 0.8229 | 44.0 | 1232 | 1.1713 | 0.4419 | | 0.8046 | 45.0 | 1260 | 1.1703 | 0.4186 | | 0.8129 | 46.0 | 1288 | 1.1698 | 0.4186 | | 0.8156 | 47.0 | 1316 | 1.1693 | 0.4186 | | 0.8259 | 48.0 | 1344 | 1.1692 | 0.4186 | | 0.7943 | 49.0 | 1372 | 1.1692 | 0.4186 | | 0.8288 | 50.0 | 1400 | 1.1692 | 0.4186 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-small_tobacco3482_kd_MSE

# dit-base_tobacco-small_tobacco3482_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7746 - Accuracy: 0.81 - Brier Loss: 0.2775 - Nll: 1.1981 - F1 Micro: 0.81 - F1 Macro: 0.7980 - Ece: 0.1403 - Aurc: 0.0500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 7.0083 | 0.14 | 0.9250 | 5.4655 | 0.14 | 0.1468 | 0.2824 | 0.8920 | | No log | 2.0 | 14 | 5.8247 | 0.35 | 0.7844 | 3.6804 | 0.35 | 0.2240 | 0.2541 | 0.5601 | | No log | 3.0 | 21 | 4.1788 | 0.49 | 0.6140 | 1.8305 | 0.49 | 0.4563 | 0.2512 | 0.2825 | | No log | 4.0 | 28 | 2.7911 | 0.66 | 0.4534 | 1.6541 | 0.66 | 0.5604 | 0.2299 | 0.1475 | | No log | 5.0 | 35 | 2.3354 | 0.74 | 0.3892 | 1.8678 | 0.74 | 0.6851 | 0.2104 | 0.0989 | | No log | 6.0 | 42 | 1.9675 | 0.73 | 0.3585 | 1.3943 | 0.7300 | 0.6822 | 0.1846 | 0.0930 | | No log | 7.0 | 49 | 1.7187 | 0.79 | 0.3190 | 1.3921 | 0.79 | 0.7510 | 0.1739 | 0.0760 | | No log | 8.0 | 56 | 1.6507 | 0.77 | 0.3469 | 1.3682 | 0.7700 | 0.7289 | 0.1834 | 0.0851 | | No log | 9.0 | 63 | 1.2713 | 0.79 | 0.3040 | 1.4042 | 0.79 | 0.7622 | 0.1505 | 0.0540 | | No log | 10.0 | 70 | 1.1461 | 0.805 | 0.2852 | 1.3953 | 0.805 | 0.7849 | 0.1371 | 0.0522 | | No log | 11.0 | 77 | 1.1328 | 0.81 | 0.2713 | 1.3113 | 0.81 | 0.7901 | 0.1371 | 0.0442 | | No log | 12.0 | 84 | 1.2818 | 0.8 | 0.3192 | 1.2680 | 0.8000 | 0.7808 | 0.1674 | 0.0725 | | No log | 13.0 | 91 | 1.0493 | 0.805 | 0.2767 | 1.2512 | 0.805 | 0.7846 | 0.1451 | 0.0535 | | No log | 14.0 | 98 | 0.9657 | 0.815 | 0.2802 | 1.1796 | 0.815 | 0.7965 | 0.1680 | 0.0487 | | No log | 15.0 | 105 | 0.9910 | 0.82 | 0.2695 | 1.3658 | 0.82 | 0.8000 | 0.1400 | 0.0475 | | No log | 16.0 | 112 | 0.9828 | 0.81 | 0.2823 | 1.3175 | 0.81 | 0.7974 | 0.1390 | 0.0549 | | No log | 17.0 | 119 | 0.9279 | 0.8 | 0.2815 | 1.3727 | 0.8000 | 0.7882 | 0.1599 | 0.0454 | | No log | 18.0 | 126 | 1.0076 | 0.805 | 0.2929 | 1.2999 | 0.805 | 0.7825 | 0.1480 | 0.0562 | | No log | 19.0 | 133 | 0.9524 | 0.82 | 0.2705 | 1.3029 | 0.82 | 0.8122 | 0.1481 | 0.0454 | | No log | 20.0 | 140 | 1.0584 | 0.795 | 0.3010 | 1.3019 | 0.795 | 0.7699 | 0.1669 | 0.0650 | | No log | 21.0 | 147 | 0.9390 | 0.805 | 0.2775 | 1.4073 | 0.805 | 0.7888 | 0.1211 | 0.0513 | | No log | 22.0 | 154 | 0.9857 | 0.81 | 0.2895 | 1.2894 | 0.81 | 0.7879 | 0.1469 | 0.0548 | | No log | 23.0 | 161 | 0.9137 | 0.795 | 0.2809 | 1.4461 | 0.795 | 0.7872 | 0.1528 | 0.0472 | | No log | 24.0 | 168 | 0.8545 | 0.815 | 0.2844 | 1.2582 | 0.815 | 0.7981 | 0.1466 | 0.0484 | | No log | 25.0 | 175 | 0.8860 | 0.81 | 0.2766 | 1.4525 | 0.81 | 0.8010 | 0.1241 | 0.0457 | | No log | 26.0 | 182 | 0.8624 | 0.83 | 0.2813 | 1.1993 | 0.83 | 0.8222 | 0.1536 | 0.0512 | | No log | 27.0 | 189 | 0.9119 | 0.805 | 0.2894 | 1.4164 | 0.805 | 0.7869 | 0.1576 | 0.0519 | | No log | 28.0 | 196 | 0.9072 | 0.82 | 0.2753 | 1.2927 | 0.82 | 0.8149 | 0.1292 | 0.0514 | | No log | 29.0 | 203 | 0.8428 | 0.8 | 0.2805 | 1.3065 | 0.8000 | 0.7820 | 0.1368 | 0.0502 | | No log | 30.0 | 210 | 0.8696 | 0.81 | 0.2858 | 1.2825 | 0.81 | 0.7989 | 0.1454 | 0.0524 | | No log | 31.0 | 217 | 0.8542 | 0.8 | 0.2861 | 1.2029 | 0.8000 | 0.7766 | 0.1412 | 0.0496 | | No log | 32.0 | 224 | 0.8576 | 0.805 | 0.2896 | 1.3371 | 0.805 | 0.7814 | 0.1513 | 0.0515 | | No log | 33.0 | 231 | 0.8615 | 0.8 | 0.2859 | 1.2347 | 0.8000 | 0.7826 | 0.1473 | 0.0522 | | No log | 34.0 | 238 | 0.8474 | 0.805 | 0.2807 | 1.3510 | 0.805 | 0.7946 | 0.1493 | 0.0524 | | No log | 35.0 | 245 | 0.9058 | 0.79 | 0.3035 | 1.2005 | 0.79 | 0.7768 | 0.1497 | 0.0553 | | No log | 36.0 | 252 | 0.8461 | 0.805 | 0.2897 | 1.2770 | 0.805 | 0.7906 | 0.1599 | 0.0513 | | No log | 37.0 | 259 | 0.8461 | 0.805 | 0.2962 | 1.1989 | 0.805 | 0.7912 | 0.1527 | 0.0533 | | No log | 38.0 | 266 | 0.8646 | 0.815 | 0.2817 | 1.3653 | 0.815 | 0.8031 | 0.1355 | 0.0499 | | No log | 39.0 | 273 | 0.8306 | 0.8 | 0.2905 | 1.1852 | 0.8000 | 0.7862 | 0.1528 | 0.0549 | | No log | 40.0 | 280 | 0.8561 | 0.815 | 0.2838 | 1.2577 | 0.815 | 0.8005 | 0.1431 | 0.0544 | | No log | 41.0 | 287 | 0.8236 | 0.805 | 0.2836 | 1.2093 | 0.805 | 0.7925 | 0.1376 | 0.0490 | | No log | 42.0 | 294 | 0.8221 | 0.805 | 0.2853 | 1.1929 | 0.805 | 0.7805 | 0.1397 | 0.0524 | | No log | 43.0 | 301 | 0.7834 | 0.815 | 0.2666 | 1.2720 | 0.815 | 0.8006 | 0.1316 | 0.0496 | | No log | 44.0 | 308 | 0.8022 | 0.8 | 0.2839 | 1.2009 | 0.8000 | 0.7870 | 0.1457 | 0.0514 | | No log | 45.0 | 315 | 0.8009 | 0.81 | 0.2735 | 1.3505 | 0.81 | 0.7970 | 0.1359 | 0.0494 | | No log | 46.0 | 322 | 0.8029 | 0.81 | 0.2775 | 1.1956 | 0.81 | 0.7983 | 0.1476 | 0.0509 | | No log | 47.0 | 329 | 0.7979 | 0.82 | 0.2818 | 1.2005 | 0.82 | 0.8049 | 0.1466 | 0.0488 | | No log | 48.0 | 336 | 0.7763 | 0.815 | 0.2784 | 1.1905 | 0.815 | 0.7970 | 0.1358 | 0.0512 | | No log | 49.0 | 343 | 0.7917 | 0.81 | 0.2802 | 1.2136 | 0.81 | 0.7989 | 0.1429 | 0.0486 | | No log | 50.0 | 350 | 0.8223 | 0.825 | 0.2809 | 1.1860 | 0.825 | 0.8042 | 0.1567 | 0.0520 | | No log | 51.0 | 357 | 0.7952 | 0.82 | 0.2747 | 1.2074 | 0.82 | 0.8078 | 0.1377 | 0.0484 | | No log | 52.0 | 364 | 0.7868 | 0.825 | 0.2714 | 1.2850 | 0.825 | 0.8170 | 0.1371 | 0.0476 | | No log | 53.0 | 371 | 0.8111 | 0.805 | 0.2869 | 1.1892 | 0.805 | 0.7954 | 0.1467 | 0.0524 | | No log | 54.0 | 378 | 0.7739 | 0.81 | 0.2755 | 1.1946 | 0.81 | 0.7953 | 0.1567 | 0.0486 | | No log | 55.0 | 385 | 0.7930 | 0.825 | 0.2825 | 1.2000 | 0.825 | 0.8087 | 0.1546 | 0.0518 | | No log | 56.0 | 392 | 0.7826 | 0.815 | 0.2789 | 1.1953 | 0.815 | 0.8031 | 0.1353 | 0.0514 | | No log | 57.0 | 399 | 0.7716 | 0.82 | 0.2714 | 1.3115 | 0.82 | 0.8079 | 0.1207 | 0.0470 | | No log | 58.0 | 406 | 0.8036 | 0.815 | 0.2878 | 1.1875 | 0.815 | 0.7945 | 0.1469 | 0.0531 | | No log | 59.0 | 413 | 0.7714 | 0.82 | 0.2722 | 1.2787 | 0.82 | 0.8128 | 0.1264 | 0.0467 | | No log | 60.0 | 420 | 0.7671 | 0.825 | 0.2720 | 1.2722 | 0.825 | 0.8136 | 0.1378 | 0.0476 | | No log | 61.0 | 427 | 0.7885 | 0.815 | 0.2834 | 1.1798 | 0.815 | 0.8007 | 0.1480 | 0.0526 | | No log | 62.0 | 434 | 0.7621 | 0.82 | 0.2706 | 1.3459 | 0.82 | 0.8102 | 0.1156 | 0.0482 | | No log | 63.0 | 441 | 0.7691 | 0.81 | 0.2797 | 1.1379 | 0.81 | 0.7959 | 0.1429 | 0.0506 | | No log | 64.0 | 448 | 0.7699 | 0.81 | 0.2776 | 1.1964 | 0.81 | 0.7974 | 0.1473 | 0.0494 | | No log | 65.0 | 455 | 0.7693 | 0.82 | 0.2739 | 1.2089 | 0.82 | 0.8106 | 0.1390 | 0.0481 | | No log | 66.0 | 462 | 0.7891 | 0.81 | 0.2805 | 1.1989 | 0.81 | 0.7927 | 0.1530 | 0.0513 | | No log | 67.0 | 469 | 0.7806 | 0.82 | 0.2798 | 1.2033 | 0.82 | 0.8068 | 0.1408 | 0.0485 | | No log | 68.0 | 476 | 0.7877 | 0.82 | 0.2815 | 1.1896 | 0.82 | 0.8054 | 0.1376 | 0.0501 | | No log | 69.0 | 483 | 0.7649 | 0.825 | 0.2731 | 1.1567 | 0.825 | 0.8155 | 0.1371 | 0.0479 | | No log | 70.0 | 490 | 0.7740 | 0.82 | 0.2764 | 1.1929 | 0.82 | 0.8107 | 0.1250 | 0.0511 | | No log | 71.0 | 497 | 0.7657 | 0.82 | 0.2744 | 1.2762 | 0.82 | 0.8068 | 0.1374 | 0.0488 | | 0.4804 | 72.0 | 504 | 0.7887 | 0.805 | 0.2839 | 1.1851 | 0.805 | 0.7914 | 0.1524 | 0.0513 | | 0.4804 | 73.0 | 511 | 0.7662 | 0.815 | 0.2759 | 1.1973 | 0.815 | 0.8010 | 0.1395 | 0.0496 | | 0.4804 | 74.0 | 518 | 0.7706 | 0.825 | 0.2742 | 1.2020 | 0.825 | 0.8196 | 0.1398 | 0.0492 | | 0.4804 | 75.0 | 525 | 0.7780 | 0.815 | 0.2802 | 1.1881 | 0.815 | 0.7970 | 0.1392 | 0.0505 | | 0.4804 | 76.0 | 532 | 0.7731 | 0.825 | 0.2745 | 1.2695 | 0.825 | 0.8152 | 0.1548 | 0.0485 | | 0.4804 | 77.0 | 539 | 0.7743 | 0.825 | 0.2762 | 1.2039 | 0.825 | 0.8109 | 0.1326 | 0.0490 | | 0.4804 | 78.0 | 546 | 0.7782 | 0.805 | 0.2792 | 1.2001 | 0.805 | 0.7905 | 0.1381 | 0.0506 | | 0.4804 | 79.0 | 553 | 0.7786 | 0.81 | 0.2807 | 1.1929 | 0.81 | 0.7980 | 0.1394 | 0.0505 | | 0.4804 | 80.0 | 560 | 0.7759 | 0.82 | 0.2772 | 1.1973 | 0.82 | 0.8081 | 0.1296 | 0.0494 | | 0.4804 | 81.0 | 567 | 0.7703 | 0.82 | 0.2758 | 1.2069 | 0.82 | 0.8096 | 0.1405 | 0.0491 | | 0.4804 | 82.0 | 574 | 0.7749 | 0.81 | 0.2777 | 1.1996 | 0.81 | 0.7980 | 0.1502 | 0.0501 | | 0.4804 | 83.0 | 581 | 0.7768 | 0.815 | 0.2777 | 1.2009 | 0.815 | 0.8052 | 0.1237 | 0.0496 | | 0.4804 | 84.0 | 588 | 0.7761 | 0.815 | 0.2778 | 1.1986 | 0.815 | 0.8008 | 0.1333 | 0.0495 | | 0.4804 | 85.0 | 595 | 0.7771 | 0.815 | 0.2780 | 1.1984 | 0.815 | 0.8008 | 0.1335 | 0.0497 | | 0.4804 | 86.0 | 602 | 0.7755 | 0.81 | 0.2777 | 1.1987 | 0.81 | 0.7980 | 0.1327 | 0.0501 | | 0.4804 | 87.0 | 609 | 0.7749 | 0.81 | 0.2776 | 1.1974 | 0.81 | 0.7980 | 0.1261 | 0.0499 | | 0.4804 | 88.0 | 616 | 0.7746 | 0.815 | 0.2776 | 1.1981 | 0.815 | 0.8052 | 0.1238 | 0.0497 | | 0.4804 | 89.0 | 623 | 0.7744 | 0.81 | 0.2776 | 1.1981 | 0.81 | 0.7980 | 0.1283 | 0.0500 | | 0.4804 | 90.0 | 630 | 0.7743 | 0.81 | 0.2774 | 1.1987 | 0.81 | 0.7980 | 0.1346 | 0.0499 | | 0.4804 | 91.0 | 637 | 0.7741 | 0.81 | 0.2774 | 1.1981 | 0.81 | 0.7980 | 0.1379 | 0.0499 | | 0.4804 | 92.0 | 644 | 0.7742 | 0.81 | 0.2774 | 1.1982 | 0.81 | 0.7980 | 0.1403 | 0.0499 | | 0.4804 | 93.0 | 651 | 0.7745 | 0.81 | 0.2775 | 1.1982 | 0.81 | 0.7980 | 0.1403 | 0.0500 | | 0.4804 | 94.0 | 658 | 0.7746 | 0.81 | 0.2776 | 1.1978 | 0.81 | 0.7980 | 0.1316 | 0.0500 | | 0.4804 | 95.0 | 665 | 0.7745 | 0.81 | 0.2775 | 1.1982 | 0.81 | 0.7980 | 0.1380 | 0.0499 | | 0.4804 | 96.0 | 672 | 0.7746 | 0.81 | 0.2775 | 1.1981 | 0.81 | 0.7980 | 0.1315 | 0.0500 | | 0.4804 | 97.0 | 679 | 0.7746 | 0.81 | 0.2775 | 1.1981 | 0.81 | 0.7980 | 0.1403 | 0.0500 | | 0.4804 | 98.0 | 686 | 0.7746 | 0.81 | 0.2775 | 1.1981 | 0.81 | 0.7980 | 0.1403 | 0.0500 | | 0.4804 | 99.0 | 693 | 0.7746 | 0.81 | 0.2775 | 1.1981 | 0.81 | 0.7980 | 0.1403 | 0.0500 | | 0.4804 | 100.0 | 700 | 0.7746 | 0.81 | 0.2775 | 1.1981 | 0.81 | 0.7980 | 0.1403 | 0.0500 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_small_sgd_001_fold4

# hushem_5x_deit_small_sgd_001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0586 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.487 | 1.0 | 28 | 1.4172 | 0.3095 | | 1.4655 | 2.0 | 56 | 1.3829 | 0.3333 | | 1.3519 | 3.0 | 84 | 1.3645 | 0.2619 | | 1.3235 | 4.0 | 112 | 1.3524 | 0.2619 | | 1.3019 | 5.0 | 140 | 1.3421 | 0.2857 | | 1.2683 | 6.0 | 168 | 1.3287 | 0.2857 | | 1.2448 | 7.0 | 196 | 1.3147 | 0.2857 | | 1.2154 | 8.0 | 224 | 1.3011 | 0.2619 | | 1.1886 | 9.0 | 252 | 1.2876 | 0.3571 | | 1.1547 | 10.0 | 280 | 1.2739 | 0.3810 | | 1.1374 | 11.0 | 308 | 1.2618 | 0.3810 | | 1.1111 | 12.0 | 336 | 1.2488 | 0.3810 | | 1.1298 | 13.0 | 364 | 1.2398 | 0.4048 | | 1.0797 | 14.0 | 392 | 1.2302 | 0.4048 | | 1.0414 | 15.0 | 420 | 1.2217 | 0.4286 | | 1.061 | 16.0 | 448 | 1.2120 | 0.4286 | | 1.0634 | 17.0 | 476 | 1.2016 | 0.4524 | | 1.0054 | 18.0 | 504 | 1.1928 | 0.4524 | | 0.9762 | 19.0 | 532 | 1.1844 | 0.4524 | | 1.0106 | 20.0 | 560 | 1.1764 | 0.4524 | | 0.9235 | 21.0 | 588 | 1.1685 | 0.4524 | | 0.9458 | 22.0 | 616 | 1.1599 | 0.4762 | | 0.9326 | 23.0 | 644 | 1.1543 | 0.5 | | 0.9222 | 24.0 | 672 | 1.1465 | 0.5 | | 0.8846 | 25.0 | 700 | 1.1391 | 0.5 | | 0.8795 | 26.0 | 728 | 1.1307 | 0.4762 | | 0.8711 | 27.0 | 756 | 1.1242 | 0.5238 | | 0.8921 | 28.0 | 784 | 1.1184 | 0.5238 | | 0.8796 | 29.0 | 812 | 1.1133 | 0.5238 | | 0.8567 | 30.0 | 840 | 1.1054 | 0.5238 | | 0.8632 | 31.0 | 868 | 1.1011 | 0.5238 | | 0.8179 | 32.0 | 896 | 1.0965 | 0.5238 | | 0.8418 | 33.0 | 924 | 1.0917 | 0.5238 | | 0.8097 | 34.0 | 952 | 1.0866 | 0.5238 | | 0.8474 | 35.0 | 980 | 1.0818 | 0.5238 | | 0.7989 | 36.0 | 1008 | 1.0776 | 0.5238 | | 0.7935 | 37.0 | 1036 | 1.0750 | 0.5238 | | 0.8104 | 38.0 | 1064 | 1.0725 | 0.5238 | | 0.8018 | 39.0 | 1092 | 1.0698 | 0.5238 | | 0.797 | 40.0 | 1120 | 1.0673 | 0.5238 | | 0.8004 | 41.0 | 1148 | 1.0654 | 0.5238 | | 0.775 | 42.0 | 1176 | 1.0641 | 0.5 | | 0.7606 | 43.0 | 1204 | 1.0623 | 0.5 | | 0.7649 | 44.0 | 1232 | 1.0613 | 0.5 | | 0.7627 | 45.0 | 1260 | 1.0601 | 0.5 | | 0.7807 | 46.0 | 1288 | 1.0595 | 0.5 | | 0.7697 | 47.0 | 1316 | 1.0588 | 0.5 | | 0.7683 | 48.0 | 1344 | 1.0586 | 0.5 | | 0.783 | 49.0 | 1372 | 1.0586 | 0.5 | | 0.7862 | 50.0 | 1400 | 1.0586 | 0.5 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_small_sgd_001_fold5

# hushem_5x_deit_small_sgd_001_fold5 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0219 - Accuracy: 0.5854 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5389 | 1.0 | 28 | 1.4343 | 0.2439 | | 1.3921 | 2.0 | 56 | 1.3847 | 0.2683 | | 1.3749 | 3.0 | 84 | 1.3585 | 0.3659 | | 1.3126 | 4.0 | 112 | 1.3409 | 0.3659 | | 1.3117 | 5.0 | 140 | 1.3251 | 0.3659 | | 1.3121 | 6.0 | 168 | 1.3119 | 0.3415 | | 1.2628 | 7.0 | 196 | 1.2980 | 0.3415 | | 1.2308 | 8.0 | 224 | 1.2843 | 0.3659 | | 1.2428 | 9.0 | 252 | 1.2711 | 0.4146 | | 1.1961 | 10.0 | 280 | 1.2591 | 0.4146 | | 1.1795 | 11.0 | 308 | 1.2486 | 0.3902 | | 1.1594 | 12.0 | 336 | 1.2381 | 0.3902 | | 1.1371 | 13.0 | 364 | 1.2260 | 0.3902 | | 1.1217 | 14.0 | 392 | 1.2140 | 0.4390 | | 1.0975 | 15.0 | 420 | 1.2018 | 0.4634 | | 1.1139 | 16.0 | 448 | 1.1910 | 0.4878 | | 1.0797 | 17.0 | 476 | 1.1802 | 0.4878 | | 1.0813 | 18.0 | 504 | 1.1682 | 0.4878 | | 1.0619 | 19.0 | 532 | 1.1572 | 0.4878 | | 1.0398 | 20.0 | 560 | 1.1467 | 0.5122 | | 1.0215 | 21.0 | 588 | 1.1362 | 0.5122 | | 1.0014 | 22.0 | 616 | 1.1280 | 0.5366 | | 1.0047 | 23.0 | 644 | 1.1216 | 0.5610 | | 0.9823 | 24.0 | 672 | 1.1144 | 0.5610 | | 0.9814 | 25.0 | 700 | 1.1058 | 0.5610 | | 0.9822 | 26.0 | 728 | 1.0976 | 0.5610 | | 0.9448 | 27.0 | 756 | 1.0916 | 0.5366 | | 0.9805 | 28.0 | 784 | 1.0839 | 0.5366 | | 0.9187 | 29.0 | 812 | 1.0780 | 0.5366 | | 0.9659 | 30.0 | 840 | 1.0725 | 0.5366 | | 0.9135 | 31.0 | 868 | 1.0663 | 0.5610 | | 0.889 | 32.0 | 896 | 1.0628 | 0.5610 | | 0.9089 | 33.0 | 924 | 1.0587 | 0.5610 | | 0.9062 | 34.0 | 952 | 1.0524 | 0.5610 | | 0.9029 | 35.0 | 980 | 1.0479 | 0.5610 | | 0.8924 | 36.0 | 1008 | 1.0439 | 0.5854 | | 0.8694 | 37.0 | 1036 | 1.0402 | 0.5854 | | 0.8578 | 38.0 | 1064 | 1.0365 | 0.5610 | | 0.8992 | 39.0 | 1092 | 1.0340 | 0.5854 | | 0.8586 | 40.0 | 1120 | 1.0317 | 0.5854 | | 0.8737 | 41.0 | 1148 | 1.0296 | 0.5854 | | 0.8517 | 42.0 | 1176 | 1.0278 | 0.5854 | | 0.8537 | 43.0 | 1204 | 1.0257 | 0.5854 | | 0.8642 | 44.0 | 1232 | 1.0243 | 0.5854 | | 0.871 | 45.0 | 1260 | 1.0234 | 0.5854 | | 0.8594 | 46.0 | 1288 | 1.0226 | 0.5854 | | 0.8675 | 47.0 | 1316 | 1.0221 | 0.5854 | | 0.874 | 48.0 | 1344 | 1.0219 | 0.5854 | | 0.8459 | 49.0 | 1372 | 1.0219 | 0.5854 | | 0.8538 | 50.0 | 1400 | 1.0219 | 0.5854 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

piecurus/convnext-tiny-224-finetuned-eurosat-albumentations

# convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2257 - Accuracy: 0.9622 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2446 | 1.0 | 190 | 0.2257 | 0.9622 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

hkivancoral/hushem_5x_deit_base_adamax_0001_fold1

# hushem_5x_deit_base_adamax_0001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 - Accuracy: 0.7111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.238 | 1.0 | 27 | 1.3398 | 0.2667 | | 0.9269 | 2.0 | 54 | 1.2685 | 0.4444 | | 0.6591 | 3.0 | 81 | 1.1740 | 0.5333 | | 0.5112 | 4.0 | 108 | 1.1289 | 0.5556 | | 0.3169 | 5.0 | 135 | 1.0720 | 0.5778 | | 0.2415 | 6.0 | 162 | 0.9458 | 0.6 | | 0.1769 | 7.0 | 189 | 0.9250 | 0.6 | | 0.0983 | 8.0 | 216 | 0.8893 | 0.6667 | | 0.0567 | 9.0 | 243 | 0.8959 | 0.7111 | | 0.0295 | 10.0 | 270 | 1.0130 | 0.5778 | | 0.0202 | 11.0 | 297 | 0.9509 | 0.6889 | | 0.0113 | 12.0 | 324 | 0.9586 | 0.7111 | | 0.0094 | 13.0 | 351 | 0.9844 | 0.6889 | | 0.0072 | 14.0 | 378 | 0.9965 | 0.7333 | | 0.0063 | 15.0 | 405 | 1.0087 | 0.7111 | | 0.005 | 16.0 | 432 | 1.0089 | 0.6889 | | 0.0041 | 17.0 | 459 | 1.0347 | 0.6889 | | 0.004 | 18.0 | 486 | 1.0569 | 0.6889 | | 0.0034 | 19.0 | 513 | 1.0522 | 0.6889 | | 0.0031 | 20.0 | 540 | 1.0681 | 0.6889 | | 0.0027 | 21.0 | 567 | 1.0686 | 0.6889 | | 0.0026 | 22.0 | 594 | 1.0745 | 0.6889 | | 0.0023 | 23.0 | 621 | 1.0948 | 0.6889 | | 0.0022 | 24.0 | 648 | 1.0979 | 0.6889 | | 0.0021 | 25.0 | 675 | 1.0958 | 0.6889 | | 0.0021 | 26.0 | 702 | 1.1008 | 0.6889 | | 0.0018 | 27.0 | 729 | 1.1079 | 0.6889 | | 0.0017 | 28.0 | 756 | 1.1114 | 0.6889 | | 0.0019 | 29.0 | 783 | 1.1187 | 0.6889 | | 0.0017 | 30.0 | 810 | 1.1246 | 0.6889 | | 0.0016 | 31.0 | 837 | 1.1229 | 0.6889 | | 0.0016 | 32.0 | 864 | 1.1290 | 0.6889 | | 0.0014 | 33.0 | 891 | 1.1312 | 0.6889 | | 0.0015 | 34.0 | 918 | 1.1349 | 0.6889 | | 0.0014 | 35.0 | 945 | 1.1402 | 0.6889 | | 0.0013 | 36.0 | 972 | 1.1442 | 0.6889 | | 0.0013 | 37.0 | 999 | 1.1434 | 0.6889 | | 0.0012 | 38.0 | 1026 | 1.1425 | 0.7111 | | 0.0012 | 39.0 | 1053 | 1.1512 | 0.6889 | | 0.0011 | 40.0 | 1080 | 1.1497 | 0.6889 | | 0.0012 | 41.0 | 1107 | 1.1525 | 0.6889 | | 0.0012 | 42.0 | 1134 | 1.1548 | 0.6889 | | 0.0012 | 43.0 | 1161 | 1.1577 | 0.6889 | | 0.0011 | 44.0 | 1188 | 1.1573 | 0.6889 | | 0.0011 | 45.0 | 1215 | 1.1575 | 0.6889 | | 0.0011 | 46.0 | 1242 | 1.1575 | 0.7111 | | 0.0011 | 47.0 | 1269 | 1.1575 | 0.7111 | | 0.0011 | 48.0 | 1296 | 1.1576 | 0.7111 | | 0.0011 | 49.0 | 1323 | 1.1576 | 0.7111 | | 0.0011 | 50.0 | 1350 | 1.1576 | 0.7111 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_adamax_0001_fold2

# hushem_5x_deit_base_adamax_0001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3720 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2573 | 1.0 | 27 | 1.3177 | 0.3778 | | 0.9474 | 2.0 | 54 | 1.2698 | 0.4667 | | 0.6743 | 3.0 | 81 | 1.1709 | 0.5333 | | 0.53 | 4.0 | 108 | 1.1238 | 0.6 | | 0.3327 | 5.0 | 135 | 1.1060 | 0.6 | | 0.2187 | 6.0 | 162 | 1.0991 | 0.6444 | | 0.1497 | 7.0 | 189 | 1.1072 | 0.6444 | | 0.086 | 8.0 | 216 | 1.1220 | 0.6444 | | 0.0449 | 9.0 | 243 | 1.1215 | 0.6444 | | 0.0257 | 10.0 | 270 | 1.1368 | 0.6667 | | 0.0174 | 11.0 | 297 | 1.1587 | 0.6667 | | 0.0102 | 12.0 | 324 | 1.1715 | 0.6889 | | 0.0083 | 13.0 | 351 | 1.2117 | 0.6889 | | 0.0067 | 14.0 | 378 | 1.2042 | 0.6889 | | 0.0061 | 15.0 | 405 | 1.2320 | 0.6889 | | 0.0048 | 16.0 | 432 | 1.2396 | 0.6889 | | 0.0043 | 17.0 | 459 | 1.2501 | 0.6889 | | 0.0039 | 18.0 | 486 | 1.2585 | 0.6667 | | 0.0034 | 19.0 | 513 | 1.2714 | 0.6889 | | 0.0031 | 20.0 | 540 | 1.2786 | 0.6889 | | 0.0029 | 21.0 | 567 | 1.2831 | 0.6667 | | 0.0026 | 22.0 | 594 | 1.2886 | 0.6667 | | 0.0022 | 23.0 | 621 | 1.2985 | 0.6667 | | 0.0022 | 24.0 | 648 | 1.3036 | 0.6667 | | 0.002 | 25.0 | 675 | 1.3071 | 0.6667 | | 0.002 | 26.0 | 702 | 1.3150 | 0.6667 | | 0.0017 | 27.0 | 729 | 1.3222 | 0.6667 | | 0.0018 | 28.0 | 756 | 1.3235 | 0.6667 | | 0.0018 | 29.0 | 783 | 1.3294 | 0.6667 | | 0.0017 | 30.0 | 810 | 1.3351 | 0.6667 | | 0.0015 | 31.0 | 837 | 1.3358 | 0.6667 | | 0.0016 | 32.0 | 864 | 1.3406 | 0.6667 | | 0.0015 | 33.0 | 891 | 1.3434 | 0.6667 | | 0.0014 | 34.0 | 918 | 1.3481 | 0.6667 | | 0.0013 | 35.0 | 945 | 1.3523 | 0.6667 | | 0.0013 | 36.0 | 972 | 1.3535 | 0.6667 | | 0.0013 | 37.0 | 999 | 1.3558 | 0.6667 | | 0.0012 | 38.0 | 1026 | 1.3590 | 0.6667 | | 0.0012 | 39.0 | 1053 | 1.3619 | 0.6667 | | 0.0011 | 40.0 | 1080 | 1.3634 | 0.6667 | | 0.0012 | 41.0 | 1107 | 1.3657 | 0.6667 | | 0.0011 | 42.0 | 1134 | 1.3669 | 0.6667 | | 0.0011 | 43.0 | 1161 | 1.3696 | 0.6667 | | 0.0011 | 44.0 | 1188 | 1.3699 | 0.6667 | | 0.0011 | 45.0 | 1215 | 1.3707 | 0.6667 | | 0.0011 | 46.0 | 1242 | 1.3712 | 0.6667 | | 0.0011 | 47.0 | 1269 | 1.3718 | 0.6667 | | 0.0011 | 48.0 | 1296 | 1.3720 | 0.6667 | | 0.0011 | 49.0 | 1323 | 1.3720 | 0.6667 | | 0.0011 | 50.0 | 1350 | 1.3720 | 0.6667 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-small_tobacco3482_kd_NKD_t1.0_g1.5

# dit-base_tobacco-small_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1084 - Accuracy: 0.825 - Brier Loss: 0.2907 - Nll: 1.2013 - F1 Micro: 0.825 - F1 Macro: 0.8171 - Ece: 0.1500 - Aurc: 0.0459 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 5.5631 | 0.135 | 0.9164 | 5.3726 | 0.135 | 0.1126 | 0.2543 | 0.8397 | | No log | 2.0 | 14 | 4.9048 | 0.35 | 0.8238 | 3.0911 | 0.35 | 0.2637 | 0.3348 | 0.6709 | | No log | 3.0 | 21 | 4.1439 | 0.49 | 0.6650 | 1.8580 | 0.49 | 0.4532 | 0.2990 | 0.2902 | | No log | 4.0 | 28 | 3.5518 | 0.66 | 0.4867 | 1.6397 | 0.66 | 0.6303 | 0.2902 | 0.1489 | | No log | 5.0 | 35 | 3.3371 | 0.755 | 0.3981 | 1.6213 | 0.755 | 0.7261 | 0.2670 | 0.0984 | | No log | 6.0 | 42 | 3.4978 | 0.69 | 0.4211 | 1.5668 | 0.69 | 0.6792 | 0.2240 | 0.1170 | | No log | 7.0 | 49 | 3.0945 | 0.795 | 0.3094 | 1.5507 | 0.795 | 0.7653 | 0.1765 | 0.0622 | | No log | 8.0 | 56 | 3.0882 | 0.775 | 0.3056 | 1.5470 | 0.775 | 0.7500 | 0.1826 | 0.0634 | | No log | 9.0 | 63 | 3.1861 | 0.745 | 0.3331 | 1.6432 | 0.745 | 0.7362 | 0.1822 | 0.0754 | | No log | 10.0 | 70 | 2.9849 | 0.81 | 0.2789 | 1.5850 | 0.81 | 0.7802 | 0.1559 | 0.0548 | | No log | 11.0 | 77 | 3.0131 | 0.795 | 0.3012 | 1.4820 | 0.795 | 0.7720 | 0.1627 | 0.0567 | | No log | 12.0 | 84 | 2.9054 | 0.795 | 0.2734 | 1.4141 | 0.795 | 0.7843 | 0.1501 | 0.0535 | | No log | 13.0 | 91 | 2.9704 | 0.815 | 0.2720 | 1.4241 | 0.815 | 0.8144 | 0.1584 | 0.0536 | | No log | 14.0 | 98 | 2.9393 | 0.815 | 0.2627 | 1.4735 | 0.815 | 0.7902 | 0.1582 | 0.0504 | | No log | 15.0 | 105 | 3.0346 | 0.805 | 0.2963 | 1.3649 | 0.805 | 0.7973 | 0.1617 | 0.0564 | | No log | 16.0 | 112 | 2.9648 | 0.79 | 0.2839 | 1.6270 | 0.79 | 0.7722 | 0.1418 | 0.0525 | | No log | 17.0 | 119 | 3.0458 | 0.82 | 0.2960 | 1.3476 | 0.82 | 0.8048 | 0.1575 | 0.0622 | | No log | 18.0 | 126 | 2.8571 | 0.82 | 0.2754 | 1.3958 | 0.82 | 0.8081 | 0.1482 | 0.0493 | | No log | 19.0 | 133 | 2.9429 | 0.775 | 0.2971 | 1.4302 | 0.775 | 0.7617 | 0.1616 | 0.0575 | | No log | 20.0 | 140 | 2.8274 | 0.825 | 0.2698 | 1.3759 | 0.825 | 0.8081 | 0.1520 | 0.0449 | | No log | 21.0 | 147 | 2.8769 | 0.81 | 0.2713 | 1.3604 | 0.81 | 0.8086 | 0.1390 | 0.0466 | | No log | 22.0 | 154 | 2.8787 | 0.805 | 0.2694 | 1.3016 | 0.805 | 0.7975 | 0.1522 | 0.0435 | | No log | 23.0 | 161 | 2.8771 | 0.825 | 0.2646 | 1.4753 | 0.825 | 0.8215 | 0.1414 | 0.0485 | | No log | 24.0 | 168 | 2.8950 | 0.805 | 0.2774 | 1.2783 | 0.805 | 0.7754 | 0.1406 | 0.0495 | | No log | 25.0 | 175 | 2.9780 | 0.825 | 0.2829 | 1.3207 | 0.825 | 0.8332 | 0.1402 | 0.0496 | | No log | 26.0 | 182 | 2.8906 | 0.82 | 0.2653 | 1.3097 | 0.82 | 0.8007 | 0.1380 | 0.0454 | | No log | 27.0 | 189 | 2.9385 | 0.82 | 0.2778 | 1.3039 | 0.82 | 0.8211 | 0.1489 | 0.0469 | | No log | 28.0 | 196 | 2.8644 | 0.83 | 0.2618 | 1.4004 | 0.83 | 0.8325 | 0.1358 | 0.0494 | | No log | 29.0 | 203 | 2.8761 | 0.82 | 0.2720 | 1.2220 | 0.82 | 0.8192 | 0.1411 | 0.0463 | | No log | 30.0 | 210 | 2.8594 | 0.83 | 0.2620 | 1.3323 | 0.83 | 0.8130 | 0.1257 | 0.0448 | | No log | 31.0 | 217 | 2.8946 | 0.825 | 0.2658 | 1.3388 | 0.825 | 0.8236 | 0.1322 | 0.0427 | | No log | 32.0 | 224 | 2.8698 | 0.825 | 0.2712 | 1.3141 | 0.825 | 0.8107 | 0.1467 | 0.0473 | | No log | 33.0 | 231 | 2.8106 | 0.83 | 0.2563 | 1.3750 | 0.83 | 0.8178 | 0.1126 | 0.0422 | | No log | 34.0 | 238 | 2.9752 | 0.8 | 0.2881 | 1.3007 | 0.8000 | 0.7902 | 0.1522 | 0.0499 | | No log | 35.0 | 245 | 2.8919 | 0.815 | 0.2886 | 1.3057 | 0.815 | 0.8149 | 0.1472 | 0.0468 | | No log | 36.0 | 252 | 2.8863 | 0.81 | 0.2833 | 1.1973 | 0.81 | 0.8006 | 0.1453 | 0.0458 | | No log | 37.0 | 259 | 2.8283 | 0.845 | 0.2685 | 1.2743 | 0.845 | 0.8438 | 0.1481 | 0.0451 | | No log | 38.0 | 266 | 2.9174 | 0.815 | 0.2825 | 1.2658 | 0.815 | 0.7965 | 0.1408 | 0.0530 | | No log | 39.0 | 273 | 2.8837 | 0.82 | 0.2775 | 1.2946 | 0.82 | 0.8050 | 0.1440 | 0.0472 | | No log | 40.0 | 280 | 2.8585 | 0.835 | 0.2654 | 1.2830 | 0.835 | 0.8169 | 0.1450 | 0.0467 | | No log | 41.0 | 287 | 2.9323 | 0.82 | 0.2809 | 1.2833 | 0.82 | 0.8085 | 0.1342 | 0.0490 | | No log | 42.0 | 294 | 2.9525 | 0.82 | 0.2847 | 1.2331 | 0.82 | 0.8055 | 0.1352 | 0.0481 | | No log | 43.0 | 301 | 2.9005 | 0.83 | 0.2819 | 1.2643 | 0.83 | 0.8225 | 0.1548 | 0.0482 | | No log | 44.0 | 308 | 2.8388 | 0.83 | 0.2634 | 1.2662 | 0.83 | 0.8152 | 0.1286 | 0.0460 | | No log | 45.0 | 315 | 2.8962 | 0.82 | 0.2752 | 1.3291 | 0.82 | 0.8127 | 0.1442 | 0.0496 | | No log | 46.0 | 322 | 2.9479 | 0.815 | 0.2883 | 1.2433 | 0.815 | 0.7968 | 0.1540 | 0.0523 | | No log | 47.0 | 329 | 2.8795 | 0.825 | 0.2737 | 1.2477 | 0.825 | 0.8260 | 0.1295 | 0.0447 | | No log | 48.0 | 336 | 2.9872 | 0.815 | 0.2992 | 1.2556 | 0.815 | 0.8029 | 0.1379 | 0.0510 | | No log | 49.0 | 343 | 2.8156 | 0.84 | 0.2536 | 1.2715 | 0.8400 | 0.8263 | 0.1240 | 0.0422 | | No log | 50.0 | 350 | 2.9534 | 0.81 | 0.2924 | 1.3383 | 0.81 | 0.7937 | 0.1471 | 0.0478 | | No log | 51.0 | 357 | 2.8604 | 0.855 | 0.2549 | 1.2566 | 0.855 | 0.8547 | 0.1318 | 0.0411 | | No log | 52.0 | 364 | 2.9769 | 0.825 | 0.2828 | 1.2325 | 0.825 | 0.8160 | 0.1407 | 0.0480 | | No log | 53.0 | 371 | 2.8717 | 0.84 | 0.2635 | 1.2511 | 0.8400 | 0.8342 | 0.1254 | 0.0434 | | No log | 54.0 | 378 | 2.9313 | 0.825 | 0.2704 | 1.2676 | 0.825 | 0.8159 | 0.1310 | 0.0477 | | No log | 55.0 | 385 | 2.8552 | 0.82 | 0.2638 | 1.2417 | 0.82 | 0.8031 | 0.1490 | 0.0435 | | No log | 56.0 | 392 | 2.9680 | 0.845 | 0.2729 | 1.2530 | 0.845 | 0.8414 | 0.1349 | 0.0452 | | No log | 57.0 | 399 | 2.9440 | 0.83 | 0.2796 | 1.2344 | 0.83 | 0.8222 | 0.1367 | 0.0450 | | No log | 58.0 | 406 | 3.0577 | 0.815 | 0.2913 | 1.2232 | 0.815 | 0.8068 | 0.1447 | 0.0488 | | No log | 59.0 | 413 | 2.8861 | 0.835 | 0.2643 | 1.2618 | 0.835 | 0.8280 | 0.1354 | 0.0422 | | No log | 60.0 | 420 | 3.0007 | 0.825 | 0.2822 | 1.2352 | 0.825 | 0.8136 | 0.1342 | 0.0449 | | No log | 61.0 | 427 | 2.9368 | 0.835 | 0.2746 | 1.2437 | 0.835 | 0.8258 | 0.1402 | 0.0437 | | No log | 62.0 | 434 | 2.9202 | 0.835 | 0.2709 | 1.2281 | 0.835 | 0.8258 | 0.1435 | 0.0435 | | No log | 63.0 | 441 | 2.9720 | 0.835 | 0.2768 | 1.2129 | 0.835 | 0.8354 | 0.1444 | 0.0460 | | No log | 64.0 | 448 | 2.9993 | 0.835 | 0.2815 | 1.2250 | 0.835 | 0.8245 | 0.1526 | 0.0451 | | No log | 65.0 | 455 | 2.9628 | 0.83 | 0.2725 | 1.2477 | 0.83 | 0.8190 | 0.1405 | 0.0439 | | No log | 66.0 | 462 | 3.0418 | 0.825 | 0.2863 | 1.2244 | 0.825 | 0.8142 | 0.1447 | 0.0473 | | No log | 67.0 | 469 | 3.0196 | 0.83 | 0.2797 | 1.2317 | 0.83 | 0.8223 | 0.1450 | 0.0463 | | No log | 68.0 | 476 | 3.0227 | 0.835 | 0.2834 | 1.2362 | 0.835 | 0.8270 | 0.1416 | 0.0446 | | No log | 69.0 | 483 | 3.0343 | 0.835 | 0.2837 | 1.2377 | 0.835 | 0.8310 | 0.1423 | 0.0455 | | No log | 70.0 | 490 | 2.9982 | 0.835 | 0.2755 | 1.2247 | 0.835 | 0.8245 | 0.1306 | 0.0443 | | No log | 71.0 | 497 | 3.0230 | 0.825 | 0.2860 | 1.2302 | 0.825 | 0.8171 | 0.1376 | 0.0464 | | 2.5595 | 72.0 | 504 | 3.0254 | 0.83 | 0.2843 | 1.2190 | 0.83 | 0.8222 | 0.1386 | 0.0463 | | 2.5595 | 73.0 | 511 | 3.0295 | 0.825 | 0.2851 | 1.2206 | 0.825 | 0.8192 | 0.1417 | 0.0462 | | 2.5595 | 74.0 | 518 | 3.0381 | 0.83 | 0.2845 | 1.2130 | 0.83 | 0.8243 | 0.1423 | 0.0457 | | 2.5595 | 75.0 | 525 | 3.0258 | 0.825 | 0.2837 | 1.2210 | 0.825 | 0.8171 | 0.1431 | 0.0460 | | 2.5595 | 76.0 | 532 | 3.0694 | 0.825 | 0.2886 | 1.2091 | 0.825 | 0.8171 | 0.1533 | 0.0476 | | 2.5595 | 77.0 | 539 | 3.0924 | 0.825 | 0.2939 | 1.2130 | 0.825 | 0.8171 | 0.1515 | 0.0473 | | 2.5595 | 78.0 | 546 | 3.0956 | 0.82 | 0.2921 | 1.2081 | 0.82 | 0.8140 | 0.1539 | 0.0482 | | 2.5595 | 79.0 | 553 | 3.0859 | 0.825 | 0.2884 | 1.2109 | 0.825 | 0.8220 | 0.1480 | 0.0468 | | 2.5595 | 80.0 | 560 | 3.0740 | 0.825 | 0.2894 | 1.2081 | 0.825 | 0.8136 | 0.1399 | 0.0459 | | 2.5595 | 81.0 | 567 | 3.0776 | 0.825 | 0.2901 | 1.2066 | 0.825 | 0.8171 | 0.1502 | 0.0462 | | 2.5595 | 82.0 | 574 | 3.0736 | 0.83 | 0.2869 | 1.2100 | 0.83 | 0.8251 | 0.1405 | 0.0462 | | 2.5595 | 83.0 | 581 | 3.0943 | 0.825 | 0.2919 | 1.2065 | 0.825 | 0.8171 | 0.1503 | 0.0464 | | 2.5595 | 84.0 | 588 | 3.0857 | 0.825 | 0.2908 | 1.2080 | 0.825 | 0.8171 | 0.1456 | 0.0461 | | 2.5595 | 85.0 | 595 | 3.0874 | 0.825 | 0.2890 | 1.2063 | 0.825 | 0.8171 | 0.1457 | 0.0461 | | 2.5595 | 86.0 | 602 | 3.0863 | 0.825 | 0.2880 | 1.2069 | 0.825 | 0.8171 | 0.1453 | 0.0459 | | 2.5595 | 87.0 | 609 | 3.0844 | 0.825 | 0.2882 | 1.2059 | 0.825 | 0.8171 | 0.1457 | 0.0456 | | 2.5595 | 88.0 | 616 | 3.1011 | 0.825 | 0.2909 | 1.2034 | 0.825 | 0.8171 | 0.1557 | 0.0462 | | 2.5595 | 89.0 | 623 | 3.1033 | 0.825 | 0.2912 | 1.2033 | 0.825 | 0.8171 | 0.1528 | 0.0463 | | 2.5595 | 90.0 | 630 | 3.1004 | 0.825 | 0.2903 | 1.2029 | 0.825 | 0.8171 | 0.1541 | 0.0461 | | 2.5595 | 91.0 | 637 | 3.0998 | 0.825 | 0.2900 | 1.2033 | 0.825 | 0.8171 | 0.1499 | 0.0459 | | 2.5595 | 92.0 | 644 | 3.1039 | 0.825 | 0.2904 | 1.2023 | 0.825 | 0.8171 | 0.1535 | 0.0460 | | 2.5595 | 93.0 | 651 | 3.1058 | 0.825 | 0.2906 | 1.2020 | 0.825 | 0.8171 | 0.1498 | 0.0460 | | 2.5595 | 94.0 | 658 | 3.1057 | 0.825 | 0.2906 | 1.2022 | 0.825 | 0.8171 | 0.1504 | 0.0459 | | 2.5595 | 95.0 | 665 | 3.1066 | 0.825 | 0.2908 | 1.2018 | 0.825 | 0.8171 | 0.1509 | 0.0460 | | 2.5595 | 96.0 | 672 | 3.1069 | 0.825 | 0.2906 | 1.2018 | 0.825 | 0.8171 | 0.1506 | 0.0459 | | 2.5595 | 97.0 | 679 | 3.1079 | 0.825 | 0.2906 | 1.2013 | 0.825 | 0.8171 | 0.1497 | 0.0459 | | 2.5595 | 98.0 | 686 | 3.1085 | 0.825 | 0.2907 | 1.2013 | 0.825 | 0.8171 | 0.1500 | 0.0459 | | 2.5595 | 99.0 | 693 | 3.1083 | 0.825 | 0.2907 | 1.2013 | 0.825 | 0.8171 | 0.1499 | 0.0460 | | 2.5595 | 100.0 | 700 | 3.1084 | 0.825 | 0.2907 | 1.2013 | 0.825 | 0.8171 | 0.1500 | 0.0459 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_base_adamax_0001_fold3

# hushem_5x_deit_base_adamax_0001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4419 - Accuracy: 0.8372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3275 | 1.0 | 28 | 1.2372 | 0.5814 | | 1.0641 | 2.0 | 56 | 1.0484 | 0.6977 | | 0.7591 | 3.0 | 84 | 0.8760 | 0.7442 | | 0.5652 | 4.0 | 112 | 0.7360 | 0.8140 | | 0.3906 | 5.0 | 140 | 0.6489 | 0.8372 | | 0.3059 | 6.0 | 168 | 0.5954 | 0.8605 | | 0.1994 | 7.0 | 196 | 0.5269 | 0.8372 | | 0.134 | 8.0 | 224 | 0.5174 | 0.8605 | | 0.0783 | 9.0 | 252 | 0.4602 | 0.8605 | | 0.0454 | 10.0 | 280 | 0.4569 | 0.8372 | | 0.0318 | 11.0 | 308 | 0.4393 | 0.8837 | | 0.018 | 12.0 | 336 | 0.4222 | 0.8605 | | 0.0132 | 13.0 | 364 | 0.4453 | 0.8837 | | 0.0088 | 14.0 | 392 | 0.4098 | 0.8837 | | 0.0068 | 15.0 | 420 | 0.4226 | 0.8605 | | 0.0058 | 16.0 | 448 | 0.4268 | 0.8605 | | 0.0055 | 17.0 | 476 | 0.4132 | 0.8605 | | 0.0045 | 18.0 | 504 | 0.4342 | 0.8605 | | 0.004 | 19.0 | 532 | 0.4228 | 0.8605 | | 0.0033 | 20.0 | 560 | 0.4271 | 0.8372 | | 0.0033 | 21.0 | 588 | 0.4254 | 0.8372 | | 0.0029 | 22.0 | 616 | 0.4205 | 0.8372 | | 0.0027 | 23.0 | 644 | 0.4207 | 0.8372 | | 0.0024 | 24.0 | 672 | 0.4248 | 0.8605 | | 0.0022 | 25.0 | 700 | 0.4229 | 0.8372 | | 0.0021 | 26.0 | 728 | 0.4293 | 0.8372 | | 0.002 | 27.0 | 756 | 0.4267 | 0.8372 | | 0.002 | 28.0 | 784 | 0.4239 | 0.8605 | | 0.0018 | 29.0 | 812 | 0.4273 | 0.8372 | | 0.0018 | 30.0 | 840 | 0.4313 | 0.8372 | | 0.0016 | 31.0 | 868 | 0.4289 | 0.8372 | | 0.0016 | 32.0 | 896 | 0.4329 | 0.8372 | | 0.0016 | 33.0 | 924 | 0.4313 | 0.8372 | | 0.0014 | 34.0 | 952 | 0.4362 | 0.8372 | | 0.0016 | 35.0 | 980 | 0.4336 | 0.8372 | | 0.0014 | 36.0 | 1008 | 0.4353 | 0.8372 | | 0.0014 | 37.0 | 1036 | 0.4446 | 0.8372 | | 0.0013 | 38.0 | 1064 | 0.4482 | 0.8372 | | 0.0013 | 39.0 | 1092 | 0.4496 | 0.8372 | | 0.0012 | 40.0 | 1120 | 0.4442 | 0.8372 | | 0.0013 | 41.0 | 1148 | 0.4456 | 0.8372 | | 0.0013 | 42.0 | 1176 | 0.4450 | 0.8372 | | 0.0012 | 43.0 | 1204 | 0.4433 | 0.8372 | | 0.0012 | 44.0 | 1232 | 0.4424 | 0.8372 | | 0.0011 | 45.0 | 1260 | 0.4418 | 0.8372 | | 0.0011 | 46.0 | 1288 | 0.4417 | 0.8372 | | 0.0011 | 47.0 | 1316 | 0.4421 | 0.8372 | | 0.0011 | 48.0 | 1344 | 0.4419 | 0.8372 | | 0.0011 | 49.0 | 1372 | 0.4419 | 0.8372 | | 0.0011 | 50.0 | 1400 | 0.4419 | 0.8372 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_adamax_0001_fold4

# hushem_5x_deit_base_adamax_0001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7295 | 1.0 | 28 | 0.4220 | 0.8095 | | 0.0888 | 2.0 | 56 | 0.1615 | 0.9286 | | 0.0158 | 3.0 | 84 | 0.0867 | 0.9524 | | 0.0047 | 4.0 | 112 | 0.1052 | 0.9524 | | 0.001 | 5.0 | 140 | 0.1157 | 0.9524 | | 0.0007 | 6.0 | 168 | 0.0506 | 0.9762 | | 0.0004 | 7.0 | 196 | 0.0523 | 0.9762 | | 0.0003 | 8.0 | 224 | 0.0521 | 1.0 | | 0.0003 | 9.0 | 252 | 0.0486 | 1.0 | | 0.0002 | 10.0 | 280 | 0.0478 | 1.0 | | 0.0002 | 11.0 | 308 | 0.0461 | 1.0 | | 0.0002 | 12.0 | 336 | 0.0436 | 1.0 | | 0.0002 | 13.0 | 364 | 0.0422 | 1.0 | | 0.0002 | 14.0 | 392 | 0.0427 | 1.0 | | 0.0002 | 15.0 | 420 | 0.0410 | 1.0 | | 0.0001 | 16.0 | 448 | 0.0408 | 1.0 | | 0.0001 | 17.0 | 476 | 0.0398 | 1.0 | | 0.0001 | 18.0 | 504 | 0.0389 | 1.0 | | 0.0001 | 19.0 | 532 | 0.0389 | 1.0 | | 0.0001 | 20.0 | 560 | 0.0386 | 1.0 | | 0.0001 | 21.0 | 588 | 0.0377 | 1.0 | | 0.0001 | 22.0 | 616 | 0.0375 | 1.0 | | 0.0001 | 23.0 | 644 | 0.0368 | 1.0 | | 0.0001 | 24.0 | 672 | 0.0368 | 1.0 | | 0.0001 | 25.0 | 700 | 0.0364 | 1.0 | | 0.0001 | 26.0 | 728 | 0.0360 | 1.0 | | 0.0001 | 27.0 | 756 | 0.0354 | 1.0 | | 0.0001 | 28.0 | 784 | 0.0352 | 1.0 | | 0.0001 | 29.0 | 812 | 0.0345 | 1.0 | | 0.0001 | 30.0 | 840 | 0.0346 | 1.0 | | 0.0001 | 31.0 | 868 | 0.0344 | 1.0 | | 0.0001 | 32.0 | 896 | 0.0342 | 1.0 | | 0.0001 | 33.0 | 924 | 0.0343 | 1.0 | | 0.0001 | 34.0 | 952 | 0.0340 | 1.0 | | 0.0001 | 35.0 | 980 | 0.0336 | 1.0 | | 0.0001 | 36.0 | 1008 | 0.0333 | 1.0 | | 0.0001 | 37.0 | 1036 | 0.0331 | 1.0 | | 0.0001 | 38.0 | 1064 | 0.0333 | 1.0 | | 0.0001 | 39.0 | 1092 | 0.0331 | 1.0 | | 0.0001 | 40.0 | 1120 | 0.0332 | 1.0 | | 0.0001 | 41.0 | 1148 | 0.0332 | 1.0 | | 0.0001 | 42.0 | 1176 | 0.0331 | 1.0 | | 0.0001 | 43.0 | 1204 | 0.0330 | 1.0 | | 0.0001 | 44.0 | 1232 | 0.0331 | 1.0 | | 0.0001 | 45.0 | 1260 | 0.0330 | 1.0 | | 0.0001 | 46.0 | 1288 | 0.0331 | 1.0 | | 0.0001 | 47.0 | 1316 | 0.0332 | 1.0 | | 0.0001 | 48.0 | 1344 | 0.0332 | 1.0 | | 0.0001 | 49.0 | 1372 | 0.0332 | 1.0 | | 0.0001 | 50.0 | 1400 | 0.0332 | 1.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/dit-base_tobacco-small_tobacco3482_hint

# dit-base_tobacco-small_tobacco3482_hint This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9099 - Accuracy: 0.85 - Brier Loss: 0.2772 - Nll: 1.4757 - F1 Micro: 0.85 - F1 Macro: 0.8366 - Ece: 0.1392 - Aurc: 0.0460 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 3.2233 | 0.44 | 0.7001 | 2.8339 | 0.44 | 0.3067 | 0.2724 | 0.3661 | | No log | 2.0 | 100 | 2.3954 | 0.705 | 0.4016 | 1.5814 | 0.705 | 0.6657 | 0.2046 | 0.1093 | | No log | 3.0 | 150 | 2.1938 | 0.735 | 0.3560 | 1.5685 | 0.735 | 0.7026 | 0.1879 | 0.0858 | | No log | 4.0 | 200 | 2.0989 | 0.74 | 0.3533 | 1.5416 | 0.74 | 0.7058 | 0.2015 | 0.0896 | | No log | 5.0 | 250 | 2.0203 | 0.795 | 0.3169 | 1.5407 | 0.795 | 0.7861 | 0.1773 | 0.0919 | | No log | 6.0 | 300 | 2.1849 | 0.675 | 0.4531 | 1.6333 | 0.675 | 0.6701 | 0.2207 | 0.1166 | | No log | 7.0 | 350 | 2.2223 | 0.745 | 0.4113 | 1.4333 | 0.745 | 0.7293 | 0.2045 | 0.0980 | | No log | 8.0 | 400 | 2.1696 | 0.715 | 0.4221 | 1.6537 | 0.715 | 0.6723 | 0.2069 | 0.1040 | | No log | 9.0 | 450 | 2.4443 | 0.735 | 0.4291 | 1.5392 | 0.735 | 0.7458 | 0.2236 | 0.1323 | | 1.8536 | 10.0 | 500 | 2.0474 | 0.775 | 0.3649 | 1.6156 | 0.775 | 0.7528 | 0.1915 | 0.0844 | | 1.8536 | 11.0 | 550 | 2.0046 | 0.81 | 0.3170 | 1.6225 | 0.81 | 0.7920 | 0.1547 | 0.0639 | | 1.8536 | 12.0 | 600 | 2.4864 | 0.725 | 0.4602 | 1.5678 | 0.7250 | 0.7308 | 0.2415 | 0.1218 | | 1.8536 | 13.0 | 650 | 1.8413 | 0.83 | 0.2698 | 1.6361 | 0.83 | 0.8117 | 0.1349 | 0.0674 | | 1.8536 | 14.0 | 700 | 2.1304 | 0.815 | 0.3281 | 1.5685 | 0.815 | 0.7936 | 0.1715 | 0.0703 | | 1.8536 | 15.0 | 750 | 2.5075 | 0.71 | 0.4652 | 1.9297 | 0.7100 | 0.6877 | 0.2281 | 0.1099 | | 1.8536 | 16.0 | 800 | 2.4854 | 0.73 | 0.4462 | 1.5241 | 0.7300 | 0.7176 | 0.2282 | 0.1097 | | 1.8536 | 17.0 | 850 | 2.1252 | 0.805 | 0.3210 | 1.5685 | 0.805 | 0.7907 | 0.1650 | 0.0804 | | 1.8536 | 18.0 | 900 | 1.9249 | 0.86 | 0.2473 | 1.7031 | 0.8600 | 0.8689 | 0.1244 | 0.0528 | | 1.8536 | 19.0 | 950 | 2.0943 | 0.835 | 0.2840 | 1.4696 | 0.835 | 0.8267 | 0.1439 | 0.0652 | | 1.0941 | 20.0 | 1000 | 1.8548 | 0.845 | 0.2566 | 1.3059 | 0.845 | 0.8403 | 0.1333 | 0.0558 | | 1.0941 | 21.0 | 1050 | 2.1487 | 0.805 | 0.3362 | 1.4556 | 0.805 | 0.8051 | 0.1665 | 0.0764 | | 1.0941 | 22.0 | 1100 | 2.2147 | 0.81 | 0.3149 | 1.4884 | 0.81 | 0.8081 | 0.1710 | 0.0984 | | 1.0941 | 23.0 | 1150 | 2.1111 | 0.84 | 0.2898 | 1.5426 | 0.8400 | 0.8410 | 0.1489 | 0.0848 | | 1.0941 | 24.0 | 1200 | 2.2432 | 0.85 | 0.2884 | 1.7273 | 0.85 | 0.8482 | 0.1532 | 0.0765 | | 1.0941 | 25.0 | 1250 | 2.3105 | 0.75 | 0.4190 | 1.4648 | 0.75 | 0.7396 | 0.2177 | 0.1074 | | 1.0941 | 26.0 | 1300 | 2.0587 | 0.795 | 0.3444 | 1.6181 | 0.795 | 0.7960 | 0.1641 | 0.0799 | | 1.0941 | 27.0 | 1350 | 2.4465 | 0.8 | 0.3517 | 2.0076 | 0.8000 | 0.7770 | 0.1731 | 0.0849 | | 1.0941 | 28.0 | 1400 | 2.1351 | 0.825 | 0.3132 | 1.5650 | 0.825 | 0.8315 | 0.1631 | 0.0553 | | 1.0941 | 29.0 | 1450 | 1.9746 | 0.86 | 0.2451 | 1.5908 | 0.8600 | 0.8374 | 0.1267 | 0.0537 | | 0.9575 | 30.0 | 1500 | 2.0257 | 0.855 | 0.2737 | 1.6541 | 0.855 | 0.8121 | 0.1352 | 0.0480 | | 0.9575 | 31.0 | 1550 | 1.9631 | 0.84 | 0.3037 | 1.7341 | 0.8400 | 0.8201 | 0.1515 | 0.0423 | | 0.9575 | 32.0 | 1600 | 2.4215 | 0.785 | 0.3909 | 1.4042 | 0.785 | 0.7740 | 0.2018 | 0.0708 | | 0.9575 | 33.0 | 1650 | 2.2159 | 0.795 | 0.3492 | 1.7639 | 0.795 | 0.7716 | 0.1721 | 0.0537 | | 0.9575 | 34.0 | 1700 | 2.3363 | 0.82 | 0.3132 | 1.9858 | 0.82 | 0.7993 | 0.1610 | 0.0845 | | 0.9575 | 35.0 | 1750 | 2.2187 | 0.84 | 0.2884 | 1.5376 | 0.8400 | 0.8182 | 0.1523 | 0.0803 | | 0.9575 | 36.0 | 1800 | 2.3407 | 0.825 | 0.3206 | 1.8292 | 0.825 | 0.8028 | 0.1588 | 0.0719 | | 0.9575 | 37.0 | 1850 | 2.4302 | 0.815 | 0.3353 | 1.7611 | 0.815 | 0.8091 | 0.1654 | 0.0920 | | 0.9575 | 38.0 | 1900 | 2.3307 | 0.815 | 0.3269 | 1.8263 | 0.815 | 0.8043 | 0.1675 | 0.0876 | | 0.9575 | 39.0 | 1950 | 2.2905 | 0.825 | 0.3217 | 1.7612 | 0.825 | 0.8116 | 0.1639 | 0.0841 | | 0.8923 | 40.0 | 2000 | 2.2699 | 0.83 | 0.3225 | 1.7537 | 0.83 | 0.8186 | 0.1655 | 0.0792 | | 0.8923 | 41.0 | 2050 | 2.2327 | 0.83 | 0.3179 | 1.7534 | 0.83 | 0.8186 | 0.1559 | 0.0764 | | 0.8923 | 42.0 | 2100 | 2.2852 | 0.825 | 0.3230 | 1.6737 | 0.825 | 0.8150 | 0.1611 | 0.0760 | | 0.8923 | 43.0 | 2150 | 2.2597 | 0.825 | 0.3221 | 1.6727 | 0.825 | 0.8147 | 0.1610 | 0.0734 | | 0.8923 | 44.0 | 2200 | 2.2492 | 0.83 | 0.3176 | 1.6692 | 0.83 | 0.8169 | 0.1619 | 0.0720 | | 0.8923 | 45.0 | 2250 | 2.2208 | 0.825 | 0.3182 | 1.6737 | 0.825 | 0.8124 | 0.1627 | 0.0707 | | 0.8923 | 46.0 | 2300 | 2.2192 | 0.825 | 0.3209 | 1.6771 | 0.825 | 0.8121 | 0.1650 | 0.0712 | | 0.8923 | 47.0 | 2350 | 2.2127 | 0.825 | 0.3198 | 1.6187 | 0.825 | 0.8124 | 0.1636 | 0.0684 | | 0.8923 | 48.0 | 2400 | 2.2079 | 0.825 | 0.3208 | 1.6760 | 0.825 | 0.8121 | 0.1632 | 0.0707 | | 0.8923 | 49.0 | 2450 | 2.1995 | 0.825 | 0.3187 | 1.5377 | 0.825 | 0.8124 | 0.1656 | 0.0702 | | 0.8511 | 50.0 | 2500 | 2.1877 | 0.825 | 0.3158 | 1.6098 | 0.825 | 0.8124 | 0.1600 | 0.0690 | | 0.8511 | 51.0 | 2550 | 2.1698 | 0.825 | 0.3167 | 1.5353 | 0.825 | 0.8124 | 0.1607 | 0.0695 | | 0.8511 | 52.0 | 2600 | 2.1667 | 0.825 | 0.3133 | 1.5303 | 0.825 | 0.8121 | 0.1596 | 0.0680 | | 0.8511 | 53.0 | 2650 | 2.1791 | 0.83 | 0.3170 | 1.5332 | 0.83 | 0.8149 | 0.1608 | 0.0690 | | 0.8511 | 54.0 | 2700 | 2.1621 | 0.83 | 0.3148 | 1.5274 | 0.83 | 0.8146 | 0.1551 | 0.0693 | | 0.8511 | 55.0 | 2750 | 2.1572 | 0.83 | 0.3119 | 1.5318 | 0.83 | 0.8149 | 0.1532 | 0.0680 | | 0.8511 | 56.0 | 2800 | 2.1587 | 0.83 | 0.3100 | 1.5232 | 0.83 | 0.8148 | 0.1524 | 0.0712 | | 0.8511 | 57.0 | 2850 | 2.1596 | 0.83 | 0.3101 | 1.5234 | 0.83 | 0.8146 | 0.1560 | 0.0696 | | 0.8511 | 58.0 | 2900 | 2.1048 | 0.835 | 0.3047 | 1.5231 | 0.835 | 0.8189 | 0.1442 | 0.0676 | | 0.8511 | 59.0 | 2950 | 2.4279 | 0.76 | 0.4096 | 1.4535 | 0.76 | 0.7538 | 0.2078 | 0.0731 | | 0.8335 | 60.0 | 3000 | 2.2098 | 0.775 | 0.4036 | 1.4180 | 0.775 | 0.7565 | 0.2010 | 0.0870 | | 0.8335 | 61.0 | 3050 | 2.0122 | 0.85 | 0.2596 | 1.5903 | 0.85 | 0.8272 | 0.1349 | 0.0779 | | 0.8335 | 62.0 | 3100 | 2.2465 | 0.815 | 0.3311 | 1.6852 | 0.815 | 0.7899 | 0.1672 | 0.0658 | | 0.8335 | 63.0 | 3150 | 2.1239 | 0.84 | 0.2963 | 1.6390 | 0.8400 | 0.8305 | 0.1458 | 0.0878 | | 0.8335 | 64.0 | 3200 | 2.1931 | 0.82 | 0.3181 | 1.7037 | 0.82 | 0.8199 | 0.1654 | 0.0719 | | 0.8335 | 65.0 | 3250 | 1.8262 | 0.855 | 0.2493 | 1.4845 | 0.855 | 0.8335 | 0.1297 | 0.0456 | | 0.8335 | 66.0 | 3300 | 1.9467 | 0.845 | 0.2657 | 1.4217 | 0.845 | 0.8326 | 0.1361 | 0.0498 | | 0.8335 | 67.0 | 3350 | 1.9371 | 0.85 | 0.2680 | 1.4175 | 0.85 | 0.8405 | 0.1293 | 0.0506 | | 0.8335 | 68.0 | 3400 | 1.9172 | 0.85 | 0.2656 | 1.4203 | 0.85 | 0.8405 | 0.1331 | 0.0503 | | 0.8335 | 69.0 | 3450 | 1.8872 | 0.845 | 0.2664 | 1.4327 | 0.845 | 0.8324 | 0.1360 | 0.0493 | | 0.8281 | 70.0 | 3500 | 1.9045 | 0.845 | 0.2715 | 1.4920 | 0.845 | 0.8324 | 0.1377 | 0.0496 | | 0.8281 | 71.0 | 3550 | 1.8954 | 0.845 | 0.2684 | 1.4919 | 0.845 | 0.8338 | 0.1385 | 0.0499 | | 0.8281 | 72.0 | 3600 | 1.9222 | 0.85 | 0.2698 | 1.4870 | 0.85 | 0.8375 | 0.1356 | 0.0499 | | 0.8281 | 73.0 | 3650 | 1.9004 | 0.845 | 0.2691 | 1.4912 | 0.845 | 0.8335 | 0.1377 | 0.0484 | | 0.8281 | 74.0 | 3700 | 1.9168 | 0.85 | 0.2693 | 1.4903 | 0.85 | 0.8375 | 0.1338 | 0.0495 | | 0.8281 | 75.0 | 3750 | 1.8970 | 0.85 | 0.2700 | 1.4908 | 0.85 | 0.8366 | 0.1416 | 0.0477 | | 0.8281 | 76.0 | 3800 | 1.9089 | 0.85 | 0.2705 | 1.4867 | 0.85 | 0.8366 | 0.1373 | 0.0480 | | 0.8281 | 77.0 | 3850 | 1.8902 | 0.85 | 0.2697 | 1.4896 | 0.85 | 0.8366 | 0.1407 | 0.0464 | | 0.8281 | 78.0 | 3900 | 1.8889 | 0.85 | 0.2710 | 1.4882 | 0.85 | 0.8366 | 0.1421 | 0.0472 | | 0.8281 | 79.0 | 3950 | 1.9080 | 0.85 | 0.2712 | 1.4876 | 0.85 | 0.8366 | 0.1345 | 0.0476 | | 0.8047 | 80.0 | 4000 | 1.9011 | 0.85 | 0.2703 | 1.4864 | 0.85 | 0.8366 | 0.1373 | 0.0472 | | 0.8047 | 81.0 | 4050 | 1.9112 | 0.85 | 0.2735 | 1.4867 | 0.85 | 0.8366 | 0.1379 | 0.0465 | | 0.8047 | 82.0 | 4100 | 1.8850 | 0.85 | 0.2728 | 1.4872 | 0.85 | 0.8366 | 0.1419 | 0.0462 | | 0.8047 | 83.0 | 4150 | 1.9074 | 0.85 | 0.2740 | 1.4862 | 0.85 | 0.8366 | 0.1369 | 0.0463 | | 0.8047 | 84.0 | 4200 | 1.8804 | 0.85 | 0.2714 | 1.4818 | 0.85 | 0.8366 | 0.1376 | 0.0461 | | 0.8047 | 85.0 | 4250 | 1.9092 | 0.85 | 0.2757 | 1.4825 | 0.85 | 0.8366 | 0.1437 | 0.0463 | | 0.8047 | 86.0 | 4300 | 1.8985 | 0.85 | 0.2745 | 1.4827 | 0.85 | 0.8366 | 0.1390 | 0.0460 | | 0.8047 | 87.0 | 4350 | 1.9091 | 0.85 | 0.2731 | 1.4808 | 0.85 | 0.8366 | 0.1403 | 0.0466 | | 0.8047 | 88.0 | 4400 | 1.9037 | 0.85 | 0.2754 | 1.4836 | 0.85 | 0.8366 | 0.1383 | 0.0459 | | 0.8047 | 89.0 | 4450 | 1.8950 | 0.85 | 0.2750 | 1.4798 | 0.85 | 0.8366 | 0.1386 | 0.0452 | | 0.7971 | 90.0 | 4500 | 1.9115 | 0.85 | 0.2755 | 1.4785 | 0.85 | 0.8366 | 0.1387 | 0.0461 | | 0.7971 | 91.0 | 4550 | 1.9061 | 0.85 | 0.2757 | 1.4791 | 0.85 | 0.8366 | 0.1451 | 0.0460 | | 0.7971 | 92.0 | 4600 | 1.9058 | 0.85 | 0.2757 | 1.4785 | 0.85 | 0.8366 | 0.1392 | 0.0464 | | 0.7971 | 93.0 | 4650 | 1.9128 | 0.85 | 0.2724 | 1.4769 | 0.85 | 0.8366 | 0.1341 | 0.0468 | | 0.7971 | 94.0 | 4700 | 1.9115 | 0.85 | 0.2770 | 1.4771 | 0.85 | 0.8366 | 0.1388 | 0.0463 | | 0.7971 | 95.0 | 4750 | 1.9097 | 0.85 | 0.2761 | 1.4761 | 0.85 | 0.8366 | 0.1382 | 0.0462 | | 0.7971 | 96.0 | 4800 | 1.9025 | 0.85 | 0.2761 | 1.4759 | 0.85 | 0.8366 | 0.1385 | 0.0460 | | 0.7971 | 97.0 | 4850 | 1.9153 | 0.85 | 0.2775 | 1.4757 | 0.85 | 0.8366 | 0.1394 | 0.0463 | | 0.7971 | 98.0 | 4900 | 1.9084 | 0.85 | 0.2765 | 1.4755 | 0.85 | 0.8366 | 0.1388 | 0.0460 | | 0.7971 | 99.0 | 4950 | 1.9087 | 0.85 | 0.2772 | 1.4757 | 0.85 | 0.8366 | 0.1392 | 0.0460 | | 0.7931 | 100.0 | 5000 | 1.9099 | 0.85 | 0.2772 | 1.4757 | 0.85 | 0.8366 | 0.1392 | 0.0460 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-small_tobacco3482_simkd

# dit-base_tobacco-small_tobacco3482_simkd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6962 - Accuracy: 0.85 - Brier Loss: 0.2700 - Nll: 0.9667 - F1 Micro: 0.85 - F1 Macro: 0.8241 - Ece: 0.2479 - Aurc: 0.0379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 1.0013 | 0.18 | 0.8965 | 4.5407 | 0.18 | 0.1379 | 0.2160 | 0.6680 | | No log | 2.0 | 100 | 0.9916 | 0.3 | 0.8871 | 3.1090 | 0.3 | 0.1526 | 0.3057 | 0.4735 | | No log | 3.0 | 150 | 0.9644 | 0.51 | 0.8433 | 2.4502 | 0.51 | 0.3257 | 0.4499 | 0.2544 | | No log | 4.0 | 200 | 0.9207 | 0.575 | 0.7585 | 2.1964 | 0.575 | 0.3958 | 0.4563 | 0.2193 | | No log | 5.0 | 250 | 0.8726 | 0.635 | 0.6620 | 2.3923 | 0.635 | 0.5105 | 0.4321 | 0.1730 | | No log | 6.0 | 300 | 0.8303 | 0.665 | 0.5604 | 1.4922 | 0.665 | 0.5869 | 0.3717 | 0.1305 | | No log | 7.0 | 350 | 0.7994 | 0.745 | 0.4490 | 1.3772 | 0.745 | 0.6541 | 0.3557 | 0.0853 | | No log | 8.0 | 400 | 0.7822 | 0.79 | 0.4124 | 1.2076 | 0.79 | 0.7109 | 0.3035 | 0.0873 | | No log | 9.0 | 450 | 0.7808 | 0.78 | 0.3955 | 1.5529 | 0.78 | 0.7041 | 0.3123 | 0.0763 | | 0.8704 | 10.0 | 500 | 0.7923 | 0.695 | 0.4296 | 1.7171 | 0.695 | 0.6150 | 0.3012 | 0.1039 | | 0.8704 | 11.0 | 550 | 0.7848 | 0.745 | 0.4327 | 1.6327 | 0.745 | 0.6972 | 0.2800 | 0.1321 | | 0.8704 | 12.0 | 600 | 0.7600 | 0.825 | 0.3579 | 1.2569 | 0.825 | 0.7621 | 0.3015 | 0.0624 | | 0.8704 | 13.0 | 650 | 0.7570 | 0.79 | 0.3554 | 1.4638 | 0.79 | 0.7706 | 0.2964 | 0.0621 | | 0.8704 | 14.0 | 700 | 0.7504 | 0.81 | 0.3434 | 1.5597 | 0.81 | 0.7714 | 0.2930 | 0.0589 | | 0.8704 | 15.0 | 750 | 0.7481 | 0.8 | 0.3439 | 1.3827 | 0.8000 | 0.7641 | 0.2805 | 0.0675 | | 0.8704 | 16.0 | 800 | 0.7358 | 0.81 | 0.3357 | 1.4522 | 0.81 | 0.7889 | 0.3077 | 0.0610 | | 0.8704 | 17.0 | 850 | 0.7294 | 0.82 | 0.3179 | 1.0458 | 0.82 | 0.7820 | 0.2909 | 0.0564 | | 0.8704 | 18.0 | 900 | 0.7229 | 0.815 | 0.3092 | 1.2562 | 0.815 | 0.7862 | 0.2719 | 0.0496 | | 0.8704 | 19.0 | 950 | 0.7186 | 0.825 | 0.3069 | 1.0425 | 0.825 | 0.7977 | 0.2824 | 0.0558 | | 0.6968 | 20.0 | 1000 | 0.7156 | 0.83 | 0.3031 | 0.9897 | 0.83 | 0.8039 | 0.2660 | 0.0490 | | 0.6968 | 21.0 | 1050 | 0.7135 | 0.82 | 0.3014 | 1.0562 | 0.82 | 0.7887 | 0.2745 | 0.0462 | | 0.6968 | 22.0 | 1100 | 0.7116 | 0.835 | 0.2997 | 0.9822 | 0.835 | 0.8102 | 0.2817 | 0.0452 | | 0.6968 | 23.0 | 1150 | 0.7114 | 0.82 | 0.3047 | 0.9197 | 0.82 | 0.7937 | 0.2669 | 0.0484 | | 0.6968 | 24.0 | 1200 | 0.7111 | 0.8 | 0.3032 | 0.9744 | 0.8000 | 0.7690 | 0.2624 | 0.0504 | | 0.6968 | 25.0 | 1250 | 0.7076 | 0.805 | 0.3025 | 0.9884 | 0.805 | 0.7677 | 0.2538 | 0.0478 | | 0.6968 | 26.0 | 1300 | 0.7074 | 0.82 | 0.3037 | 0.9954 | 0.82 | 0.7877 | 0.2592 | 0.0496 | | 0.6968 | 27.0 | 1350 | 0.7053 | 0.825 | 0.2998 | 0.9712 | 0.825 | 0.7885 | 0.2628 | 0.0454 | | 0.6968 | 28.0 | 1400 | 0.7046 | 0.82 | 0.2936 | 0.9780 | 0.82 | 0.7886 | 0.2573 | 0.0438 | | 0.6968 | 29.0 | 1450 | 0.7068 | 0.82 | 0.3000 | 0.9943 | 0.82 | 0.7895 | 0.2382 | 0.0447 | | 0.6551 | 30.0 | 1500 | 0.7045 | 0.83 | 0.2881 | 0.9107 | 0.83 | 0.8010 | 0.2363 | 0.0439 | | 0.6551 | 31.0 | 1550 | 0.7033 | 0.825 | 0.2936 | 0.9794 | 0.825 | 0.7858 | 0.2556 | 0.0433 | | 0.6551 | 32.0 | 1600 | 0.7014 | 0.82 | 0.2890 | 0.9799 | 0.82 | 0.7895 | 0.2495 | 0.0418 | | 0.6551 | 33.0 | 1650 | 0.7020 | 0.815 | 0.2921 | 0.9658 | 0.815 | 0.7820 | 0.2556 | 0.0449 | | 0.6551 | 34.0 | 1700 | 0.7012 | 0.835 | 0.2885 | 1.0419 | 0.835 | 0.8042 | 0.2581 | 0.0417 | | 0.6551 | 35.0 | 1750 | 0.7013 | 0.835 | 0.2902 | 0.9773 | 0.835 | 0.8035 | 0.2522 | 0.0435 | | 0.6551 | 36.0 | 1800 | 0.7016 | 0.825 | 0.2884 | 0.9815 | 0.825 | 0.7851 | 0.2518 | 0.0432 | | 0.6551 | 37.0 | 1850 | 0.7007 | 0.835 | 0.2888 | 0.9724 | 0.835 | 0.8133 | 0.2486 | 0.0438 | | 0.6551 | 38.0 | 1900 | 0.6984 | 0.825 | 0.2847 | 0.9650 | 0.825 | 0.7897 | 0.2487 | 0.0415 | | 0.6551 | 39.0 | 1950 | 0.7001 | 0.84 | 0.2843 | 1.0535 | 0.8400 | 0.8104 | 0.2566 | 0.0418 | | 0.6381 | 40.0 | 2000 | 0.6990 | 0.825 | 0.2843 | 0.9673 | 0.825 | 0.7963 | 0.2396 | 0.0429 | | 0.6381 | 41.0 | 2050 | 0.7002 | 0.84 | 0.2875 | 1.0599 | 0.8400 | 0.8098 | 0.2618 | 0.0413 | | 0.6381 | 42.0 | 2100 | 0.6967 | 0.83 | 0.2791 | 0.9676 | 0.83 | 0.7929 | 0.2441 | 0.0403 | | 0.6381 | 43.0 | 2150 | 0.6978 | 0.835 | 0.2802 | 0.9771 | 0.835 | 0.8071 | 0.2526 | 0.0416 | | 0.6381 | 44.0 | 2200 | 0.6969 | 0.84 | 0.2795 | 0.9478 | 0.8400 | 0.8164 | 0.2464 | 0.0418 | | 0.6381 | 45.0 | 2250 | 0.6971 | 0.835 | 0.2760 | 0.9712 | 0.835 | 0.8030 | 0.2333 | 0.0392 | | 0.6381 | 46.0 | 2300 | 0.6985 | 0.84 | 0.2813 | 0.9692 | 0.8400 | 0.8072 | 0.2403 | 0.0404 | | 0.6381 | 47.0 | 2350 | 0.6976 | 0.835 | 0.2796 | 1.0420 | 0.835 | 0.8042 | 0.2374 | 0.0406 | | 0.6381 | 48.0 | 2400 | 0.6965 | 0.85 | 0.2778 | 0.9753 | 0.85 | 0.8205 | 0.2653 | 0.0403 | | 0.6381 | 49.0 | 2450 | 0.6969 | 0.825 | 0.2747 | 0.9606 | 0.825 | 0.7871 | 0.2478 | 0.0394 | | 0.6274 | 50.0 | 2500 | 0.6954 | 0.835 | 0.2746 | 0.9572 | 0.835 | 0.8070 | 0.2395 | 0.0406 | | 0.6274 | 51.0 | 2550 | 0.6972 | 0.835 | 0.2755 | 1.0383 | 0.835 | 0.8070 | 0.2484 | 0.0391 | | 0.6274 | 52.0 | 2600 | 0.6955 | 0.83 | 0.2752 | 0.9699 | 0.83 | 0.7998 | 0.2562 | 0.0406 | | 0.6274 | 53.0 | 2650 | 0.6950 | 0.835 | 0.2693 | 0.9563 | 0.835 | 0.8030 | 0.2300 | 0.0373 | | 0.6274 | 54.0 | 2700 | 0.6960 | 0.83 | 0.2727 | 0.9646 | 0.83 | 0.7977 | 0.2347 | 0.0399 | | 0.6274 | 55.0 | 2750 | 0.6946 | 0.83 | 0.2711 | 0.9603 | 0.83 | 0.8058 | 0.2279 | 0.0384 | | 0.6274 | 56.0 | 2800 | 0.6940 | 0.835 | 0.2726 | 0.9579 | 0.835 | 0.8088 | 0.2478 | 0.0380 | | 0.6274 | 57.0 | 2850 | 0.6951 | 0.835 | 0.2732 | 0.9594 | 0.835 | 0.8090 | 0.2336 | 0.0418 | | 0.6274 | 58.0 | 2900 | 0.6936 | 0.84 | 0.2684 | 0.9575 | 0.8400 | 0.8079 | 0.2490 | 0.0373 | | 0.6274 | 59.0 | 2950 | 0.6949 | 0.835 | 0.2701 | 0.9543 | 0.835 | 0.8088 | 0.2261 | 0.0389 | | 0.6207 | 60.0 | 3000 | 0.6939 | 0.84 | 0.2697 | 0.9574 | 0.8400 | 0.8161 | 0.2339 | 0.0378 | | 0.6207 | 61.0 | 3050 | 0.6952 | 0.84 | 0.2706 | 0.9611 | 0.8400 | 0.8080 | 0.2306 | 0.0379 | | 0.6207 | 62.0 | 3100 | 0.6940 | 0.835 | 0.2691 | 0.9523 | 0.835 | 0.8086 | 0.2451 | 0.0382 | | 0.6207 | 63.0 | 3150 | 0.6946 | 0.835 | 0.2672 | 0.9627 | 0.835 | 0.8088 | 0.2347 | 0.0374 | | 0.6207 | 64.0 | 3200 | 0.6949 | 0.84 | 0.2713 | 0.9602 | 0.8400 | 0.8139 | 0.2404 | 0.0384 | | 0.6207 | 65.0 | 3250 | 0.6944 | 0.835 | 0.2662 | 0.9603 | 0.835 | 0.8079 | 0.2308 | 0.0377 | | 0.6207 | 66.0 | 3300 | 0.6946 | 0.835 | 0.2698 | 0.9593 | 0.835 | 0.8088 | 0.2352 | 0.0390 | | 0.6207 | 67.0 | 3350 | 0.6934 | 0.83 | 0.2658 | 0.9558 | 0.83 | 0.8060 | 0.2260 | 0.0384 | | 0.6207 | 68.0 | 3400 | 0.6944 | 0.83 | 0.2689 | 0.9517 | 0.83 | 0.8058 | 0.2208 | 0.0399 | | 0.6207 | 69.0 | 3450 | 0.6946 | 0.835 | 0.2698 | 0.9553 | 0.835 | 0.8042 | 0.2331 | 0.0383 | | 0.6156 | 70.0 | 3500 | 0.6948 | 0.83 | 0.2690 | 0.9549 | 0.83 | 0.8058 | 0.2280 | 0.0391 | | 0.6156 | 71.0 | 3550 | 0.6936 | 0.84 | 0.2676 | 0.9532 | 0.8400 | 0.8122 | 0.2346 | 0.0383 | | 0.6156 | 72.0 | 3600 | 0.6946 | 0.835 | 0.2667 | 0.9545 | 0.835 | 0.8088 | 0.2492 | 0.0379 | | 0.6156 | 73.0 | 3650 | 0.6939 | 0.84 | 0.2670 | 0.9534 | 0.8400 | 0.8139 | 0.2466 | 0.0377 | | 0.6156 | 74.0 | 3700 | 0.6948 | 0.835 | 0.2695 | 0.9522 | 0.835 | 0.8086 | 0.2312 | 0.0390 | | 0.6156 | 75.0 | 3750 | 0.6951 | 0.835 | 0.2701 | 0.9622 | 0.835 | 0.8111 | 0.2158 | 0.0397 | | 0.6156 | 76.0 | 3800 | 0.6949 | 0.84 | 0.2682 | 0.9606 | 0.8400 | 0.8139 | 0.2415 | 0.0382 | | 0.6156 | 77.0 | 3850 | 0.6950 | 0.84 | 0.2684 | 0.9629 | 0.8400 | 0.8118 | 0.2493 | 0.0381 | | 0.6156 | 78.0 | 3900 | 0.6946 | 0.835 | 0.2685 | 0.9522 | 0.835 | 0.8111 | 0.2360 | 0.0390 | | 0.6156 | 79.0 | 3950 | 0.6944 | 0.84 | 0.2668 | 0.9544 | 0.8400 | 0.8118 | 0.2377 | 0.0372 | | 0.612 | 80.0 | 4000 | 0.6954 | 0.84 | 0.2692 | 0.9579 | 0.8400 | 0.8139 | 0.2321 | 0.0381 | | 0.612 | 81.0 | 4050 | 0.6956 | 0.84 | 0.2701 | 0.9606 | 0.8400 | 0.8139 | 0.2354 | 0.0382 | | 0.612 | 82.0 | 4100 | 0.6952 | 0.835 | 0.2686 | 0.9600 | 0.835 | 0.8086 | 0.2540 | 0.0381 | | 0.612 | 83.0 | 4150 | 0.6955 | 0.835 | 0.2689 | 0.9571 | 0.835 | 0.8086 | 0.2465 | 0.0383 | | 0.612 | 84.0 | 4200 | 0.6952 | 0.84 | 0.2689 | 0.9583 | 0.8400 | 0.8159 | 0.2308 | 0.0387 | | 0.612 | 85.0 | 4250 | 0.6956 | 0.835 | 0.2702 | 0.9618 | 0.835 | 0.8042 | 0.2365 | 0.0386 | | 0.612 | 86.0 | 4300 | 0.6950 | 0.835 | 0.2683 | 0.9572 | 0.835 | 0.8086 | 0.2228 | 0.0382 | | 0.612 | 87.0 | 4350 | 0.6949 | 0.84 | 0.2692 | 0.9583 | 0.8400 | 0.8118 | 0.2497 | 0.0381 | | 0.612 | 88.0 | 4400 | 0.6953 | 0.845 | 0.2695 | 0.9617 | 0.845 | 0.8209 | 0.2558 | 0.0386 | | 0.612 | 89.0 | 4450 | 0.6952 | 0.845 | 0.2689 | 0.9611 | 0.845 | 0.8209 | 0.2251 | 0.0383 | | 0.6097 | 90.0 | 4500 | 0.6961 | 0.835 | 0.2701 | 0.9645 | 0.835 | 0.8042 | 0.2444 | 0.0386 | | 0.6097 | 91.0 | 4550 | 0.6954 | 0.845 | 0.2689 | 0.9619 | 0.845 | 0.8209 | 0.2324 | 0.0383 | | 0.6097 | 92.0 | 4600 | 0.6959 | 0.845 | 0.2700 | 0.9636 | 0.845 | 0.8209 | 0.2277 | 0.0388 | | 0.6097 | 93.0 | 4650 | 0.6959 | 0.85 | 0.2694 | 0.9654 | 0.85 | 0.8241 | 0.2396 | 0.0379 | | 0.6097 | 94.0 | 4700 | 0.6960 | 0.85 | 0.2696 | 0.9643 | 0.85 | 0.8241 | 0.2471 | 0.0379 | | 0.6097 | 95.0 | 4750 | 0.6959 | 0.85 | 0.2694 | 0.9650 | 0.85 | 0.8241 | 0.2233 | 0.0378 | | 0.6097 | 96.0 | 4800 | 0.6962 | 0.845 | 0.2700 | 0.9666 | 0.845 | 0.8144 | 0.2558 | 0.0382 | | 0.6097 | 97.0 | 4850 | 0.6962 | 0.85 | 0.2699 | 0.9662 | 0.85 | 0.8241 | 0.2400 | 0.0381 | | 0.6097 | 98.0 | 4900 | 0.6962 | 0.85 | 0.2700 | 0.9662 | 0.85 | 0.8241 | 0.2396 | 0.0380 | | 0.6097 | 99.0 | 4950 | 0.6963 | 0.85 | 0.2700 | 0.9667 | 0.85 | 0.8241 | 0.2478 | 0.0379 | | 0.6083 | 100.0 | 5000 | 0.6962 | 0.85 | 0.2700 | 0.9667 | 0.85 | 0.8241 | 0.2479 | 0.0379 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/dit-base_tobacco-small_tobacco3482_og_simkd

# dit-base_tobacco-small_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 309.8690 - Accuracy: 0.815 - Brier Loss: 0.3313 - Nll: 1.1190 - F1 Micro: 0.815 - F1 Macro: 0.7825 - Ece: 0.2569 - Aurc: 0.0659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 328.2257 | 0.365 | 0.8441 | 5.5835 | 0.3650 | 0.2447 | 0.3371 | 0.4390 | | No log | 2.0 | 100 | 325.5961 | 0.58 | 0.6442 | 1.8499 | 0.58 | 0.4727 | 0.3233 | 0.2414 | | No log | 3.0 | 150 | 323.3813 | 0.66 | 0.4759 | 1.5815 | 0.66 | 0.5348 | 0.2704 | 0.1489 | | No log | 4.0 | 200 | 322.5013 | 0.715 | 0.4234 | 1.6240 | 0.715 | 0.6009 | 0.2382 | 0.1142 | | No log | 5.0 | 250 | 321.7315 | 0.755 | 0.3532 | 1.2868 | 0.755 | 0.6596 | 0.2141 | 0.0687 | | No log | 6.0 | 300 | 320.5884 | 0.775 | 0.3668 | 1.4106 | 0.775 | 0.7233 | 0.2284 | 0.0922 | | No log | 7.0 | 350 | 320.8456 | 0.775 | 0.3638 | 1.4833 | 0.775 | 0.7172 | 0.2487 | 0.0666 | | No log | 8.0 | 400 | 319.6829 | 0.785 | 0.3308 | 1.3914 | 0.785 | 0.7203 | 0.1959 | 0.0674 | | No log | 9.0 | 450 | 319.7741 | 0.815 | 0.3459 | 1.3920 | 0.815 | 0.7832 | 0.2541 | 0.0681 | | 325.907 | 10.0 | 500 | 319.4605 | 0.775 | 0.3162 | 1.2997 | 0.775 | 0.6987 | 0.2140 | 0.0575 | | 325.907 | 11.0 | 550 | 318.6996 | 0.81 | 0.3190 | 1.2271 | 0.81 | 0.7670 | 0.2110 | 0.0614 | | 325.907 | 12.0 | 600 | 318.0233 | 0.81 | 0.3183 | 1.2432 | 0.81 | 0.7673 | 0.2134 | 0.0624 | | 325.907 | 13.0 | 650 | 318.2606 | 0.79 | 0.3259 | 1.2187 | 0.79 | 0.7457 | 0.2299 | 0.0591 | | 325.907 | 14.0 | 700 | 317.7428 | 0.83 | 0.3183 | 1.3279 | 0.83 | 0.8035 | 0.2449 | 0.0512 | | 325.907 | 15.0 | 750 | 317.7053 | 0.81 | 0.3251 | 1.2097 | 0.81 | 0.7604 | 0.2193 | 0.0566 | | 325.907 | 16.0 | 800 | 317.3470 | 0.84 | 0.3142 | 1.2606 | 0.8400 | 0.8132 | 0.2272 | 0.0484 | | 325.907 | 17.0 | 850 | 316.8029 | 0.815 | 0.3202 | 1.1571 | 0.815 | 0.7748 | 0.2316 | 0.0563 | | 325.907 | 18.0 | 900 | 316.9777 | 0.805 | 0.3442 | 1.1453 | 0.805 | 0.7645 | 0.2432 | 0.0625 | | 325.907 | 19.0 | 950 | 316.2359 | 0.815 | 0.3219 | 1.1399 | 0.815 | 0.7717 | 0.2404 | 0.0603 | | 320.1296 | 20.0 | 1000 | 316.1051 | 0.8 | 0.3220 | 1.1807 | 0.8000 | 0.7500 | 0.2412 | 0.0576 | | 320.1296 | 21.0 | 1050 | 315.8117 | 0.845 | 0.3099 | 1.0976 | 0.845 | 0.8084 | 0.2530 | 0.0547 | | 320.1296 | 22.0 | 1100 | 315.7457 | 0.82 | 0.3238 | 1.1904 | 0.82 | 0.7663 | 0.2507 | 0.0548 | | 320.1296 | 23.0 | 1150 | 315.6591 | 0.82 | 0.3357 | 1.4044 | 0.82 | 0.7925 | 0.2639 | 0.0586 | | 320.1296 | 24.0 | 1200 | 315.4048 | 0.82 | 0.3270 | 1.0817 | 0.82 | 0.7681 | 0.2575 | 0.0629 | | 320.1296 | 25.0 | 1250 | 314.9790 | 0.81 | 0.3309 | 1.2002 | 0.81 | 0.7732 | 0.2279 | 0.0656 | | 320.1296 | 26.0 | 1300 | 314.6778 | 0.79 | 0.3189 | 1.1219 | 0.79 | 0.7464 | 0.2014 | 0.0576 | | 320.1296 | 27.0 | 1350 | 314.7844 | 0.8 | 0.3345 | 1.0655 | 0.8000 | 0.7555 | 0.2398 | 0.0661 | | 320.1296 | 28.0 | 1400 | 314.4464 | 0.815 | 0.3175 | 1.1116 | 0.815 | 0.7636 | 0.2426 | 0.0532 | | 320.1296 | 29.0 | 1450 | 314.3737 | 0.845 | 0.3271 | 1.1042 | 0.845 | 0.8072 | 0.2595 | 0.0531 | | 317.1926 | 30.0 | 1500 | 313.9464 | 0.82 | 0.3225 | 1.1270 | 0.82 | 0.7841 | 0.2087 | 0.0609 | | 317.1926 | 31.0 | 1550 | 314.0068 | 0.835 | 0.3187 | 1.1834 | 0.835 | 0.8070 | 0.2470 | 0.0522 | | 317.1926 | 32.0 | 1600 | 313.8198 | 0.81 | 0.3271 | 1.0324 | 0.81 | 0.7642 | 0.2484 | 0.0609 | | 317.1926 | 33.0 | 1650 | 313.7599 | 0.83 | 0.3193 | 1.0993 | 0.83 | 0.7910 | 0.2382 | 0.0536 | | 317.1926 | 34.0 | 1700 | 313.4889 | 0.82 | 0.3224 | 1.1743 | 0.82 | 0.7823 | 0.2587 | 0.0546 | | 317.1926 | 35.0 | 1750 | 313.2496 | 0.825 | 0.3324 | 1.1041 | 0.825 | 0.7988 | 0.2404 | 0.0652 | | 317.1926 | 36.0 | 1800 | 313.1823 | 0.83 | 0.3207 | 1.0900 | 0.83 | 0.8007 | 0.2505 | 0.0581 | | 317.1926 | 37.0 | 1850 | 313.1304 | 0.83 | 0.3367 | 1.2073 | 0.83 | 0.7973 | 0.2615 | 0.0571 | | 317.1926 | 38.0 | 1900 | 313.2971 | 0.815 | 0.3398 | 1.1045 | 0.815 | 0.7709 | 0.2411 | 0.0641 | | 317.1926 | 39.0 | 1950 | 313.0526 | 0.815 | 0.3352 | 1.1023 | 0.815 | 0.7744 | 0.2455 | 0.0616 | | 315.1897 | 40.0 | 2000 | 312.7858 | 0.84 | 0.3231 | 1.0983 | 0.8400 | 0.8096 | 0.2619 | 0.0538 | | 315.1897 | 41.0 | 2050 | 312.5119 | 0.815 | 0.3290 | 1.1174 | 0.815 | 0.7858 | 0.2540 | 0.0604 | | 315.1897 | 42.0 | 2100 | 312.5961 | 0.82 | 0.3305 | 1.2144 | 0.82 | 0.7787 | 0.2480 | 0.0572 | | 315.1897 | 43.0 | 2150 | 312.3510 | 0.825 | 0.3357 | 1.1367 | 0.825 | 0.7936 | 0.2398 | 0.0658 | | 315.1897 | 44.0 | 2200 | 312.4015 | 0.81 | 0.3303 | 1.1015 | 0.81 | 0.7837 | 0.2488 | 0.0598 | | 315.1897 | 45.0 | 2250 | 312.2003 | 0.825 | 0.3286 | 1.1810 | 0.825 | 0.7953 | 0.2480 | 0.0614 | | 315.1897 | 46.0 | 2300 | 312.1683 | 0.825 | 0.3283 | 1.1112 | 0.825 | 0.7881 | 0.2414 | 0.0587 | | 315.1897 | 47.0 | 2350 | 312.2554 | 0.815 | 0.3433 | 1.1313 | 0.815 | 0.7709 | 0.2579 | 0.0694 | | 315.1897 | 48.0 | 2400 | 312.0919 | 0.825 | 0.3364 | 1.1074 | 0.825 | 0.7963 | 0.2471 | 0.0636 | | 315.1897 | 49.0 | 2450 | 312.0760 | 0.82 | 0.3412 | 1.1076 | 0.82 | 0.7859 | 0.2554 | 0.0661 | | 313.7276 | 50.0 | 2500 | 311.7450 | 0.83 | 0.3245 | 1.1723 | 0.83 | 0.7994 | 0.2512 | 0.0558 | | 313.7276 | 51.0 | 2550 | 311.5801 | 0.835 | 0.3236 | 1.1056 | 0.835 | 0.7954 | 0.2581 | 0.0576 | | 313.7276 | 52.0 | 2600 | 311.7016 | 0.83 | 0.3235 | 1.1182 | 0.83 | 0.7988 | 0.2462 | 0.0560 | | 313.7276 | 53.0 | 2650 | 311.0808 | 0.81 | 0.3308 | 1.0526 | 0.81 | 0.7716 | 0.2401 | 0.0687 | | 313.7276 | 54.0 | 2700 | 311.3835 | 0.81 | 0.3304 | 1.1210 | 0.81 | 0.7803 | 0.2442 | 0.0604 | | 313.7276 | 55.0 | 2750 | 311.1007 | 0.825 | 0.3285 | 1.1265 | 0.825 | 0.7931 | 0.2569 | 0.0639 | | 313.7276 | 56.0 | 2800 | 311.3446 | 0.81 | 0.3273 | 1.1896 | 0.81 | 0.7810 | 0.2342 | 0.0622 | | 313.7276 | 57.0 | 2850 | 311.0753 | 0.825 | 0.3327 | 1.1225 | 0.825 | 0.7929 | 0.2659 | 0.0668 | | 313.7276 | 58.0 | 2900 | 311.3600 | 0.825 | 0.3320 | 1.1142 | 0.825 | 0.8000 | 0.2524 | 0.0640 | | 313.7276 | 59.0 | 2950 | 310.8636 | 0.83 | 0.3242 | 1.1157 | 0.83 | 0.8022 | 0.2416 | 0.0633 | | 312.6368 | 60.0 | 3000 | 310.7809 | 0.815 | 0.3386 | 1.2166 | 0.815 | 0.7820 | 0.2571 | 0.0702 | | 312.6368 | 61.0 | 3050 | 310.9625 | 0.825 | 0.3273 | 1.1168 | 0.825 | 0.7923 | 0.2362 | 0.0608 | | 312.6368 | 62.0 | 3100 | 311.1122 | 0.81 | 0.3369 | 1.1021 | 0.81 | 0.7700 | 0.2433 | 0.0633 | | 312.6368 | 63.0 | 3150 | 311.1530 | 0.82 | 0.3351 | 1.1108 | 0.82 | 0.7780 | 0.2584 | 0.0615 | | 312.6368 | 64.0 | 3200 | 310.9366 | 0.8 | 0.3288 | 1.1112 | 0.8000 | 0.7616 | 0.2545 | 0.0609 | | 312.6368 | 65.0 | 3250 | 310.7639 | 0.82 | 0.3379 | 1.0992 | 0.82 | 0.7898 | 0.2407 | 0.0710 | | 312.6368 | 66.0 | 3300 | 310.5876 | 0.81 | 0.3287 | 1.1197 | 0.81 | 0.7763 | 0.2270 | 0.0654 | | 312.6368 | 67.0 | 3350 | 310.7344 | 0.805 | 0.3387 | 1.1279 | 0.805 | 0.7646 | 0.2354 | 0.0679 | | 312.6368 | 68.0 | 3400 | 310.2750 | 0.825 | 0.3323 | 1.1367 | 0.825 | 0.7971 | 0.2514 | 0.0673 | | 312.6368 | 69.0 | 3450 | 310.5080 | 0.815 | 0.3298 | 1.1049 | 0.815 | 0.7845 | 0.2329 | 0.0664 | | 311.7616 | 70.0 | 3500 | 310.6353 | 0.81 | 0.3305 | 1.1098 | 0.81 | 0.7745 | 0.2346 | 0.0633 | | 311.7616 | 71.0 | 3550 | 310.3249 | 0.825 | 0.3286 | 1.1117 | 0.825 | 0.7951 | 0.2455 | 0.0641 | | 311.7616 | 72.0 | 3600 | 310.5689 | 0.825 | 0.3248 | 1.1079 | 0.825 | 0.7911 | 0.2388 | 0.0586 | | 311.7616 | 73.0 | 3650 | 310.4175 | 0.82 | 0.3298 | 1.1169 | 0.82 | 0.7859 | 0.2338 | 0.0630 | | 311.7616 | 74.0 | 3700 | 310.1338 | 0.815 | 0.3313 | 1.1236 | 0.815 | 0.7902 | 0.2558 | 0.0677 | | 311.7616 | 75.0 | 3750 | 310.4428 | 0.825 | 0.3310 | 1.1269 | 0.825 | 0.7972 | 0.2458 | 0.0606 | | 311.7616 | 76.0 | 3800 | 310.3477 | 0.81 | 0.3317 | 1.1060 | 0.81 | 0.7775 | 0.2392 | 0.0654 | | 311.7616 | 77.0 | 3850 | 310.2144 | 0.815 | 0.3294 | 1.1076 | 0.815 | 0.7857 | 0.2387 | 0.0627 | | 311.7616 | 78.0 | 3900 | 310.1073 | 0.82 | 0.3296 | 1.1246 | 0.82 | 0.7993 | 0.2496 | 0.0634 | | 311.7616 | 79.0 | 3950 | 310.1449 | 0.805 | 0.3246 | 1.1134 | 0.805 | 0.7734 | 0.2277 | 0.0627 | | 311.1587 | 80.0 | 4000 | 310.1684 | 0.81 | 0.3327 | 1.1094 | 0.81 | 0.7781 | 0.2493 | 0.0660 | | 311.1587 | 81.0 | 4050 | 310.1772 | 0.815 | 0.3311 | 1.1129 | 0.815 | 0.7876 | 0.2447 | 0.0668 | | 311.1587 | 82.0 | 4100 | 309.9326 | 0.805 | 0.3295 | 1.1172 | 0.805 | 0.7716 | 0.2508 | 0.0666 | | 311.1587 | 83.0 | 4150 | 310.1067 | 0.805 | 0.3330 | 1.1209 | 0.805 | 0.7756 | 0.2252 | 0.0653 | | 311.1587 | 84.0 | 4200 | 309.9362 | 0.825 | 0.3288 | 1.1150 | 0.825 | 0.8024 | 0.2500 | 0.0637 | | 311.1587 | 85.0 | 4250 | 309.6593 | 0.81 | 0.3292 | 1.1226 | 0.81 | 0.7723 | 0.2306 | 0.0680 | | 311.1587 | 86.0 | 4300 | 309.9828 | 0.8 | 0.3310 | 1.1252 | 0.8000 | 0.7643 | 0.2474 | 0.0662 | | 311.1587 | 87.0 | 4350 | 310.0325 | 0.825 | 0.3322 | 1.1136 | 0.825 | 0.7935 | 0.2633 | 0.0634 | | 311.1587 | 88.0 | 4400 | 309.8688 | 0.815 | 0.3320 | 1.1145 | 0.815 | 0.7824 | 0.2478 | 0.0675 | | 311.1587 | 89.0 | 4450 | 310.0577 | 0.81 | 0.3324 | 1.1160 | 0.81 | 0.7810 | 0.2475 | 0.0648 | | 310.732 | 90.0 | 4500 | 309.8999 | 0.81 | 0.3273 | 1.1120 | 0.81 | 0.7720 | 0.2356 | 0.0624 | | 310.732 | 91.0 | 4550 | 309.7399 | 0.815 | 0.3256 | 1.1164 | 0.815 | 0.7824 | 0.2502 | 0.0649 | | 310.732 | 92.0 | 4600 | 309.9419 | 0.805 | 0.3287 | 1.1183 | 0.805 | 0.7751 | 0.2353 | 0.0640 | | 310.732 | 93.0 | 4650 | 309.9055 | 0.81 | 0.3268 | 1.1194 | 0.81 | 0.7761 | 0.2429 | 0.0613 | | 310.732 | 94.0 | 4700 | 309.7320 | 0.82 | 0.3275 | 1.1117 | 0.82 | 0.7914 | 0.2408 | 0.0654 | | 310.732 | 95.0 | 4750 | 309.9635 | 0.81 | 0.3334 | 1.1067 | 0.81 | 0.7747 | 0.2317 | 0.0637 | | 310.732 | 96.0 | 4800 | 309.9630 | 0.805 | 0.3304 | 1.1165 | 0.805 | 0.7712 | 0.2316 | 0.0631 | | 310.732 | 97.0 | 4850 | 309.8564 | 0.815 | 0.3263 | 1.1130 | 0.815 | 0.7870 | 0.2355 | 0.0619 | | 310.732 | 98.0 | 4900 | 309.7815 | 0.815 | 0.3298 | 1.1198 | 0.815 | 0.7857 | 0.2386 | 0.0634 | | 310.732 | 99.0 | 4950 | 309.8337 | 0.81 | 0.3354 | 1.0806 | 0.81 | 0.7818 | 0.2480 | 0.0672 | | 310.5225 | 100.0 | 5000 | 309.8690 | 0.815 | 0.3313 | 1.1190 | 0.815 | 0.7825 | 0.2569 | 0.0659 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd

# resnet101-base_tobacco-cnn_tobacco3482_kd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8158 - Accuracy: 0.565 - Brier Loss: 0.6104 - Nll: 2.6027 - F1 Micro: 0.565 - F1 Macro: 0.4783 - Ece: 0.2677 - Aurc: 0.2516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 4 | 1.4988 | 0.065 | 0.9007 | 9.6504 | 0.065 | 0.0267 | 0.1512 | 0.9377 | | No log | 2.0 | 8 | 1.4615 | 0.155 | 0.8961 | 7.9200 | 0.155 | 0.0268 | 0.2328 | 0.9605 | | No log | 3.0 | 12 | 1.4913 | 0.155 | 0.9531 | 11.6402 | 0.155 | 0.0268 | 0.3390 | 0.8899 | | No log | 4.0 | 16 | 2.2747 | 0.155 | 1.4111 | 11.0294 | 0.155 | 0.0268 | 0.7077 | 0.7068 | | No log | 5.0 | 20 | 2.4543 | 0.155 | 1.4359 | 8.3074 | 0.155 | 0.0268 | 0.7226 | 0.6151 | | No log | 6.0 | 24 | 1.9614 | 0.155 | 1.1785 | 6.6431 | 0.155 | 0.0283 | 0.5497 | 0.6022 | | No log | 7.0 | 28 | 1.6280 | 0.18 | 0.9978 | 5.8468 | 0.18 | 0.0488 | 0.4014 | 0.6135 | | No log | 8.0 | 32 | 1.3465 | 0.225 | 0.8993 | 5.6177 | 0.225 | 0.0740 | 0.3378 | 0.5786 | | No log | 9.0 | 36 | 1.2597 | 0.225 | 0.8794 | 5.0542 | 0.225 | 0.0727 | 0.3403 | 0.5658 | | No log | 10.0 | 40 | 1.1149 | 0.27 | 0.8181 | 4.5188 | 0.27 | 0.1222 | 0.2890 | 0.5230 | | No log | 11.0 | 44 | 0.9805 | 0.31 | 0.7600 | 3.8687 | 0.31 | 0.1726 | 0.2703 | 0.4690 | | No log | 12.0 | 48 | 1.0099 | 0.335 | 0.7732 | 3.6652 | 0.335 | 0.2095 | 0.2892 | 0.4739 | | No log | 13.0 | 52 | 1.0522 | 0.335 | 0.7919 | 3.3843 | 0.335 | 0.2562 | 0.3006 | 0.6402 | | No log | 14.0 | 56 | 1.0566 | 0.32 | 0.7868 | 3.4244 | 0.32 | 0.2373 | 0.3023 | 0.6094 | | No log | 15.0 | 60 | 0.9670 | 0.405 | 0.7333 | 3.3926 | 0.405 | 0.3189 | 0.3013 | 0.4037 | | No log | 16.0 | 64 | 1.0979 | 0.31 | 0.7877 | 3.3045 | 0.31 | 0.2262 | 0.2792 | 0.5720 | | No log | 17.0 | 68 | 0.9022 | 0.44 | 0.6913 | 3.2277 | 0.44 | 0.3429 | 0.2902 | 0.3657 | | No log | 18.0 | 72 | 1.2120 | 0.315 | 0.8075 | 4.1289 | 0.315 | 0.2323 | 0.2857 | 0.5909 | | No log | 19.0 | 76 | 1.1945 | 0.39 | 0.7974 | 4.2350 | 0.39 | 0.3292 | 0.3271 | 0.5989 | | No log | 20.0 | 80 | 1.3861 | 0.345 | 0.7981 | 5.2605 | 0.345 | 0.2700 | 0.2832 | 0.5299 | | No log | 21.0 | 84 | 1.2243 | 0.33 | 0.8073 | 4.5262 | 0.33 | 0.2545 | 0.3068 | 0.6133 | | No log | 22.0 | 88 | 1.0455 | 0.38 | 0.7238 | 2.7133 | 0.38 | 0.3084 | 0.2901 | 0.4855 | | No log | 23.0 | 92 | 0.9044 | 0.45 | 0.6814 | 3.4361 | 0.45 | 0.3273 | 0.2927 | 0.3246 | | No log | 24.0 | 96 | 0.8930 | 0.495 | 0.6596 | 3.3412 | 0.495 | 0.4185 | 0.2882 | 0.3070 | | No log | 25.0 | 100 | 0.8665 | 0.485 | 0.6534 | 2.9998 | 0.485 | 0.4154 | 0.2641 | 0.3298 | | No log | 26.0 | 104 | 1.0458 | 0.375 | 0.7579 | 3.1074 | 0.375 | 0.3333 | 0.2735 | 0.5293 | | No log | 27.0 | 108 | 1.0170 | 0.41 | 0.7321 | 2.8884 | 0.41 | 0.3468 | 0.2976 | 0.4566 | | No log | 28.0 | 112 | 1.0956 | 0.395 | 0.7464 | 3.3094 | 0.395 | 0.3255 | 0.3154 | 0.4684 | | No log | 29.0 | 116 | 1.0805 | 0.39 | 0.7544 | 3.2115 | 0.39 | 0.3193 | 0.3014 | 0.4594 | | No log | 30.0 | 120 | 1.2358 | 0.375 | 0.7733 | 4.3992 | 0.375 | 0.3058 | 0.2845 | 0.4876 | | No log | 31.0 | 124 | 1.0532 | 0.4 | 0.7458 | 2.7398 | 0.4000 | 0.3614 | 0.2890 | 0.4961 | | No log | 32.0 | 128 | 1.0166 | 0.365 | 0.7355 | 2.5093 | 0.3650 | 0.2862 | 0.2728 | 0.5057 | | No log | 33.0 | 132 | 0.9395 | 0.48 | 0.6807 | 2.6211 | 0.48 | 0.4394 | 0.2843 | 0.3719 | | No log | 34.0 | 136 | 0.8718 | 0.52 | 0.6538 | 2.6802 | 0.52 | 0.4697 | 0.2954 | 0.3051 | | No log | 35.0 | 140 | 0.8339 | 0.51 | 0.6362 | 3.1084 | 0.51 | 0.4373 | 0.2654 | 0.3006 | | No log | 36.0 | 144 | 0.8411 | 0.51 | 0.6359 | 2.7881 | 0.51 | 0.4286 | 0.2759 | 0.2906 | | No log | 37.0 | 148 | 0.8556 | 0.505 | 0.6402 | 2.5519 | 0.505 | 0.4076 | 0.2522 | 0.3060 | | No log | 38.0 | 152 | 1.0928 | 0.395 | 0.7438 | 2.8660 | 0.395 | 0.3337 | 0.2815 | 0.4724 | | No log | 39.0 | 156 | 1.3830 | 0.39 | 0.8135 | 4.7392 | 0.39 | 0.3094 | 0.2879 | 0.5239 | | No log | 40.0 | 160 | 1.2180 | 0.38 | 0.7760 | 3.8384 | 0.38 | 0.3106 | 0.2614 | 0.5109 | | No log | 41.0 | 164 | 1.1337 | 0.365 | 0.7486 | 2.8843 | 0.3650 | 0.2948 | 0.2665 | 0.4630 | | No log | 42.0 | 168 | 0.8814 | 0.53 | 0.6425 | 2.3353 | 0.53 | 0.4645 | 0.2968 | 0.2973 | | No log | 43.0 | 172 | 0.8324 | 0.515 | 0.6174 | 2.4407 | 0.515 | 0.4517 | 0.2847 | 0.2742 | | No log | 44.0 | 176 | 0.8477 | 0.53 | 0.6282 | 2.5469 | 0.53 | 0.4615 | 0.2712 | 0.2831 | | No log | 45.0 | 180 | 0.8307 | 0.515 | 0.6190 | 2.4871 | 0.515 | 0.4404 | 0.2594 | 0.2845 | | No log | 46.0 | 184 | 0.8116 | 0.53 | 0.6070 | 2.4944 | 0.53 | 0.4410 | 0.2337 | 0.2451 | | No log | 47.0 | 188 | 0.8349 | 0.54 | 0.6260 | 2.2843 | 0.54 | 0.4423 | 0.2911 | 0.2616 | | No log | 48.0 | 192 | 0.8298 | 0.555 | 0.6178 | 2.2946 | 0.555 | 0.4725 | 0.2568 | 0.2482 | | No log | 49.0 | 196 | 0.8252 | 0.565 | 0.6141 | 2.3311 | 0.565 | 0.4762 | 0.2810 | 0.2504 | | No log | 50.0 | 200 | 0.8158 | 0.565 | 0.6104 | 2.6027 | 0.565 | 0.4783 | 0.2677 | 0.2516 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hkivancoral/hushem_5x_deit_base_adamax_0001_fold5

# hushem_5x_deit_base_adamax_0001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4553 - Accuracy: 0.9024 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7557 | 1.0 | 28 | 0.4830 | 0.7805 | | 0.1392 | 2.0 | 56 | 0.3138 | 0.8293 | | 0.0115 | 3.0 | 84 | 0.3481 | 0.8537 | | 0.0078 | 4.0 | 112 | 0.3098 | 0.8537 | | 0.0013 | 5.0 | 140 | 0.3283 | 0.9024 | | 0.0023 | 6.0 | 168 | 0.5654 | 0.8537 | | 0.0004 | 7.0 | 196 | 0.4129 | 0.9024 | | 0.0003 | 8.0 | 224 | 0.4041 | 0.9024 | | 0.0002 | 9.0 | 252 | 0.4192 | 0.9024 | | 0.0002 | 10.0 | 280 | 0.4257 | 0.9024 | | 0.0002 | 11.0 | 308 | 0.4271 | 0.9024 | | 0.0002 | 12.0 | 336 | 0.4272 | 0.9024 | | 0.0001 | 13.0 | 364 | 0.4303 | 0.9024 | | 0.0002 | 14.0 | 392 | 0.4309 | 0.9024 | | 0.0001 | 15.0 | 420 | 0.4302 | 0.9024 | | 0.0001 | 16.0 | 448 | 0.4300 | 0.9024 | | 0.0001 | 17.0 | 476 | 0.4319 | 0.9024 | | 0.0001 | 18.0 | 504 | 0.4342 | 0.9024 | | 0.0001 | 19.0 | 532 | 0.4349 | 0.9024 | | 0.0001 | 20.0 | 560 | 0.4354 | 0.9024 | | 0.0001 | 21.0 | 588 | 0.4378 | 0.9024 | | 0.0001 | 22.0 | 616 | 0.4393 | 0.9024 | | 0.0001 | 23.0 | 644 | 0.4414 | 0.9024 | | 0.0001 | 24.0 | 672 | 0.4417 | 0.9024 | | 0.0001 | 25.0 | 700 | 0.4428 | 0.9024 | | 0.0001 | 26.0 | 728 | 0.4429 | 0.9024 | | 0.0001 | 27.0 | 756 | 0.4437 | 0.9024 | | 0.0001 | 28.0 | 784 | 0.4437 | 0.9024 | | 0.0001 | 29.0 | 812 | 0.4449 | 0.9024 | | 0.0001 | 30.0 | 840 | 0.4459 | 0.9024 | | 0.0001 | 31.0 | 868 | 0.4470 | 0.9024 | | 0.0001 | 32.0 | 896 | 0.4471 | 0.9024 | | 0.0001 | 33.0 | 924 | 0.4499 | 0.9024 | | 0.0001 | 34.0 | 952 | 0.4499 | 0.9024 | | 0.0001 | 35.0 | 980 | 0.4504 | 0.9024 | | 0.0001 | 36.0 | 1008 | 0.4504 | 0.9024 | | 0.0001 | 37.0 | 1036 | 0.4513 | 0.9024 | | 0.0001 | 38.0 | 1064 | 0.4525 | 0.9024 | | 0.0001 | 39.0 | 1092 | 0.4530 | 0.9024 | | 0.0001 | 40.0 | 1120 | 0.4533 | 0.9024 | | 0.0001 | 41.0 | 1148 | 0.4538 | 0.9024 | | 0.0001 | 42.0 | 1176 | 0.4539 | 0.9024 | | 0.0001 | 43.0 | 1204 | 0.4547 | 0.9024 | | 0.0001 | 44.0 | 1232 | 0.4551 | 0.9024 | | 0.0001 | 45.0 | 1260 | 0.4551 | 0.9024 | | 0.0001 | 46.0 | 1288 | 0.4551 | 0.9024 | | 0.0001 | 47.0 | 1316 | 0.4553 | 0.9024 | | 0.0001 | 48.0 | 1344 | 0.4553 | 0.9024 | | 0.0001 | 49.0 | 1372 | 0.4553 | 0.9024 | | 0.0001 | 50.0 | 1400 | 0.4553 | 0.9024 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.0_a0.5

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.0_a0.5 This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8577 - Accuracy: 0.53 - Brier Loss: 0.6406 - Nll: 2.1208 - F1 Micro: 0.53 - F1 Macro: 0.4957 - Ece: 0.3004 - Aurc: 0.3168 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 4 | 1.4267 | 0.05 | 0.9008 | 9.6592 | 0.0500 | 0.0177 | 0.1432 | 0.9439 | | No log | 2.0 | 8 | 1.4006 | 0.155 | 0.8969 | 7.9140 | 0.155 | 0.0268 | 0.2365 | 0.9603 | | No log | 3.0 | 12 | 1.4621 | 0.155 | 0.9457 | 13.3695 | 0.155 | 0.0268 | 0.3013 | 0.9107 | | No log | 4.0 | 16 | 2.1836 | 0.155 | 1.3252 | 12.8977 | 0.155 | 0.0268 | 0.6400 | 0.7514 | | No log | 5.0 | 20 | 2.4365 | 0.155 | 1.3998 | 8.4435 | 0.155 | 0.0268 | 0.7030 | 0.6102 | | No log | 6.0 | 24 | 2.1554 | 0.155 | 1.2534 | 6.9190 | 0.155 | 0.0279 | 0.5987 | 0.6271 | | No log | 7.0 | 28 | 1.5617 | 0.175 | 0.9637 | 5.7454 | 0.175 | 0.0462 | 0.3802 | 0.6485 | | No log | 8.0 | 32 | 1.3267 | 0.245 | 0.8707 | 5.2368 | 0.245 | 0.0835 | 0.2961 | 0.5438 | | No log | 9.0 | 36 | 1.2434 | 0.19 | 0.8886 | 5.0360 | 0.19 | 0.0471 | 0.3198 | 0.7720 | | No log | 10.0 | 40 | 1.0721 | 0.305 | 0.8123 | 4.5157 | 0.305 | 0.1762 | 0.2684 | 0.5269 | | No log | 11.0 | 44 | 1.1256 | 0.22 | 0.8429 | 3.9215 | 0.22 | 0.1083 | 0.2812 | 0.7346 | | No log | 12.0 | 48 | 0.9865 | 0.35 | 0.7676 | 3.4553 | 0.35 | 0.2565 | 0.2884 | 0.4790 | | No log | 13.0 | 52 | 1.0206 | 0.355 | 0.7899 | 3.3582 | 0.3550 | 0.2278 | 0.2954 | 0.5883 | | No log | 14.0 | 56 | 0.9096 | 0.415 | 0.6994 | 3.2174 | 0.415 | 0.3147 | 0.2563 | 0.3596 | | No log | 15.0 | 60 | 0.9187 | 0.415 | 0.7129 | 3.2059 | 0.415 | 0.2742 | 0.2941 | 0.3971 | | No log | 16.0 | 64 | 0.8905 | 0.395 | 0.6956 | 2.9931 | 0.395 | 0.2618 | 0.2590 | 0.3826 | | No log | 17.0 | 68 | 0.9108 | 0.425 | 0.7073 | 3.1634 | 0.425 | 0.2855 | 0.2995 | 0.3685 | | No log | 18.0 | 72 | 0.8769 | 0.465 | 0.6706 | 3.1088 | 0.465 | 0.3652 | 0.2855 | 0.3261 | | No log | 19.0 | 76 | 0.8585 | 0.475 | 0.6687 | 2.8710 | 0.4750 | 0.3884 | 0.2916 | 0.3282 | | No log | 20.0 | 80 | 0.9822 | 0.405 | 0.7378 | 2.8889 | 0.405 | 0.3570 | 0.2850 | 0.4895 | | No log | 21.0 | 84 | 0.9324 | 0.445 | 0.6992 | 2.7975 | 0.445 | 0.3553 | 0.3021 | 0.3762 | | No log | 22.0 | 88 | 1.0330 | 0.42 | 0.7350 | 2.7487 | 0.4200 | 0.3506 | 0.2984 | 0.4771 | | No log | 23.0 | 92 | 0.8755 | 0.455 | 0.6674 | 2.5903 | 0.455 | 0.3415 | 0.2570 | 0.3352 | | No log | 24.0 | 96 | 0.8651 | 0.47 | 0.6443 | 2.8456 | 0.47 | 0.3800 | 0.2451 | 0.2975 | | No log | 25.0 | 100 | 0.9567 | 0.445 | 0.7150 | 2.7083 | 0.445 | 0.3727 | 0.2667 | 0.4676 | | No log | 26.0 | 104 | 1.0224 | 0.42 | 0.7376 | 2.4408 | 0.4200 | 0.3367 | 0.2968 | 0.5019 | | No log | 27.0 | 108 | 0.8365 | 0.525 | 0.6407 | 2.6426 | 0.525 | 0.4496 | 0.2960 | 0.2657 | | No log | 28.0 | 112 | 0.9798 | 0.425 | 0.7287 | 2.6379 | 0.425 | 0.3489 | 0.2640 | 0.4668 | | No log | 29.0 | 116 | 0.9226 | 0.44 | 0.6965 | 2.5748 | 0.44 | 0.3669 | 0.2561 | 0.4054 | | No log | 30.0 | 120 | 0.8303 | 0.49 | 0.6398 | 2.4839 | 0.49 | 0.3924 | 0.2981 | 0.2936 | | No log | 31.0 | 124 | 0.8426 | 0.52 | 0.6478 | 2.5282 | 0.52 | 0.4322 | 0.3109 | 0.3084 | | No log | 32.0 | 128 | 0.9111 | 0.45 | 0.6970 | 2.3870 | 0.45 | 0.3947 | 0.2837 | 0.4448 | | No log | 33.0 | 132 | 0.8723 | 0.51 | 0.6524 | 2.6124 | 0.51 | 0.4170 | 0.2536 | 0.3365 | | No log | 34.0 | 136 | 0.8936 | 0.47 | 0.6671 | 2.8892 | 0.47 | 0.3814 | 0.2436 | 0.3357 | | No log | 35.0 | 140 | 1.2870 | 0.42 | 0.7660 | 4.4020 | 0.4200 | 0.3468 | 0.2860 | 0.4606 | | No log | 36.0 | 144 | 0.9991 | 0.455 | 0.7289 | 2.6973 | 0.455 | 0.4132 | 0.3272 | 0.4684 | | No log | 37.0 | 148 | 1.6352 | 0.365 | 0.8356 | 4.7695 | 0.3650 | 0.3020 | 0.3312 | 0.6069 | | No log | 38.0 | 152 | 1.3014 | 0.39 | 0.8213 | 2.9436 | 0.39 | 0.3382 | 0.3262 | 0.5476 | | No log | 39.0 | 156 | 1.0294 | 0.415 | 0.7361 | 2.7188 | 0.415 | 0.3446 | 0.2454 | 0.4632 | | No log | 40.0 | 160 | 0.8825 | 0.52 | 0.6538 | 2.3887 | 0.52 | 0.4608 | 0.2721 | 0.3186 | | No log | 41.0 | 164 | 0.8572 | 0.54 | 0.6288 | 2.4201 | 0.54 | 0.4822 | 0.2963 | 0.2899 | | No log | 42.0 | 168 | 0.8393 | 0.535 | 0.6291 | 2.3587 | 0.535 | 0.4726 | 0.2824 | 0.2937 | | No log | 43.0 | 172 | 0.8369 | 0.515 | 0.6303 | 2.4060 | 0.515 | 0.4583 | 0.2689 | 0.2903 | | No log | 44.0 | 176 | 0.8458 | 0.49 | 0.6346 | 2.3323 | 0.49 | 0.4428 | 0.2526 | 0.2951 | | No log | 45.0 | 180 | 0.8446 | 0.49 | 0.6367 | 2.2207 | 0.49 | 0.4289 | 0.2655 | 0.3041 | | No log | 46.0 | 184 | 0.8324 | 0.54 | 0.6289 | 2.3685 | 0.54 | 0.4779 | 0.2571 | 0.2873 | | No log | 47.0 | 188 | 0.8658 | 0.515 | 0.6486 | 2.3922 | 0.515 | 0.4584 | 0.2623 | 0.3100 | | No log | 48.0 | 192 | 0.8516 | 0.525 | 0.6410 | 2.4448 | 0.525 | 0.4700 | 0.3006 | 0.3044 | | No log | 49.0 | 196 | 0.8520 | 0.55 | 0.6350 | 2.2049 | 0.55 | 0.4947 | 0.3030 | 0.2980 | | No log | 50.0 | 200 | 0.8577 | 0.53 | 0.6406 | 2.1208 | 0.53 | 0.4957 | 0.3004 | 0.3168 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

# resnet101-base_tobacco-cnn_tobacco3482_kd_MSE This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1315 - Accuracy: 0.365 - Brier Loss: 0.7313 - Nll: 5.5846 - F1 Micro: 0.3650 - F1 Macro: 0.2369 - Ece: 0.2526 - Aurc: 0.4412 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 4 | 1.1507 | 0.1 | 0.8998 | 9.9508 | 0.1000 | 0.0462 | 0.1728 | 0.9208 | | No log | 2.0 | 8 | 0.9900 | 0.155 | 0.8924 | 9.6289 | 0.155 | 0.0268 | 0.2400 | 0.9571 | | No log | 3.0 | 12 | 0.8441 | 0.155 | 0.9273 | 8.9944 | 0.155 | 0.0268 | 0.3276 | 0.9345 | | No log | 4.0 | 16 | 1.4048 | 0.155 | 1.3149 | 8.9869 | 0.155 | 0.0268 | 0.6569 | 0.6091 | | No log | 5.0 | 20 | 1.0761 | 0.155 | 1.1553 | 8.9185 | 0.155 | 0.0272 | 0.5441 | 0.6112 | | No log | 6.0 | 24 | 1.1745 | 0.155 | 1.1386 | 9.2644 | 0.155 | 0.0304 | 0.4982 | 0.6120 | | No log | 7.0 | 28 | 0.4686 | 0.225 | 0.8829 | 7.3879 | 0.225 | 0.0724 | 0.3173 | 0.5804 | | No log | 8.0 | 32 | 0.3535 | 0.24 | 0.8393 | 7.0880 | 0.24 | 0.0797 | 0.2963 | 0.5518 | | No log | 9.0 | 36 | 0.2519 | 0.295 | 0.8157 | 6.6738 | 0.295 | 0.1375 | 0.2944 | 0.4810 | | No log | 10.0 | 40 | 0.2957 | 0.265 | 0.8432 | 6.8903 | 0.265 | 0.1030 | 0.3171 | 0.5807 | | No log | 11.0 | 44 | 0.5224 | 0.21 | 0.8832 | 8.6128 | 0.2100 | 0.0987 | 0.2948 | 0.6814 | | No log | 12.0 | 48 | 0.4088 | 0.18 | 0.8807 | 7.0533 | 0.18 | 0.0309 | 0.2966 | 0.7466 | | No log | 13.0 | 52 | 0.5082 | 0.225 | 0.8732 | 8.3126 | 0.225 | 0.0606 | 0.2761 | 0.7285 | | No log | 14.0 | 56 | 0.5253 | 0.18 | 0.8905 | 8.3229 | 0.18 | 0.0305 | 0.2973 | 0.7838 | | No log | 15.0 | 60 | 0.5612 | 0.225 | 0.8579 | 7.9410 | 0.225 | 0.0642 | 0.2690 | 0.7108 | | No log | 16.0 | 64 | 0.2805 | 0.28 | 0.8094 | 6.0275 | 0.28 | 0.1475 | 0.2633 | 0.5701 | | No log | 17.0 | 68 | 0.3076 | 0.32 | 0.8151 | 6.1462 | 0.32 | 0.1641 | 0.2852 | 0.6162 | | No log | 18.0 | 72 | 0.3824 | 0.29 | 0.8072 | 6.0214 | 0.29 | 0.1681 | 0.2900 | 0.6048 | | No log | 19.0 | 76 | 0.5089 | 0.19 | 0.8701 | 8.9391 | 0.19 | 0.0418 | 0.2582 | 0.7152 | | No log | 20.0 | 80 | 0.1490 | 0.335 | 0.7347 | 5.7349 | 0.335 | 0.1786 | 0.2500 | 0.4430 | | No log | 21.0 | 84 | 0.3448 | 0.255 | 0.8455 | 6.6598 | 0.255 | 0.0998 | 0.3124 | 0.7183 | | No log | 22.0 | 88 | 0.6254 | 0.22 | 0.8413 | 6.9926 | 0.22 | 0.0966 | 0.2654 | 0.7197 | | No log | 23.0 | 92 | 0.5464 | 0.215 | 0.8909 | 8.4952 | 0.2150 | 0.0570 | 0.2931 | 0.7084 | | No log | 24.0 | 96 | 0.4465 | 0.24 | 0.8445 | 7.2319 | 0.24 | 0.1396 | 0.2575 | 0.6667 | | No log | 25.0 | 100 | 0.3967 | 0.215 | 0.8547 | 6.7234 | 0.2150 | 0.0962 | 0.2913 | 0.7053 | | No log | 26.0 | 104 | 0.2459 | 0.295 | 0.8041 | 5.1627 | 0.295 | 0.1901 | 0.2525 | 0.6590 | | No log | 27.0 | 108 | 0.4125 | 0.19 | 0.8595 | 7.1181 | 0.19 | 0.0551 | 0.2707 | 0.7087 | | No log | 28.0 | 112 | 0.1686 | 0.36 | 0.7309 | 5.1322 | 0.36 | 0.2178 | 0.2296 | 0.4432 | | No log | 29.0 | 116 | 0.3573 | 0.205 | 0.8664 | 6.6815 | 0.205 | 0.0523 | 0.2753 | 0.7131 | | No log | 30.0 | 120 | 0.1634 | 0.32 | 0.7416 | 5.6798 | 0.32 | 0.1862 | 0.2473 | 0.4616 | | No log | 31.0 | 124 | 0.1404 | 0.35 | 0.7295 | 5.6538 | 0.35 | 0.2152 | 0.2688 | 0.4389 | | No log | 32.0 | 128 | 0.1435 | 0.325 | 0.7415 | 5.5376 | 0.325 | 0.1439 | 0.2567 | 0.4489 | | No log | 33.0 | 132 | 0.1428 | 0.33 | 0.7292 | 5.5151 | 0.33 | 0.1791 | 0.2502 | 0.4403 | | No log | 34.0 | 136 | 0.1602 | 0.33 | 0.7371 | 5.8829 | 0.33 | 0.1941 | 0.2542 | 0.4481 | | No log | 35.0 | 140 | 0.1663 | 0.325 | 0.7398 | 5.6501 | 0.325 | 0.1880 | 0.2443 | 0.4564 | | No log | 36.0 | 144 | 0.1637 | 0.35 | 0.7422 | 5.9440 | 0.35 | 0.2053 | 0.2748 | 0.4361 | | No log | 37.0 | 148 | 0.1520 | 0.325 | 0.7317 | 5.3284 | 0.325 | 0.1787 | 0.2677 | 0.4531 | | No log | 38.0 | 152 | 0.1585 | 0.335 | 0.7385 | 5.9712 | 0.335 | 0.1939 | 0.2648 | 0.4483 | | No log | 39.0 | 156 | 0.1491 | 0.335 | 0.7334 | 5.6729 | 0.335 | 0.1912 | 0.2533 | 0.4404 | | No log | 40.0 | 160 | 0.1367 | 0.32 | 0.7297 | 5.7350 | 0.32 | 0.1818 | 0.2512 | 0.4498 | | No log | 41.0 | 164 | 0.2089 | 0.335 | 0.7583 | 5.2150 | 0.335 | 0.2073 | 0.2822 | 0.4712 | | No log | 42.0 | 168 | 0.1612 | 0.335 | 0.7323 | 4.9145 | 0.335 | 0.2058 | 0.2696 | 0.4482 | | No log | 43.0 | 172 | 0.1616 | 0.335 | 0.7349 | 5.4305 | 0.335 | 0.1916 | 0.2650 | 0.4493 | | No log | 44.0 | 176 | 0.1477 | 0.335 | 0.7335 | 5.3482 | 0.335 | 0.1761 | 0.2478 | 0.4410 | | No log | 45.0 | 180 | 0.1426 | 0.34 | 0.7321 | 5.4265 | 0.34 | 0.2018 | 0.2307 | 0.4483 | | No log | 46.0 | 184 | 0.1531 | 0.345 | 0.7351 | 5.2269 | 0.345 | 0.2108 | 0.2812 | 0.4572 | | No log | 47.0 | 188 | 0.1426 | 0.34 | 0.7299 | 5.1412 | 0.34 | 0.2040 | 0.2418 | 0.4443 | | No log | 48.0 | 192 | 0.1321 | 0.335 | 0.7353 | 5.2955 | 0.335 | 0.2017 | 0.2515 | 0.4547 | | No log | 49.0 | 196 | 0.1330 | 0.34 | 0.7332 | 5.5391 | 0.34 | 0.2065 | 0.2485 | 0.4524 | | No log | 50.0 | 200 | 0.1315 | 0.365 | 0.7313 | 5.5846 | 0.3650 | 0.2369 | 0.2526 | 0.4412 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_kd_NKD_t1.0_g1.5

# resnet101-base_tobacco-cnn_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2266 - Accuracy: 0.385 - Brier Loss: 0.7374 - Nll: 4.0859 - F1 Micro: 0.3850 - F1 Macro: 0.2652 - Ece: 0.2858 - Aurc: 0.4261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 4 | 3.9610 | 0.05 | 0.9004 | 9.1922 | 0.0500 | 0.0100 | 0.1457 | 0.9423 | | No log | 2.0 | 8 | 3.8752 | 0.155 | 0.8934 | 8.5838 | 0.155 | 0.0268 | 0.2356 | 0.9630 | | No log | 3.0 | 12 | 3.8665 | 0.155 | 0.9261 | 8.8645 | 0.155 | 0.0268 | 0.3113 | 0.7263 | | No log | 4.0 | 16 | 5.0552 | 0.155 | 1.3190 | 8.8736 | 0.155 | 0.0268 | 0.6667 | 0.6226 | | No log | 5.0 | 20 | 4.9755 | 0.155 | 1.2873 | 8.9603 | 0.155 | 0.0270 | 0.6315 | 0.6033 | | No log | 6.0 | 24 | 4.6069 | 0.155 | 1.1443 | 7.0637 | 0.155 | 0.0301 | 0.5057 | 0.6065 | | No log | 7.0 | 28 | 3.7058 | 0.22 | 0.9193 | 6.6528 | 0.22 | 0.0685 | 0.3454 | 0.5737 | | No log | 8.0 | 32 | 3.3000 | 0.25 | 0.8140 | 6.8642 | 0.25 | 0.1011 | 0.2638 | 0.5377 | | No log | 9.0 | 36 | 3.3805 | 0.195 | 0.8768 | 6.5108 | 0.195 | 0.0779 | 0.2955 | 0.7532 | | No log | 10.0 | 40 | 3.4626 | 0.2 | 0.8985 | 6.3933 | 0.2000 | 0.0745 | 0.3154 | 0.7384 | | No log | 11.0 | 44 | 3.2088 | 0.32 | 0.7621 | 6.0433 | 0.32 | 0.1695 | 0.2375 | 0.4457 | | No log | 12.0 | 48 | 3.4543 | 0.22 | 0.8720 | 6.1413 | 0.22 | 0.1065 | 0.3144 | 0.7026 | | No log | 13.0 | 52 | 3.5300 | 0.225 | 0.8684 | 7.0938 | 0.225 | 0.1182 | 0.2747 | 0.7110 | | No log | 14.0 | 56 | 3.5981 | 0.215 | 0.8821 | 7.5146 | 0.2150 | 0.0978 | 0.3047 | 0.7351 | | No log | 15.0 | 60 | 3.5641 | 0.23 | 0.8895 | 7.7554 | 0.23 | 0.0944 | 0.2985 | 0.7568 | | No log | 16.0 | 64 | 3.5853 | 0.235 | 0.8698 | 6.6949 | 0.235 | 0.1292 | 0.2634 | 0.6518 | | No log | 17.0 | 68 | 3.5539 | 0.255 | 0.8597 | 7.5062 | 0.255 | 0.1331 | 0.2821 | 0.6332 | | No log | 18.0 | 72 | 3.5725 | 0.265 | 0.8569 | 7.4117 | 0.265 | 0.1396 | 0.2708 | 0.5940 | | No log | 19.0 | 76 | 3.5207 | 0.27 | 0.8415 | 6.5482 | 0.27 | 0.1542 | 0.2592 | 0.5619 | | No log | 20.0 | 80 | 3.5360 | 0.26 | 0.8573 | 7.4207 | 0.26 | 0.1358 | 0.2942 | 0.5949 | | No log | 21.0 | 84 | 3.2807 | 0.345 | 0.7933 | 4.8232 | 0.345 | 0.2077 | 0.2903 | 0.5385 | | No log | 22.0 | 88 | 3.1633 | 0.39 | 0.7217 | 4.3843 | 0.39 | 0.2417 | 0.2547 | 0.3857 | | No log | 23.0 | 92 | 3.2159 | 0.39 | 0.7463 | 4.4691 | 0.39 | 0.2481 | 0.2923 | 0.3756 | | No log | 24.0 | 96 | 3.1650 | 0.375 | 0.7248 | 4.4043 | 0.375 | 0.2276 | 0.2433 | 0.3809 | | No log | 25.0 | 100 | 3.2000 | 0.375 | 0.7470 | 4.7004 | 0.375 | 0.2473 | 0.2671 | 0.4264 | | No log | 26.0 | 104 | 3.4356 | 0.27 | 0.8326 | 6.6479 | 0.27 | 0.1466 | 0.2636 | 0.5640 | | No log | 27.0 | 108 | 3.5761 | 0.285 | 0.8347 | 6.5689 | 0.285 | 0.1796 | 0.2537 | 0.6182 | | No log | 28.0 | 112 | 3.5778 | 0.26 | 0.8546 | 7.0753 | 0.26 | 0.1380 | 0.2629 | 0.5870 | | No log | 29.0 | 116 | 3.1280 | 0.39 | 0.7075 | 4.5179 | 0.39 | 0.2450 | 0.2479 | 0.3759 | | No log | 30.0 | 120 | 3.1559 | 0.37 | 0.7268 | 4.3444 | 0.37 | 0.2413 | 0.2588 | 0.3941 | | No log | 31.0 | 124 | 3.1493 | 0.39 | 0.7133 | 4.6188 | 0.39 | 0.2305 | 0.2338 | 0.3686 | | No log | 32.0 | 128 | 3.1287 | 0.39 | 0.7015 | 4.0848 | 0.39 | 0.2379 | 0.2271 | 0.3655 | | No log | 33.0 | 132 | 3.1409 | 0.395 | 0.7048 | 4.0026 | 0.395 | 0.2290 | 0.2210 | 0.3606 | | No log | 34.0 | 136 | 3.1691 | 0.375 | 0.7210 | 4.4086 | 0.375 | 0.2215 | 0.2495 | 0.3800 | | No log | 35.0 | 140 | 3.1529 | 0.4 | 0.7117 | 4.1376 | 0.4000 | 0.2487 | 0.2200 | 0.3605 | | No log | 36.0 | 144 | 3.1088 | 0.4 | 0.6989 | 4.0773 | 0.4000 | 0.2645 | 0.2478 | 0.3641 | | No log | 37.0 | 148 | 3.2158 | 0.4 | 0.7230 | 4.1145 | 0.4000 | 0.2603 | 0.2517 | 0.3761 | | No log | 38.0 | 152 | 3.1351 | 0.39 | 0.7064 | 4.3952 | 0.39 | 0.2398 | 0.2475 | 0.3606 | | No log | 39.0 | 156 | 3.1239 | 0.395 | 0.7001 | 4.0496 | 0.395 | 0.2569 | 0.2364 | 0.3583 | | No log | 40.0 | 160 | 3.1855 | 0.385 | 0.7169 | 4.0634 | 0.3850 | 0.2274 | 0.2467 | 0.3687 | | No log | 41.0 | 164 | 3.1938 | 0.37 | 0.7098 | 3.9505 | 0.37 | 0.2146 | 0.2207 | 0.3781 | | No log | 42.0 | 168 | 3.3495 | 0.395 | 0.7438 | 4.0247 | 0.395 | 0.2428 | 0.2901 | 0.3973 | | No log | 43.0 | 172 | 3.2352 | 0.395 | 0.7115 | 3.9875 | 0.395 | 0.2431 | 0.2651 | 0.3790 | | No log | 44.0 | 176 | 3.2838 | 0.39 | 0.7223 | 3.8867 | 0.39 | 0.2246 | 0.2590 | 0.3824 | | No log | 45.0 | 180 | 3.3175 | 0.395 | 0.7304 | 4.2165 | 0.395 | 0.2286 | 0.2549 | 0.3811 | | No log | 46.0 | 184 | 3.1183 | 0.395 | 0.6916 | 3.9786 | 0.395 | 0.2338 | 0.2345 | 0.3581 | | No log | 47.0 | 188 | 3.1608 | 0.395 | 0.7049 | 3.7245 | 0.395 | 0.2580 | 0.2429 | 0.3668 | | No log | 48.0 | 192 | 3.2144 | 0.38 | 0.7316 | 3.9593 | 0.38 | 0.2512 | 0.2517 | 0.4202 | | No log | 49.0 | 196 | 3.2781 | 0.365 | 0.7561 | 3.9721 | 0.3650 | 0.2440 | 0.2429 | 0.4654 | | No log | 50.0 | 200 | 3.2266 | 0.385 | 0.7374 | 4.0859 | 0.3850 | 0.2652 | 0.2858 | 0.4261 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_hint

# resnet101-base_tobacco-cnn_tobacco3482_hint This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 24.6607 - Accuracy: 0.57 - Brier Loss: 0.6012 - Nll: 2.9238 - F1 Micro: 0.57 - F1 Macro: 0.5344 - Ece: 0.2496 - Aurc: 0.2274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 27.2504 | 0.07 | 0.9006 | 8.5876 | 0.07 | 0.0131 | 0.1634 | 0.9646 | | No log | 2.0 | 14 | 27.1186 | 0.155 | 0.9229 | 12.2960 | 0.155 | 0.0268 | 0.2967 | 0.8769 | | No log | 3.0 | 21 | 27.9163 | 0.155 | 1.3722 | 11.2040 | 0.155 | 0.0268 | 0.6887 | 0.5963 | | No log | 4.0 | 28 | 28.2724 | 0.155 | 1.4334 | 9.3615 | 0.155 | 0.0273 | 0.7029 | 0.6185 | | No log | 5.0 | 35 | 26.9699 | 0.175 | 1.0316 | 5.3928 | 0.175 | 0.0465 | 0.4168 | 0.5989 | | No log | 6.0 | 42 | 26.1797 | 0.23 | 0.8746 | 3.9558 | 0.23 | 0.0993 | 0.3120 | 0.5627 | | No log | 7.0 | 49 | 25.8507 | 0.25 | 0.8299 | 3.2357 | 0.25 | 0.1721 | 0.2686 | 0.6618 | | No log | 8.0 | 56 | 25.7515 | 0.24 | 0.8336 | 2.7738 | 0.24 | 0.1579 | 0.2670 | 0.6619 | | No log | 9.0 | 63 | 25.3041 | 0.39 | 0.7346 | 2.5881 | 0.39 | 0.2914 | 0.2649 | 0.4362 | | No log | 10.0 | 70 | 25.1996 | 0.375 | 0.7406 | 2.7338 | 0.375 | 0.2616 | 0.2903 | 0.4923 | | No log | 11.0 | 77 | 25.0418 | 0.44 | 0.6756 | 3.2534 | 0.44 | 0.3173 | 0.2520 | 0.3197 | | No log | 12.0 | 84 | 25.3664 | 0.35 | 0.8231 | 3.6209 | 0.35 | 0.2628 | 0.2924 | 0.5484 | | No log | 13.0 | 91 | 25.0353 | 0.44 | 0.6927 | 3.5523 | 0.44 | 0.3230 | 0.2842 | 0.3332 | | No log | 14.0 | 98 | 25.2980 | 0.36 | 0.8265 | 3.3953 | 0.36 | 0.2859 | 0.3158 | 0.5347 | | No log | 15.0 | 105 | 24.8521 | 0.425 | 0.6604 | 3.0888 | 0.425 | 0.3379 | 0.2641 | 0.3096 | | No log | 16.0 | 112 | 24.8368 | 0.46 | 0.6622 | 2.7863 | 0.46 | 0.3626 | 0.2771 | 0.3429 | | No log | 17.0 | 119 | 25.0490 | 0.355 | 0.7909 | 2.9342 | 0.3550 | 0.2764 | 0.3300 | 0.5313 | | No log | 18.0 | 126 | 24.9950 | 0.4 | 0.7521 | 3.5010 | 0.4000 | 0.3467 | 0.2801 | 0.4721 | | No log | 19.0 | 133 | 24.7232 | 0.505 | 0.6259 | 2.9709 | 0.505 | 0.4017 | 0.2799 | 0.2807 | | No log | 20.0 | 140 | 24.7500 | 0.5 | 0.6408 | 3.1274 | 0.5 | 0.4278 | 0.2398 | 0.2752 | | No log | 21.0 | 147 | 24.5976 | 0.54 | 0.5922 | 2.7847 | 0.54 | 0.4872 | 0.2422 | 0.2319 | | No log | 22.0 | 154 | 24.9329 | 0.42 | 0.7518 | 2.9924 | 0.4200 | 0.3777 | 0.3094 | 0.4446 | | No log | 23.0 | 161 | 24.6088 | 0.535 | 0.6089 | 2.8494 | 0.535 | 0.5067 | 0.2756 | 0.2770 | | No log | 24.0 | 168 | 25.1851 | 0.39 | 0.8175 | 3.7625 | 0.39 | 0.3513 | 0.3211 | 0.5049 | | No log | 25.0 | 175 | 24.5058 | 0.585 | 0.5754 | 2.6524 | 0.585 | 0.5707 | 0.2296 | 0.2227 | | No log | 26.0 | 182 | 25.2073 | 0.435 | 0.7812 | 3.0365 | 0.435 | 0.3839 | 0.3190 | 0.5012 | | No log | 27.0 | 189 | 24.7752 | 0.54 | 0.6558 | 2.9071 | 0.54 | 0.4667 | 0.2669 | 0.2898 | | No log | 28.0 | 196 | 24.8546 | 0.515 | 0.6697 | 2.5989 | 0.515 | 0.4397 | 0.2943 | 0.3817 | | No log | 29.0 | 203 | 24.5759 | 0.56 | 0.5969 | 2.6234 | 0.56 | 0.5342 | 0.2609 | 0.2493 | | No log | 30.0 | 210 | 24.7052 | 0.53 | 0.6198 | 2.9462 | 0.53 | 0.4811 | 0.2779 | 0.2766 | | No log | 31.0 | 217 | 24.5828 | 0.545 | 0.6038 | 2.7967 | 0.545 | 0.4979 | 0.2455 | 0.2369 | | No log | 32.0 | 224 | 24.6622 | 0.545 | 0.6220 | 2.8878 | 0.545 | 0.4925 | 0.2854 | 0.2682 | | No log | 33.0 | 231 | 24.6253 | 0.57 | 0.5991 | 3.1607 | 0.57 | 0.5327 | 0.2869 | 0.2518 | | No log | 34.0 | 238 | 24.6230 | 0.535 | 0.6351 | 2.5626 | 0.535 | 0.5245 | 0.2766 | 0.3077 | | No log | 35.0 | 245 | 24.5803 | 0.59 | 0.5900 | 2.8215 | 0.59 | 0.5564 | 0.2724 | 0.2563 | | No log | 36.0 | 252 | 24.5679 | 0.57 | 0.5709 | 3.1573 | 0.57 | 0.5089 | 0.2523 | 0.2222 | | No log | 37.0 | 259 | 24.5375 | 0.575 | 0.5631 | 2.9349 | 0.575 | 0.5381 | 0.2279 | 0.2007 | | No log | 38.0 | 266 | 24.6423 | 0.565 | 0.6072 | 2.6772 | 0.565 | 0.5340 | 0.2587 | 0.2247 | | No log | 39.0 | 273 | 24.6706 | 0.575 | 0.6139 | 2.9241 | 0.575 | 0.5291 | 0.2318 | 0.2416 | | No log | 40.0 | 280 | 24.6007 | 0.575 | 0.5774 | 2.9918 | 0.575 | 0.5323 | 0.2575 | 0.2138 | | No log | 41.0 | 287 | 24.7587 | 0.565 | 0.6231 | 2.9588 | 0.565 | 0.5023 | 0.2685 | 0.2665 | | No log | 42.0 | 294 | 24.5681 | 0.56 | 0.5786 | 2.9999 | 0.56 | 0.5153 | 0.2558 | 0.2093 | | No log | 43.0 | 301 | 24.5971 | 0.59 | 0.5687 | 3.0595 | 0.59 | 0.5365 | 0.2532 | 0.2004 | | No log | 44.0 | 308 | 24.6424 | 0.58 | 0.5918 | 2.9073 | 0.58 | 0.5432 | 0.2470 | 0.2113 | | No log | 45.0 | 315 | 24.5998 | 0.58 | 0.5705 | 3.0442 | 0.58 | 0.5488 | 0.2769 | 0.2011 | | No log | 46.0 | 322 | 24.5625 | 0.62 | 0.5561 | 2.9855 | 0.62 | 0.5869 | 0.2492 | 0.2069 | | No log | 47.0 | 329 | 24.6409 | 0.57 | 0.5817 | 2.8587 | 0.57 | 0.5400 | 0.2480 | 0.2239 | | No log | 48.0 | 336 | 24.6218 | 0.57 | 0.5958 | 2.8299 | 0.57 | 0.5426 | 0.2725 | 0.2251 | | No log | 49.0 | 343 | 24.5568 | 0.585 | 0.5762 | 2.9178 | 0.585 | 0.5590 | 0.2374 | 0.2102 | | No log | 50.0 | 350 | 24.6607 | 0.57 | 0.6012 | 2.9238 | 0.57 | 0.5344 | 0.2496 | 0.2274 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_simkd

# resnet101-base_tobacco-cnn_tobacco3482_simkd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 13.1229 - Accuracy: 0.295 - Brier Loss: 0.7636 - Nll: 6.8757 - F1 Micro: 0.295 - F1 Macro: 0.1150 - Ece: 0.2446 - Aurc: 0.4919 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 0.2512 | 0.18 | 0.9617 | 7.0686 | 0.18 | 0.0305 | 0.3439 | 0.7810 | | No log | 2.0 | 14 | 0.3629 | 0.18 | 1.0943 | 7.0153 | 0.18 | 0.0305 | 0.4345 | 0.8186 | | No log | 3.0 | 21 | 0.4745 | 0.18 | 1.1577 | 6.9805 | 0.18 | 0.0305 | 0.5034 | 0.8029 | | No log | 4.0 | 28 | 0.6953 | 0.18 | 1.1290 | 6.9352 | 0.18 | 0.0305 | 0.4731 | 0.8367 | | No log | 5.0 | 35 | 173.4450 | 0.18 | 1.1346 | 6.8314 | 0.18 | 0.0305 | 0.4615 | 0.8814 | | No log | 6.0 | 42 | 412.7549 | 0.18 | 1.1098 | 6.8364 | 0.18 | 0.0305 | 0.4420 | 0.8716 | | No log | 7.0 | 49 | 148.0839 | 0.18 | 1.0291 | 6.9271 | 0.18 | 0.0305 | 0.3960 | 0.7698 | | No log | 8.0 | 56 | 61.2696 | 0.18 | 0.9674 | 6.9593 | 0.18 | 0.0305 | 0.3413 | 0.7924 | | No log | 9.0 | 63 | 175.4512 | 0.18 | 0.9708 | 6.9854 | 0.18 | 0.0305 | 0.3549 | 0.8252 | | No log | 10.0 | 70 | 139.2036 | 0.18 | 0.9400 | 6.9022 | 0.18 | 0.0305 | 0.3300 | 0.7760 | | No log | 11.0 | 77 | 12.5605 | 0.295 | 0.8656 | 6.9766 | 0.295 | 0.1138 | 0.3093 | 0.5354 | | No log | 12.0 | 84 | 2.3147 | 0.18 | 0.9363 | 6.9778 | 0.18 | 0.0305 | 0.3084 | 0.7507 | | No log | 13.0 | 91 | 75.2050 | 0.18 | 0.9543 | 9.1566 | 0.18 | 0.0305 | 0.2990 | 0.7716 | | No log | 14.0 | 98 | 37.4873 | 0.18 | 0.9410 | 9.1473 | 0.18 | 0.0305 | 0.3029 | 0.7517 | | No log | 15.0 | 105 | 8.5750 | 0.18 | 0.9304 | 9.1440 | 0.18 | 0.0305 | 0.3033 | 0.7718 | | No log | 16.0 | 112 | 21.5310 | 0.18 | 0.9232 | 9.1349 | 0.18 | 0.0305 | 0.3122 | 0.7717 | | No log | 17.0 | 119 | 66.9546 | 0.18 | 0.9287 | 9.1376 | 0.18 | 0.0305 | 0.2920 | 0.7715 | | No log | 18.0 | 126 | 2.6525 | 0.285 | 0.8357 | 7.0773 | 0.285 | 0.1143 | 0.3156 | 0.5306 | | No log | 19.0 | 133 | 7.7253 | 0.24 | 0.8574 | 7.0190 | 0.24 | 0.0880 | 0.2948 | 0.7186 | | No log | 20.0 | 140 | 30.0305 | 0.285 | 0.8086 | 6.9862 | 0.285 | 0.1133 | 0.3001 | 0.5273 | | No log | 21.0 | 147 | 3.9243 | 0.18 | 0.8680 | 7.4799 | 0.18 | 0.0306 | 0.2739 | 0.7704 | | No log | 22.0 | 154 | 4.4660 | 0.18 | 0.8831 | 8.9935 | 0.18 | 0.0308 | 0.2652 | 0.7313 | | No log | 23.0 | 161 | 3.9728 | 0.18 | 0.8719 | 8.9609 | 0.18 | 0.0308 | 0.2600 | 0.7651 | | No log | 24.0 | 168 | 2.6913 | 0.285 | 0.8089 | 6.9969 | 0.285 | 0.1146 | 0.2873 | 0.5122 | | No log | 25.0 | 175 | 1.3141 | 0.29 | 0.8086 | 7.0227 | 0.29 | 0.1156 | 0.3154 | 0.5256 | | No log | 26.0 | 182 | 13.5853 | 0.29 | 0.7782 | 6.8763 | 0.29 | 0.1168 | 0.2735 | 0.5045 | | No log | 27.0 | 189 | 11.9763 | 0.3 | 0.7730 | 6.8499 | 0.3 | 0.1171 | 0.2740 | 0.4971 | | No log | 28.0 | 196 | 1.6467 | 0.285 | 0.8067 | 7.1641 | 0.285 | 0.1144 | 0.2870 | 0.5193 | | No log | 29.0 | 203 | 30.5306 | 0.285 | 0.8424 | 7.1576 | 0.285 | 0.1129 | 0.2686 | 0.6662 | | No log | 30.0 | 210 | 13.5964 | 0.18 | 0.8584 | 7.0972 | 0.18 | 0.0305 | 0.2704 | 0.7307 | | No log | 31.0 | 217 | 98.3061 | 0.29 | 0.8274 | 7.0330 | 0.29 | 0.1167 | 0.3163 | 0.5653 | | No log | 32.0 | 224 | 53.0911 | 0.29 | 0.7984 | 6.9311 | 0.29 | 0.1167 | 0.2911 | 0.5181 | | No log | 33.0 | 231 | 2.2010 | 0.265 | 0.8291 | 6.9883 | 0.265 | 0.1037 | 0.2945 | 0.6039 | | No log | 34.0 | 238 | 3.6255 | 0.295 | 0.7836 | 6.8954 | 0.295 | 0.1176 | 0.2636 | 0.5025 | | No log | 35.0 | 245 | 0.9640 | 0.3 | 0.7571 | 6.7913 | 0.3 | 0.1170 | 0.2388 | 0.4746 | | No log | 36.0 | 252 | 1.1935 | 0.295 | 0.7711 | 6.7993 | 0.295 | 0.1175 | 0.2619 | 0.4779 | | No log | 37.0 | 259 | 12.7465 | 0.305 | 0.7650 | 6.8142 | 0.305 | 0.1205 | 0.2512 | 0.4798 | | No log | 38.0 | 266 | 56.6876 | 0.305 | 0.7840 | 6.8750 | 0.305 | 0.1205 | 0.2835 | 0.4985 | | No log | 39.0 | 273 | 122.6602 | 0.295 | 0.7919 | 6.9220 | 0.295 | 0.1116 | 0.2493 | 0.5312 | | No log | 40.0 | 280 | 14.4685 | 0.295 | 0.7757 | 6.8232 | 0.295 | 0.1162 | 0.2575 | 0.4988 | | No log | 41.0 | 287 | 3.9605 | 0.295 | 0.7601 | 6.7809 | 0.295 | 0.1138 | 0.2437 | 0.4911 | | No log | 42.0 | 294 | 7.9424 | 0.295 | 0.7567 | 6.7609 | 0.295 | 0.1138 | 0.2398 | 0.4883 | | No log | 43.0 | 301 | 17.7810 | 0.295 | 0.7713 | 6.8075 | 0.295 | 0.1175 | 0.2503 | 0.5090 | | No log | 44.0 | 308 | 30.8773 | 0.295 | 0.7747 | 6.8248 | 0.295 | 0.1127 | 0.2651 | 0.5149 | | No log | 45.0 | 315 | 16.3877 | 0.29 | 0.7736 | 6.8888 | 0.29 | 0.1117 | 0.2641 | 0.5026 | | No log | 46.0 | 322 | 7.4195 | 0.29 | 0.7674 | 6.8179 | 0.29 | 0.1117 | 0.2621 | 0.4991 | | No log | 47.0 | 329 | 9.6560 | 0.295 | 0.7694 | 6.8960 | 0.295 | 0.1138 | 0.2604 | 0.4963 | | No log | 48.0 | 336 | 6.6040 | 0.29 | 0.7622 | 6.7835 | 0.29 | 0.1117 | 0.2271 | 0.4958 | | No log | 49.0 | 343 | 10.3365 | 0.29 | 0.7640 | 6.8293 | 0.29 | 0.1117 | 0.2583 | 0.4941 | | No log | 50.0 | 350 | 13.1229 | 0.295 | 0.7636 | 6.8757 | 0.295 | 0.1150 | 0.2446 | 0.4919 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/resnet101-base_tobacco-cnn_tobacco3482_og_simkd

# resnet101-base_tobacco-cnn_tobacco3482_og_simkd This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1263 - Accuracy: 0.295 - Brier Loss: 0.7485 - Nll: 6.2362 - F1 Micro: 0.295 - F1 Macro: 0.1126 - Ece: 0.2177 - Aurc: 0.4648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 0.1887 | 0.18 | 0.8839 | 8.4886 | 0.18 | 0.0305 | 0.2321 | 0.8333 | | No log | 2.0 | 14 | 0.1901 | 0.18 | 0.8775 | 6.8406 | 0.18 | 0.0305 | 0.2593 | 0.8187 | | No log | 3.0 | 21 | 0.2085 | 0.28 | 0.9045 | 7.0658 | 0.28 | 0.1005 | 0.3168 | 0.6083 | | No log | 4.0 | 28 | 0.2493 | 0.155 | 0.9936 | 8.0153 | 0.155 | 0.0300 | 0.3996 | 0.6329 | | No log | 5.0 | 35 | 0.2952 | 0.16 | 1.0253 | 8.3946 | 0.16 | 0.0355 | 0.4181 | 0.6026 | | No log | 6.0 | 42 | 0.2344 | 0.195 | 0.9022 | 7.0548 | 0.195 | 0.0485 | 0.3118 | 0.8445 | | No log | 7.0 | 49 | 0.1665 | 0.18 | 0.9004 | 6.7765 | 0.18 | 0.0310 | 0.2888 | 0.7617 | | No log | 8.0 | 56 | 0.1696 | 0.18 | 0.9279 | 9.0648 | 0.18 | 0.0309 | 0.3021 | 0.7025 | | No log | 9.0 | 63 | 0.1715 | 0.18 | 0.9330 | 9.0774 | 0.18 | 0.0305 | 0.2992 | 0.7525 | | No log | 10.0 | 70 | 0.1369 | 0.285 | 0.8092 | 6.9372 | 0.285 | 0.1134 | 0.2993 | 0.4899 | | No log | 11.0 | 77 | 0.1584 | 0.18 | 0.8953 | 8.9899 | 0.18 | 0.0310 | 0.2666 | 0.7495 | | No log | 12.0 | 84 | 0.1690 | 0.18 | 0.8896 | 8.9605 | 0.18 | 0.0310 | 0.2593 | 0.7452 | | No log | 13.0 | 91 | 0.1636 | 0.18 | 0.8848 | 8.9907 | 0.18 | 0.0310 | 0.2661 | 0.7474 | | No log | 14.0 | 98 | 0.1685 | 0.18 | 0.8815 | 8.9991 | 0.18 | 0.0309 | 0.2676 | 0.7750 | | No log | 15.0 | 105 | 0.1678 | 0.18 | 0.8807 | 8.9352 | 0.18 | 0.0305 | 0.2658 | 0.7448 | | No log | 16.0 | 112 | 0.1599 | 0.18 | 0.8848 | 9.0210 | 0.18 | 0.0309 | 0.2707 | 0.7742 | | No log | 17.0 | 119 | 0.1553 | 0.18 | 0.8559 | 7.2132 | 0.18 | 0.0305 | 0.2569 | 0.7479 | | No log | 18.0 | 126 | 0.1620 | 0.18 | 0.8728 | 8.8826 | 0.18 | 0.0308 | 0.2472 | 0.7289 | | No log | 19.0 | 133 | 0.1631 | 0.18 | 0.8600 | 8.8681 | 0.18 | 0.0305 | 0.2689 | 0.7046 | | No log | 20.0 | 140 | 0.1616 | 0.18 | 0.8702 | 8.8768 | 0.18 | 0.0305 | 0.2532 | 0.7489 | | No log | 21.0 | 147 | 0.1521 | 0.18 | 0.8505 | 6.9939 | 0.18 | 0.0310 | 0.2687 | 0.7479 | | No log | 22.0 | 154 | 0.1290 | 0.285 | 0.7742 | 6.8763 | 0.285 | 0.1123 | 0.2907 | 0.4899 | | No log | 23.0 | 161 | 0.1256 | 0.305 | 0.7453 | 6.2659 | 0.305 | 0.1190 | 0.2133 | 0.4457 | | No log | 24.0 | 168 | 0.1257 | 0.305 | 0.7527 | 6.7983 | 0.305 | 0.1192 | 0.2483 | 0.4694 | | No log | 25.0 | 175 | 0.1256 | 0.295 | 0.7483 | 6.7540 | 0.295 | 0.1106 | 0.2233 | 0.4632 | | No log | 26.0 | 182 | 0.1277 | 0.3 | 0.7590 | 6.6632 | 0.3 | 0.1214 | 0.2641 | 0.4563 | | No log | 27.0 | 189 | 0.1644 | 0.18 | 0.8539 | 8.7216 | 0.18 | 0.0306 | 0.2483 | 0.7170 | | No log | 28.0 | 196 | 0.1268 | 0.305 | 0.7494 | 6.5633 | 0.305 | 0.1146 | 0.2379 | 0.4509 | | No log | 29.0 | 203 | 0.1246 | 0.305 | 0.7376 | 6.2718 | 0.305 | 0.1158 | 0.2319 | 0.4326 | | No log | 30.0 | 210 | 0.1249 | 0.3 | 0.7428 | 6.5246 | 0.3 | 0.1138 | 0.2463 | 0.4449 | | No log | 31.0 | 217 | 0.1284 | 0.295 | 0.7474 | 6.4668 | 0.295 | 0.1116 | 0.2566 | 0.4550 | | No log | 32.0 | 224 | 0.1715 | 0.18 | 0.8599 | 8.5902 | 0.18 | 0.0310 | 0.2413 | 0.7447 | | No log | 33.0 | 231 | 0.1566 | 0.18 | 0.8495 | 7.4352 | 0.18 | 0.0308 | 0.2624 | 0.7110 | | No log | 34.0 | 238 | 0.1370 | 0.275 | 0.7990 | 6.5052 | 0.275 | 0.1096 | 0.2760 | 0.5186 | | No log | 35.0 | 245 | 0.1289 | 0.3 | 0.7569 | 6.4685 | 0.3 | 0.1212 | 0.2643 | 0.4524 | | No log | 36.0 | 252 | 0.1557 | 0.18 | 0.8493 | 6.8218 | 0.18 | 0.0305 | 0.2574 | 0.7401 | | No log | 37.0 | 259 | 0.1629 | 0.18 | 0.8558 | 8.5068 | 0.18 | 0.0310 | 0.2522 | 0.7466 | | No log | 38.0 | 266 | 0.1386 | 0.275 | 0.8117 | 6.4244 | 0.275 | 0.1053 | 0.2455 | 0.5912 | | No log | 39.0 | 273 | 0.1601 | 0.18 | 0.8508 | 8.3697 | 0.18 | 0.0305 | 0.2445 | 0.7048 | | No log | 40.0 | 280 | 0.1510 | 0.185 | 0.8428 | 6.8710 | 0.185 | 0.0369 | 0.2517 | 0.7155 | | No log | 41.0 | 287 | 0.1315 | 0.29 | 0.7675 | 6.1897 | 0.29 | 0.1167 | 0.2708 | 0.4594 | | No log | 42.0 | 294 | 0.1235 | 0.3 | 0.7405 | 6.1762 | 0.3 | 0.1158 | 0.2338 | 0.4496 | | No log | 43.0 | 301 | 0.1250 | 0.295 | 0.7456 | 6.3789 | 0.295 | 0.1174 | 0.2524 | 0.4548 | | No log | 44.0 | 308 | 0.1249 | 0.285 | 0.7440 | 6.3862 | 0.285 | 0.1097 | 0.2405 | 0.4680 | | No log | 45.0 | 315 | 0.1245 | 0.29 | 0.7428 | 6.4641 | 0.29 | 0.1117 | 0.2403 | 0.4623 | | No log | 46.0 | 322 | 0.1245 | 0.295 | 0.7440 | 6.5208 | 0.295 | 0.1149 | 0.2385 | 0.4610 | | No log | 47.0 | 329 | 0.1250 | 0.29 | 0.7464 | 6.2221 | 0.29 | 0.1117 | 0.2332 | 0.4674 | | No log | 48.0 | 336 | 0.1263 | 0.295 | 0.7458 | 6.3085 | 0.295 | 0.1126 | 0.2375 | 0.4670 | | No log | 49.0 | 343 | 0.1252 | 0.29 | 0.7469 | 6.0647 | 0.29 | 0.1117 | 0.2410 | 0.4679 | | No log | 50.0 | 350 | 0.1263 | 0.295 | 0.7485 | 6.2362 | 0.295 | 0.1126 | 0.2177 | 0.4648 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.2.0.dev20231112+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

dima806/smoker_image_classification

Returns whether the person is a smoker based on image with about 97% accuracy. See https://www.kaggle.com/code/dima806/smoker-image-detection-vit for more details. ``` Classification report: precision recall f1-score support notsmoking 0.9907 0.9464 0.9680 112 smoking 0.9487 0.9911 0.9694 112 accuracy 0.9688 224 macro avg 0.9697 0.9688 0.9687 224 weighted avg 0.9697 0.9688 0.9687 224 ```

[ "notsmoking", "smoking" ]

cjade100/vit-base-patch16-224-finetuned-flower

# vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]

dima806/mammals_45_types_image_classification

Returns a common mammal type given an image with about 96% accuracy. See https://www.kaggle.com/code/dima806/mammals-45-types-image-classification-vit for more details. ``` Classification report: precision recall f1-score support african_elephant 1.0000 1.0000 1.0000 71 alpaca 0.9200 0.9718 0.9452 71 american_bison 1.0000 1.0000 1.0000 71 anteater 0.9853 0.9437 0.9640 71 arctic_fox 0.9286 0.9155 0.9220 71 armadillo 0.9726 1.0000 0.9861 71 baboon 0.9718 0.9718 0.9718 71 badger 1.0000 0.9718 0.9857 71 blue_whale 0.9710 0.9437 0.9571 71 brown_bear 0.9722 0.9859 0.9790 71 camel 0.9861 1.0000 0.9930 71 dolphin 0.8974 0.9859 0.9396 71 giraffe 0.9857 0.9718 0.9787 71 groundhog 0.9714 0.9577 0.9645 71 highland_cattle 0.9859 0.9859 0.9859 71 horse 1.0000 0.9859 0.9929 71 jackal 0.9577 0.9444 0.9510 72 kangaroo 0.8415 0.9583 0.8961 72 koala 0.9589 0.9859 0.9722 71 manatee 0.9861 0.9861 0.9861 72 mongoose 0.9483 0.7746 0.8527 71 mountain_goat 0.9855 0.9577 0.9714 71 opossum 1.0000 0.9577 0.9784 71 orangutan 1.0000 1.0000 1.0000 71 otter 1.0000 0.9577 0.9784 71 polar_bear 0.9706 0.9296 0.9496 71 porcupine 1.0000 0.9722 0.9859 72 red_panda 0.9718 0.9718 0.9718 71 rhinoceros 0.9859 0.9859 0.9859 71 sea_lion 0.7600 0.8028 0.7808 71 seal 0.8308 0.7500 0.7883 72 snow_leopard 1.0000 1.0000 1.0000 71 squirrel 0.9444 0.9577 0.9510 71 sugar_glider 0.8554 1.0000 0.9221 71 tapir 1.0000 1.0000 1.0000 71 vampire_bat 1.0000 0.9861 0.9930 72 vicuna 1.0000 0.8873 0.9403 71 walrus 0.9342 0.9861 0.9595 72 warthog 0.9571 0.9437 0.9504 71 water_buffalo 0.9333 0.9859 0.9589 71 weasel 0.9583 0.9583 0.9583 72 wildebeest 0.9577 0.9444 0.9510 72 wombat 0.8947 0.9577 0.9252 71 yak 1.0000 0.9437 0.9710 71 zebra 0.9595 1.0000 0.9793 71 accuracy 0.9572 3204 macro avg 0.9587 0.9573 0.9572 3204 weighted avg 0.9586 0.9572 0.9572 3204 ```

[ "african_elephant", "alpaca", "american_bison", "anteater", "arctic_fox", "armadillo", "baboon", "badger", "blue_whale", "brown_bear", "camel", "dolphin", "giraffe", "groundhog", "highland_cattle", "horse", "jackal", "kangaroo", "koala", "manatee", "mongoose", "mountain_goat", "opossum", "orangutan", "otter", "polar_bear", "porcupine", "red_panda", "rhinoceros", "sea_lion", "seal", "snow_leopard", "squirrel", "sugar_glider", "tapir", "vampire_bat", "vicuna", "walrus", "warthog", "water_buffalo", "weasel", "wildebeest", "wombat", "yak", "zebra" ]

phuong-tk-nguyen/resnet-50-finetuned-cifar10

# resnet-50-finetuned-cifar10 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.9060 - Accuracy: 0.5076 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3058 | 0.03 | 10 | 2.3106 | 0.0794 | | 2.3033 | 0.06 | 20 | 2.3026 | 0.0892 | | 2.3012 | 0.09 | 30 | 2.2971 | 0.1042 | | 2.2914 | 0.11 | 40 | 2.2890 | 0.1254 | | 2.2869 | 0.14 | 50 | 2.2816 | 0.16 | | 2.2785 | 0.17 | 60 | 2.2700 | 0.1902 | | 2.2712 | 0.2 | 70 | 2.2602 | 0.2354 | | 2.2619 | 0.23 | 80 | 2.2501 | 0.2688 | | 2.2509 | 0.26 | 90 | 2.2383 | 0.3022 | | 2.2382 | 0.28 | 100 | 2.2229 | 0.3268 | | 2.2255 | 0.31 | 110 | 2.2084 | 0.353 | | 2.2164 | 0.34 | 120 | 2.1939 | 0.3608 | | 2.2028 | 0.37 | 130 | 2.1829 | 0.3668 | | 2.1977 | 0.4 | 140 | 2.1646 | 0.401 | | 2.1844 | 0.43 | 150 | 2.1441 | 0.4244 | | 2.1689 | 0.45 | 160 | 2.1323 | 0.437 | | 2.1555 | 0.48 | 170 | 2.1159 | 0.4462 | | 2.1448 | 0.51 | 180 | 2.0992 | 0.45 | | 2.1313 | 0.54 | 190 | 2.0810 | 0.4642 | | 2.1189 | 0.57 | 200 | 2.0589 | 0.4708 | | 2.1111 | 0.6 | 210 | 2.0430 | 0.4828 | | 2.0905 | 0.63 | 220 | 2.0288 | 0.4938 | | 2.082 | 0.65 | 230 | 2.0089 | 0.4938 | | 2.0646 | 0.68 | 240 | 1.9970 | 0.5014 | | 2.0636 | 0.71 | 250 | 1.9778 | 0.4946 | | 2.0579 | 0.74 | 260 | 1.9609 | 0.49 | | 2.028 | 0.77 | 270 | 1.9602 | 0.4862 | | 2.0447 | 0.8 | 280 | 1.9460 | 0.4934 | | 2.0168 | 0.82 | 290 | 1.9369 | 0.505 | | 2.0126 | 0.85 | 300 | 1.9317 | 0.4926 | | 2.0099 | 0.88 | 310 | 1.9235 | 0.4952 | | 1.9978 | 0.91 | 320 | 1.9174 | 0.4972 | | 1.9951 | 0.94 | 330 | 1.9119 | 0.507 | | 1.9823 | 0.97 | 340 | 1.9120 | 0.4992 | | 1.985 | 1.0 | 350 | 1.9064 | 0.5022 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

PK-B/roof_classifier

# PK-B/roof_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6844 - Validation Loss: 2.3315 - Train Accuracy: 0.425 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1770, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.9736 | 2.9756 | 0.05 | 0 | | 2.9016 | 2.9430 | 0.1 | 1 | | 2.8192 | 2.9084 | 0.1 | 2 | | 2.7004 | 2.8564 | 0.175 | 3 | | 2.6005 | 2.8109 | 0.175 | 4 | | 2.4981 | 2.7452 | 0.225 | 5 | | 2.3819 | 2.6988 | 0.2125 | 6 | | 2.2867 | 2.6998 | 0.25 | 7 | | 2.1804 | 2.6510 | 0.275 | 8 | | 2.1115 | 2.5307 | 0.3375 | 9 | | 2.0161 | 2.5523 | 0.3 | 10 | | 1.9189 | 2.5310 | 0.2875 | 11 | | 1.8863 | 2.4733 | 0.3375 | 12 | | 1.7518 | 2.4233 | 0.3625 | 13 | | 1.6844 | 2.3315 | 0.425 | 14 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "atlas_pinnacle_wwood_shade", "atlas_pinnacle_wwood_sun", "gaf_wwood_shade", "gaf_wwood_sun", "iko_cornerstone_shade", "iko_cornerstone_sun", "malarkey_wwood_shade", "malarkey_wwood_sun", "oc_driftwood_shade", "oc_driftwood_sun", "tamko_wwood_shade", "tamko_wwood_sun", "cteed_maxdef_wwood_shade", "cteed_maxdef_wwood_sun", "cteed_wwood_shade", "cteed_wwood_sun", "gaf_mission_brown_shade", "gaf_mission_brown_sun", "gaf_pewter_gray_shade", "gaf_pewter_gray_sun" ]

andakm/swin-tiny-patch4-window7-224

# swin-tiny-patch4-window7-224 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3635 - Accuracy: 0.5294 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 1.7560 | 0.3137 | | No log | 1.87 | 7 | 1.6225 | 0.3725 | | 1.7919 | 2.93 | 11 | 1.5661 | 0.4510 | | 1.7919 | 4.0 | 15 | 1.5332 | 0.4510 | | 1.7919 | 4.8 | 18 | 1.4522 | 0.5294 | | 1.5187 | 5.87 | 22 | 1.3873 | 0.4902 | | 1.5187 | 6.93 | 26 | 1.3741 | 0.4902 | | 1.2773 | 8.0 | 30 | 1.3635 | 0.5294 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "1-series", "3-series", "4-series", "5-series", "6-series", "7-series", "8-series", "m3", "m4", "m5" ]

phuong-tk-nguyen/vit-base-patch16-224-finetuned-cifar10

# vit-base-patch16-224-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0564 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.4597 | 0.03 | 10 | 2.2902 | 0.1662 | | 2.1429 | 0.06 | 20 | 1.7855 | 0.5086 | | 1.6466 | 0.09 | 30 | 1.0829 | 0.8484 | | 0.9962 | 0.11 | 40 | 0.4978 | 0.9288 | | 0.6127 | 0.14 | 50 | 0.2717 | 0.9508 | | 0.4544 | 0.17 | 60 | 0.1942 | 0.9588 | | 0.4352 | 0.2 | 70 | 0.1504 | 0.9672 | | 0.374 | 0.23 | 80 | 0.1221 | 0.9718 | | 0.3261 | 0.26 | 90 | 0.1057 | 0.9772 | | 0.34 | 0.28 | 100 | 0.0943 | 0.979 | | 0.284 | 0.31 | 110 | 0.0958 | 0.9754 | | 0.3151 | 0.34 | 120 | 0.0866 | 0.9776 | | 0.3004 | 0.37 | 130 | 0.0838 | 0.9788 | | 0.3334 | 0.4 | 140 | 0.0798 | 0.9806 | | 0.3018 | 0.43 | 150 | 0.0800 | 0.9778 | | 0.2957 | 0.45 | 160 | 0.0749 | 0.9808 | | 0.2952 | 0.48 | 170 | 0.0704 | 0.9814 | | 0.3084 | 0.51 | 180 | 0.0720 | 0.9812 | | 0.3015 | 0.54 | 190 | 0.0708 | 0.983 | | 0.2763 | 0.57 | 200 | 0.0672 | 0.9832 | | 0.3376 | 0.6 | 210 | 0.0700 | 0.982 | | 0.285 | 0.63 | 220 | 0.0657 | 0.9828 | | 0.2857 | 0.65 | 230 | 0.0629 | 0.9836 | | 0.2644 | 0.68 | 240 | 0.0612 | 0.9842 | | 0.2461 | 0.71 | 250 | 0.0601 | 0.9836 | | 0.2802 | 0.74 | 260 | 0.0589 | 0.9842 | | 0.2481 | 0.77 | 270 | 0.0604 | 0.9838 | | 0.2641 | 0.8 | 280 | 0.0591 | 0.9846 | | 0.2737 | 0.82 | 290 | 0.0581 | 0.9842 | | 0.2391 | 0.85 | 300 | 0.0565 | 0.9852 | | 0.2283 | 0.88 | 310 | 0.0558 | 0.986 | | 0.2626 | 0.91 | 320 | 0.0559 | 0.9852 | | 0.2325 | 0.94 | 330 | 0.0563 | 0.9846 | | 0.2459 | 0.97 | 340 | 0.0565 | 0.9846 | | 0.2474 | 1.0 | 350 | 0.0564 | 0.9844 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

HarshaSingamshetty1/roof_classifier

# HarshaSingamshetty1/roof_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6380 - Validation Loss: 2.1987 - Train Accuracy: 0.4375 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1770, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.9764 | 2.9730 | 0.05 | 0 | | 2.8746 | 2.9232 | 0.125 | 1 | | 2.7792 | 2.8802 | 0.1375 | 2 | | 2.6648 | 2.8491 | 0.225 | 3 | | 2.5573 | 2.7563 | 0.1625 | 4 | | 2.4614 | 2.7155 | 0.2875 | 5 | | 2.3453 | 2.7005 | 0.2 | 6 | | 2.2737 | 2.6443 | 0.2875 | 7 | | 2.1555 | 2.5396 | 0.3625 | 8 | | 2.0694 | 2.4244 | 0.425 | 9 | | 2.0112 | 2.3738 | 0.4875 | 10 | | 1.8867 | 2.3843 | 0.4125 | 11 | | 1.8217 | 2.2878 | 0.45 | 12 | | 1.7253 | 2.2642 | 0.475 | 13 | | 1.6380 | 2.1987 | 0.4375 | 14 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "atlas_pinnacle_wwood_shade", "atlas_pinnacle_wwood_sun", "gaf_wwood_shade", "gaf_wwood_sun", "iko_cornerstone_shade", "iko_cornerstone_sun", "malarkey_wwood_shade", "malarkey_wwood_sun", "oc_driftwood_shade", "oc_driftwood_sun", "tamko_wwood_shade", "tamko_wwood_sun", "cteed_maxdef_wwood_shade", "cteed_maxdef_wwood_sun", "cteed_wwood_shade", "cteed_wwood_sun", "gaf_mission_brown_shade", "gaf_mission_brown_sun", "gaf_pewter_gray_shade", "gaf_pewter_gray_sun" ]

Iust1n2/resnet-18-finetuned-wikiart

# resnet-18-finetuned-wikiart This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6256 - Accuracy: 0.6247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0382 | 1.0 | 2037 | 1.8771 | 0.5938 | | 1.8027 | 2.0 | 4074 | 1.6860 | 0.6160 | | 1.7033 | 3.0 | 6111 | 1.6256 | 0.6247 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unknown artist", "boris-kustodiev", "camille-pissarro", "childe-hassam", "claude-monet", "edgar-degas", "eugene-boudin", "gustave-dore", "ilya-repin", "ivan-aivazovsky", "ivan-shishkin", "john-singer-sargent", "marc-chagall", "martiros-saryan", "nicholas-roerich", "pablo-picasso", "paul-cezanne", "pierre-auguste-renoir", "pyotr-konchalovsky", "raphael-kirchner", "rembrandt", "salvador-dali", "vincent-van-gogh", "hieronymus-bosch", "leonardo-da-vinci", "albrecht-durer", "edouard-cortes", "sam-francis", "juan-gris", "lucas-cranach-the-elder", "paul-gauguin", "konstantin-makovsky", "egon-schiele", "thomas-eakins", "gustave-moreau", "francisco-goya", "edvard-munch", "henri-matisse", "fra-angelico", "maxime-maufra", "jan-matejko", "mstislav-dobuzhinsky", "alfred-sisley", "mary-cassatt", "gustave-loiseau", "fernando-botero", "zinaida-serebriakova", "georges-seurat", "isaac-levitan", "joaquãn-sorolla", "jacek-malczewski", "berthe-morisot", "andy-warhol", "arkhip-kuindzhi", "niko-pirosmani", "james-tissot", "vasily-polenov", "valentin-serov", "pietro-perugino", "pierre-bonnard", "ferdinand-hodler", "bartolome-esteban-murillo", "giovanni-boldini", "henri-martin", "gustav-klimt", "vasily-perov", "odilon-redon", "tintoretto", "gene-davis", "raphael", "john-henry-twachtman", "henri-de-toulouse-lautrec", "antoine-blanchard", "david-burliuk", "camille-corot", "konstantin-korovin", "ivan-bilibin", "titian", "maurice-prendergast", "edouard-manet", "peter-paul-rubens", "aubrey-beardsley", "paolo-veronese", "joshua-reynolds", "kuzma-petrov-vodkin", "gustave-caillebotte", "lucian-freud", "michelangelo", "dante-gabriel-rossetti", "felix-vallotton", "nikolay-bogdanov-belsky", "georges-braque", "vasily-surikov", "fernand-leger", "konstantin-somov", "katsushika-hokusai", "sir-lawrence-alma-tadema", "vasily-vereshchagin", "ernst-ludwig-kirchner", "mikhail-vrubel", "orest-kiprensky", "william-merritt-chase", "aleksey-savrasov", "hans-memling", "amedeo-modigliani", "ivan-kramskoy", "utagawa-kuniyoshi", "gustave-courbet", "william-turner", "theo-van-rysselberghe", "joseph-wright", "edward-burne-jones", "koloman-moser", "viktor-vasnetsov", "anthony-van-dyck", "raoul-dufy", "frans-hals", "hans-holbein-the-younger", "ilya-mashkov", "henri-fantin-latour", "m.c.-escher", "el-greco", "mikalojus-ciurlionis", "james-mcneill-whistler", "karl-bryullov", "jacob-jordaens", "thomas-gainsborough", "eugene-delacroix", "canaletto" ]

fashxp/car_manufacturer_model

# car_manufacturer_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 2.7826 - Accuracy: 0.3394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 3.1387 | 0.2018 | | 2.8998 | 2.0 | 14 | 3.1029 | 0.2018 | | 2.7326 | 3.0 | 21 | 3.0453 | 0.2294 | | 2.7326 | 4.0 | 28 | 3.0104 | 0.2385 | | 2.5797 | 5.0 | 35 | 2.9655 | 0.2477 | | 2.4873 | 6.0 | 42 | 2.9166 | 0.3211 | | 2.4873 | 7.0 | 49 | 2.9122 | 0.2569 | | 2.3408 | 8.0 | 56 | 2.8122 | 0.3119 | | 2.2696 | 9.0 | 63 | 2.8159 | 0.3578 | | 2.1527 | 10.0 | 70 | 2.8589 | 0.2752 | | 2.1527 | 11.0 | 77 | 2.8248 | 0.2936 | | 2.0649 | 12.0 | 84 | 2.7709 | 0.2936 | | 2.0855 | 13.0 | 91 | 2.8183 | 0.2477 | | 2.0855 | 14.0 | 98 | 2.7552 | 0.2569 | | 1.9347 | 15.0 | 105 | 2.7826 | 0.3394 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "ac cars", "alfa romeo", "chrysler", "citroen", "datsun", "dodge", "ferrari", "fiat", "ford", "jaguar", "lamborghini", "lincoln", "amc", "mazda", "mercedes", "mercury", "mga", "morris", "oldsmobile", "opel", "peugeot", "plymouth", "pontiac", "aston martin", "porsche", "renault", "saab", "toyota", "trabant", "triumph", "volkswagen", "volvo", "audi", "austin healey", "bmw", "buick", "cadillac", "chevrolet" ]

anirudhmu/swin-tiny-patch4-window7-224-finetuned-soccer-binary2

# swin-tiny-patch4-window7-224-finetuned-soccer-binary2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1078 - Accuracy: 0.9719 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4085 | 0.99 | 20 | 0.1740 | 0.9544 | | 0.1281 | 1.98 | 40 | 0.1078 | 0.9719 | | 0.108 | 2.96 | 60 | 0.0978 | 0.9684 | | 0.1077 | 4.0 | 81 | 0.1006 | 0.9684 | | 0.0916 | 4.99 | 101 | 0.0954 | 0.9649 | | 0.0824 | 5.98 | 121 | 0.0935 | 0.9684 | | 0.0859 | 6.96 | 141 | 0.0975 | 0.9684 | | 0.0927 | 8.0 | 162 | 0.0949 | 0.9684 | | 0.0836 | 8.99 | 182 | 0.0928 | 0.9684 | | 0.0958 | 9.88 | 200 | 0.0940 | 0.9684 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "closeup", "overview", "logo" ]

phuong-tk-nguyen/swin-base-patch4-window7-224-in22k-finetuned-cifar10

# swin-base-patch4-window7-224-in22k-finetuned-cifar10 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0414 - Accuracy: 0.9858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.303 | 0.03 | 10 | 2.1672 | 0.2334 | | 2.0158 | 0.06 | 20 | 1.6672 | 0.657 | | 1.4855 | 0.09 | 30 | 0.8292 | 0.8704 | | 0.7451 | 0.11 | 40 | 0.2578 | 0.93 | | 0.5618 | 0.14 | 50 | 0.1476 | 0.962 | | 0.4545 | 0.17 | 60 | 0.1248 | 0.9642 | | 0.4587 | 0.2 | 70 | 0.0941 | 0.9748 | | 0.3911 | 0.23 | 80 | 0.0944 | 0.9712 | | 0.3839 | 0.26 | 90 | 0.0848 | 0.9756 | | 0.3864 | 0.28 | 100 | 0.0744 | 0.978 | | 0.3141 | 0.31 | 110 | 0.0673 | 0.98 | | 0.3764 | 0.34 | 120 | 0.0706 | 0.9764 | | 0.3003 | 0.37 | 130 | 0.0600 | 0.984 | | 0.3566 | 0.4 | 140 | 0.0562 | 0.9826 | | 0.2855 | 0.43 | 150 | 0.0567 | 0.9816 | | 0.3351 | 0.45 | 160 | 0.0543 | 0.9828 | | 0.2977 | 0.48 | 170 | 0.0568 | 0.9798 | | 0.2924 | 0.51 | 180 | 0.0577 | 0.9804 | | 0.2884 | 0.54 | 190 | 0.0551 | 0.983 | | 0.3067 | 0.57 | 200 | 0.0487 | 0.983 | | 0.3159 | 0.6 | 210 | 0.0513 | 0.984 | | 0.2795 | 0.63 | 220 | 0.0460 | 0.9846 | | 0.3113 | 0.65 | 230 | 0.0495 | 0.9832 | | 0.2882 | 0.68 | 240 | 0.0475 | 0.9838 | | 0.263 | 0.71 | 250 | 0.0449 | 0.9854 | | 0.2686 | 0.74 | 260 | 0.0510 | 0.9826 | | 0.2705 | 0.77 | 270 | 0.0483 | 0.9846 | | 0.2807 | 0.8 | 280 | 0.0430 | 0.9854 | | 0.2583 | 0.82 | 290 | 0.0452 | 0.9858 | | 0.2346 | 0.85 | 300 | 0.0435 | 0.9858 | | 0.2294 | 0.88 | 310 | 0.0434 | 0.986 | | 0.2608 | 0.91 | 320 | 0.0433 | 0.986 | | 0.2642 | 0.94 | 330 | 0.0425 | 0.9866 | | 0.2781 | 0.97 | 340 | 0.0417 | 0.986 | | 0.247 | 1.0 | 350 | 0.0414 | 0.9858 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

edwinpalegre/vit-base-trashnet-demo

# vit-base-trashnet-demo This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the edwinpalegre/trashnet-enhanced dataset. It achieves the following results on the evaluation set: - Loss: 0.0701 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2636 | 0.4 | 100 | 0.2388 | 0.9394 | | 0.1748 | 0.8 | 200 | 0.1414 | 0.9623 | | 0.1231 | 1.2 | 300 | 0.1565 | 0.9545 | | 0.0769 | 1.61 | 400 | 0.1074 | 0.9713 | | 0.0556 | 2.01 | 500 | 0.0994 | 0.9726 | | 0.0295 | 2.41 | 600 | 0.0720 | 0.9812 | | 0.0311 | 2.81 | 700 | 0.0774 | 0.9806 | | 0.0061 | 3.21 | 800 | 0.0703 | 0.9822 | | 0.0289 | 3.61 | 900 | 0.0701 | 0.9822 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "biodegradable", "cardboard", "glass", "metal", "paper", "plastic", "trash" ]

Gracoy/swinv2-base-patch4-window8-256-Kaggle_test_20231123

# swinv2-base-patch4-window8-256-Kaggle_test_20231123 This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2066 - Accuracy: 0.9315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2882 | 1.0 | 64 | 0.2488 | 0.9311 | | 0.2568 | 2.0 | 128 | 0.2522 | 0.9311 | | 0.1961 | 3.0 | 192 | 0.2066 | 0.9315 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "0", "1" ]

parotnes/my_awesome_food_model

# my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6835 - Accuracy: 0.894 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7311 | 0.99 | 62 | 2.5508 | 0.833 | | 1.8635 | 2.0 | 125 | 1.8232 | 0.9 | | 1.6152 | 2.98 | 186 | 1.6835 | 0.894 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

Sharon8y/my_awesome_food_model

# my_awesome_food_model This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4322 - Accuracy: 0.8855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4246 | 0.99 | 41 | 1.0122 | 0.7470 | | 0.3689 | 1.99 | 82 | 0.8057 | 0.7831 | | 0.1659 | 2.98 | 123 | 0.4473 | 0.8855 | | 0.1287 | 4.0 | 165 | 0.5580 | 0.8434 | | 0.0944 | 4.97 | 205 | 0.4322 | 0.8855 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "acerolas", "apples", "apricots", "avocados", "bananas", "blackberries", "blueberries", "cantaloupes", "cherries", "coconuts", "figs", "grapefruits", "grapes", "guava", "kiwifruit", "lemons", "limes", "mangos", "olives", "oranges", "passionfruit", "peaches", "pears", "pineapples", "plums", "pomegranates", "raspberries", "strawberries", "tomatoes", "watermelons" ]

danieltur/my_awesome_catdog_model

# my_awesome_catdog_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.0083 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0132 | 0.99 | 62 | 0.0121 | 1.0 | | 0.0092 | 2.0 | 125 | 0.0089 | 1.0 | | 0.0083 | 2.98 | 186 | 0.0083 | 1.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "cat", "dog" ]

parotnes/my_awesome_animal_model

# my_awesome_animal_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9721 - Accuracy: 0.966 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.675 | 0.99 | 62 | 1.6475 | 0.966 | | 1.0692 | 2.0 | 125 | 1.1535 | 0.966 | | 0.8611 | 2.98 | 186 | 0.9721 | 0.966 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "cane", "cavallo", "elefante", "farfalla", "gallina", "gatto", "mucca", "pecora", "ragno", "scoiattolo" ]

scottglover020/convnextv2-tiny-1k-224-finetuned-citrico-2615

# convnextv2-tiny-1k-224-finetuned-beans This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9837 - Accuracy: 0.6381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7177 | 1.0 | 269 | 0.6939 | 0.5224 | | 0.6132 | 2.0 | 538 | 0.6692 | 0.6101 | | 0.642 | 3.0 | 807 | 0.6729 | 0.5951 | | 0.6431 | 4.0 | 1076 | 0.6700 | 0.5690 | | 0.5081 | 5.0 | 1345 | 0.7537 | 0.6213 | | 0.4114 | 6.0 | 1614 | 0.9249 | 0.6175 | | 0.3991 | 7.0 | 1883 | 0.9837 | 0.6381 | | 0.2194 | 8.0 | 2152 | 1.4350 | 0.5802 | | 0.0834 | 9.0 | 2421 | 1.3808 | 0.6138 | | 0.15 | 10.0 | 2690 | 1.3277 | 0.6306 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "belly", "notbelly", "unclear" ]

SirSkandrani/food_classifier

# SirSkandrani/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3560 - Validation Loss: 0.3026 - Train Accuracy: 0.93 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.7916 | 1.6000 | 0.841 | 0 | | 1.2008 | 0.7763 | 0.904 | 1 | | 0.6724 | 0.4730 | 0.92 | 2 | | 0.4895 | 0.3631 | 0.919 | 3 | | 0.3560 | 0.3026 | 0.93 | 4 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

parotnes/my_animals_model

# my_animals_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 2.2858 - Accuracy: 0.1566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 100 - eval_batch_size: 100 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 500 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.287 | 1.0 | 42 | 2.2859 | 0.1595 | | 2.2873 | 2.0 | 84 | 2.2870 | 0.1610 | | 2.287 | 3.0 | 126 | 2.2858 | 0.1566 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "t - shirt / top", "trouser", "pullover", "dress", "coat", "sandal", "shirt", "sneaker", "bag", "ankle boot" ]

hkivancoral/hushem_5x_deit_base_adamax_00001_fold1

# hushem_5x_deit_base_adamax_00001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1362 - Accuracy: 0.6444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2172 | 1.0 | 27 | 1.2445 | 0.4 | | 0.8523 | 2.0 | 54 | 1.0947 | 0.4667 | | 0.5686 | 3.0 | 81 | 1.0185 | 0.5778 | | 0.4004 | 4.0 | 108 | 0.9768 | 0.5778 | | 0.2464 | 5.0 | 135 | 0.9587 | 0.5778 | | 0.1691 | 6.0 | 162 | 0.8886 | 0.6222 | | 0.1191 | 7.0 | 189 | 0.9107 | 0.6 | | 0.0619 | 8.0 | 216 | 0.8951 | 0.6444 | | 0.0336 | 9.0 | 243 | 0.9574 | 0.6 | | 0.0186 | 10.0 | 270 | 0.9860 | 0.5778 | | 0.0125 | 11.0 | 297 | 0.9869 | 0.6 | | 0.0084 | 12.0 | 324 | 1.0113 | 0.6 | | 0.0076 | 13.0 | 351 | 0.9936 | 0.6 | | 0.0057 | 14.0 | 378 | 1.0048 | 0.6 | | 0.0052 | 15.0 | 405 | 1.0120 | 0.6 | | 0.0044 | 16.0 | 432 | 1.0086 | 0.6222 | | 0.0038 | 17.0 | 459 | 1.0209 | 0.6222 | | 0.0036 | 18.0 | 486 | 1.0433 | 0.6222 | | 0.0032 | 19.0 | 513 | 1.0446 | 0.6444 | | 0.0029 | 20.0 | 540 | 1.0517 | 0.6444 | | 0.0025 | 21.0 | 567 | 1.0577 | 0.6444 | | 0.0023 | 22.0 | 594 | 1.0550 | 0.6444 | | 0.0022 | 23.0 | 621 | 1.0799 | 0.6444 | | 0.002 | 24.0 | 648 | 1.0753 | 0.6444 | | 0.002 | 25.0 | 675 | 1.0830 | 0.6444 | | 0.002 | 26.0 | 702 | 1.0841 | 0.6444 | | 0.0018 | 27.0 | 729 | 1.0884 | 0.6444 | | 0.0017 | 28.0 | 756 | 1.0904 | 0.6444 | | 0.0017 | 29.0 | 783 | 1.1034 | 0.6444 | | 0.0016 | 30.0 | 810 | 1.1073 | 0.6444 | | 0.0015 | 31.0 | 837 | 1.1021 | 0.6444 | | 0.0015 | 32.0 | 864 | 1.1089 | 0.6444 | | 0.0014 | 33.0 | 891 | 1.1157 | 0.6444 | | 0.0014 | 34.0 | 918 | 1.1170 | 0.6444 | | 0.0013 | 35.0 | 945 | 1.1193 | 0.6444 | | 0.0012 | 36.0 | 972 | 1.1215 | 0.6444 | | 0.0013 | 37.0 | 999 | 1.1225 | 0.6444 | | 0.0012 | 38.0 | 1026 | 1.1226 | 0.6444 | | 0.0012 | 39.0 | 1053 | 1.1299 | 0.6444 | | 0.0011 | 40.0 | 1080 | 1.1301 | 0.6444 | | 0.0012 | 41.0 | 1107 | 1.1312 | 0.6444 | | 0.0011 | 42.0 | 1134 | 1.1308 | 0.6444 | | 0.0012 | 43.0 | 1161 | 1.1360 | 0.6444 | | 0.001 | 44.0 | 1188 | 1.1351 | 0.6444 | | 0.0011 | 45.0 | 1215 | 1.1359 | 0.6444 | | 0.0011 | 46.0 | 1242 | 1.1364 | 0.6444 | | 0.001 | 47.0 | 1269 | 1.1364 | 0.6444 | | 0.0011 | 48.0 | 1296 | 1.1362 | 0.6444 | | 0.0011 | 49.0 | 1323 | 1.1362 | 0.6444 | | 0.0011 | 50.0 | 1350 | 1.1362 | 0.6444 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_adamax_00001_fold2

# hushem_5x_deit_base_adamax_00001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3720 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2573 | 1.0 | 27 | 1.3177 | 0.3778 | | 0.9474 | 2.0 | 54 | 1.2698 | 0.4667 | | 0.6743 | 3.0 | 81 | 1.1709 | 0.5333 | | 0.53 | 4.0 | 108 | 1.1238 | 0.6 | | 0.3327 | 5.0 | 135 | 1.1060 | 0.6 | | 0.2187 | 6.0 | 162 | 1.0991 | 0.6444 | | 0.1497 | 7.0 | 189 | 1.1072 | 0.6444 | | 0.086 | 8.0 | 216 | 1.1220 | 0.6444 | | 0.0449 | 9.0 | 243 | 1.1215 | 0.6444 | | 0.0257 | 10.0 | 270 | 1.1368 | 0.6667 | | 0.0174 | 11.0 | 297 | 1.1587 | 0.6667 | | 0.0102 | 12.0 | 324 | 1.1715 | 0.6889 | | 0.0083 | 13.0 | 351 | 1.2117 | 0.6889 | | 0.0067 | 14.0 | 378 | 1.2042 | 0.6889 | | 0.0061 | 15.0 | 405 | 1.2320 | 0.6889 | | 0.0048 | 16.0 | 432 | 1.2396 | 0.6889 | | 0.0043 | 17.0 | 459 | 1.2501 | 0.6889 | | 0.0039 | 18.0 | 486 | 1.2585 | 0.6667 | | 0.0034 | 19.0 | 513 | 1.2714 | 0.6889 | | 0.0031 | 20.0 | 540 | 1.2786 | 0.6889 | | 0.0029 | 21.0 | 567 | 1.2831 | 0.6667 | | 0.0026 | 22.0 | 594 | 1.2886 | 0.6667 | | 0.0022 | 23.0 | 621 | 1.2985 | 0.6667 | | 0.0022 | 24.0 | 648 | 1.3036 | 0.6667 | | 0.002 | 25.0 | 675 | 1.3071 | 0.6667 | | 0.002 | 26.0 | 702 | 1.3150 | 0.6667 | | 0.0017 | 27.0 | 729 | 1.3222 | 0.6667 | | 0.0018 | 28.0 | 756 | 1.3235 | 0.6667 | | 0.0018 | 29.0 | 783 | 1.3294 | 0.6667 | | 0.0017 | 30.0 | 810 | 1.3351 | 0.6667 | | 0.0015 | 31.0 | 837 | 1.3358 | 0.6667 | | 0.0016 | 32.0 | 864 | 1.3406 | 0.6667 | | 0.0015 | 33.0 | 891 | 1.3434 | 0.6667 | | 0.0014 | 34.0 | 918 | 1.3481 | 0.6667 | | 0.0013 | 35.0 | 945 | 1.3523 | 0.6667 | | 0.0013 | 36.0 | 972 | 1.3535 | 0.6667 | | 0.0013 | 37.0 | 999 | 1.3558 | 0.6667 | | 0.0012 | 38.0 | 1026 | 1.3590 | 0.6667 | | 0.0012 | 39.0 | 1053 | 1.3619 | 0.6667 | | 0.0011 | 40.0 | 1080 | 1.3634 | 0.6667 | | 0.0012 | 41.0 | 1107 | 1.3657 | 0.6667 | | 0.0011 | 42.0 | 1134 | 1.3669 | 0.6667 | | 0.0011 | 43.0 | 1161 | 1.3696 | 0.6667 | | 0.0011 | 44.0 | 1188 | 1.3699 | 0.6667 | | 0.0011 | 45.0 | 1215 | 1.3707 | 0.6667 | | 0.0011 | 46.0 | 1242 | 1.3712 | 0.6667 | | 0.0011 | 47.0 | 1269 | 1.3718 | 0.6667 | | 0.0011 | 48.0 | 1296 | 1.3720 | 0.6667 | | 0.0011 | 49.0 | 1323 | 1.3720 | 0.6667 | | 0.0011 | 50.0 | 1350 | 1.3720 | 0.6667 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

bortle/astrophotography-object-classifier-alpha5

# astrophotography-object-classifier-alpha5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1827 - Accuracy: 0.9516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.2639 | 1.0 | 2575 | 0.2192 | 0.9461 | | 0.2457 | 2.0 | 5150 | 0.2065 | 0.9464 | | 0.3157 | 3.0 | 7725 | 0.1827 | 0.9516 | | 0.3149 | 4.0 | 10300 | 0.1855 | 0.9488 | | 0.1212 | 5.0 | 12875 | 0.2079 | 0.9480 | | 0.078 | 6.0 | 15450 | 0.2008 | 0.9516 | | 0.3493 | 7.0 | 18025 | 0.2038 | 0.9497 | | 0.131 | 8.0 | 20600 | 0.2059 | 0.9510 | | 0.2658 | 9.0 | 23175 | 0.2089 | 0.9510 | | 0.0762 | 10.0 | 25750 | 0.2068 | 0.9541 | | 0.127 | 11.0 | 28325 | 0.1986 | 0.9543 | | 0.181 | 12.0 | 30900 | 0.2227 | 0.9513 | | 0.1072 | 13.0 | 33475 | 0.2303 | 0.9502 | | 0.0179 | 14.0 | 36050 | 0.2240 | 0.9483 | | 0.1447 | 15.0 | 38625 | 0.2364 | 0.9505 | | 0.0933 | 16.0 | 41200 | 0.2372 | 0.9532 | | 0.17 | 17.0 | 43775 | 0.2166 | 0.9557 | | 0.0463 | 18.0 | 46350 | 0.2852 | 0.9461 | | 0.1207 | 19.0 | 48925 | 0.2653 | 0.9508 | | 0.1761 | 20.0 | 51500 | 0.2443 | 0.9521 | | 0.1441 | 21.0 | 54075 | 0.2464 | 0.9535 | | 0.1279 | 22.0 | 56650 | 0.2681 | 0.9499 | | 0.1811 | 23.0 | 59225 | 0.2626 | 0.9538 | | 0.1737 | 24.0 | 61800 | 0.2604 | 0.9541 | | 0.0275 | 25.0 | 64375 | 0.2625 | 0.9510 | | 0.1757 | 26.0 | 66950 | 0.2819 | 0.9488 | | 0.1257 | 27.0 | 69525 | 0.2708 | 0.9521 | | 0.1097 | 28.0 | 72100 | 0.2801 | 0.9519 | | 0.0772 | 29.0 | 74675 | 0.2870 | 0.9499 | | 0.132 | 30.0 | 77250 | 0.2824 | 0.9497 | | 0.0652 | 31.0 | 79825 | 0.2628 | 0.9538 | | 0.0324 | 32.0 | 82400 | 0.3223 | 0.9453 | | 0.1774 | 33.0 | 84975 | 0.2749 | 0.9549 | | 0.1178 | 34.0 | 87550 | 0.2905 | 0.9513 | | 0.0804 | 35.0 | 90125 | 0.3100 | 0.9480 | | 0.0617 | 36.0 | 92700 | 0.3131 | 0.9475 | | 0.0348 | 37.0 | 95275 | 0.3341 | 0.9486 | | 0.0057 | 38.0 | 97850 | 0.3225 | 0.9466 | | 0.0409 | 39.0 | 100425 | 0.3206 | 0.9483 | | 0.1052 | 40.0 | 103000 | 0.3212 | 0.9494 | | 0.0943 | 41.0 | 105575 | 0.3075 | 0.9508 | | 0.0018 | 42.0 | 108150 | 0.3062 | 0.9519 | | 0.0287 | 43.0 | 110725 | 0.3224 | 0.9469 | | 0.0384 | 44.0 | 113300 | 0.3086 | 0.9488 | | 0.1214 | 45.0 | 115875 | 0.3145 | 0.9494 | | 0.1735 | 46.0 | 118450 | 0.3191 | 0.9494 | | 0.0477 | 47.0 | 121025 | 0.3004 | 0.9521 | | 0.0221 | 48.0 | 123600 | 0.3205 | 0.9480 | | 0.0939 | 49.0 | 126175 | 0.3431 | 0.9486 | | 0.0599 | 50.0 | 128750 | 0.3167 | 0.9516 | | 0.1785 | 51.0 | 131325 | 0.3274 | 0.9513 | | 0.1039 | 52.0 | 133900 | 0.3114 | 0.9519 | | 0.0527 | 53.0 | 136475 | 0.3252 | 0.9477 | | 0.0584 | 54.0 | 139050 | 0.3200 | 0.9510 | | 0.1022 | 55.0 | 141625 | 0.3284 | 0.9491 | | 0.013 | 56.0 | 144200 | 0.3386 | 0.9475 | | 0.0488 | 57.0 | 146775 | 0.3290 | 0.9505 | | 0.0514 | 58.0 | 149350 | 0.3126 | 0.9535 | | 0.0184 | 59.0 | 151925 | 0.3196 | 0.9532 | | 0.1233 | 60.0 | 154500 | 0.3270 | 0.9516 | | 0.1667 | 61.0 | 157075 | 0.3250 | 0.9502 | | 0.0497 | 62.0 | 159650 | 0.3375 | 0.9466 | | 0.0445 | 63.0 | 162225 | 0.3493 | 0.9502 | | 0.114 | 64.0 | 164800 | 0.3368 | 0.9488 | | 0.048 | 65.0 | 167375 | 0.3358 | 0.9510 | | 0.2337 | 66.0 | 169950 | 0.3330 | 0.9510 | | 0.0705 | 67.0 | 172525 | 0.3480 | 0.9510 | | 0.094 | 68.0 | 175100 | 0.3508 | 0.9497 | | 0.0498 | 69.0 | 177675 | 0.3328 | 0.9508 | | 0.0535 | 70.0 | 180250 | 0.3558 | 0.9499 | | 0.0217 | 71.0 | 182825 | 0.3583 | 0.9488 | | 0.0264 | 72.0 | 185400 | 0.3600 | 0.9477 | | 0.0108 | 73.0 | 187975 | 0.3629 | 0.9491 | | 0.0446 | 74.0 | 190550 | 0.3570 | 0.9508 | | 0.0702 | 75.0 | 193125 | 0.3600 | 0.9502 | | 0.141 | 76.0 | 195700 | 0.3428 | 0.9527 | | 0.0226 | 77.0 | 198275 | 0.3594 | 0.9502 | | 0.0055 | 78.0 | 200850 | 0.3653 | 0.9508 | | 0.1442 | 79.0 | 203425 | 0.3437 | 0.9530 | | 0.0834 | 80.0 | 206000 | 0.3431 | 0.9524 | | 0.0388 | 81.0 | 208575 | 0.3426 | 0.9521 | | 0.0321 | 82.0 | 211150 | 0.3555 | 0.9497 | | 0.051 | 83.0 | 213725 | 0.3730 | 0.9505 | | 0.0049 | 84.0 | 216300 | 0.3549 | 0.9527 | | 0.043 | 85.0 | 218875 | 0.3592 | 0.9524 | | 0.0284 | 86.0 | 221450 | 0.3749 | 0.9499 | | 0.0923 | 87.0 | 224025 | 0.3527 | 0.9513 | | 0.1188 | 88.0 | 226600 | 0.3725 | 0.9486 | | 0.1493 | 89.0 | 229175 | 0.3560 | 0.9521 | | 0.0164 | 90.0 | 231750 | 0.3573 | 0.9508 | | 0.0477 | 91.0 | 234325 | 0.3679 | 0.9502 | | 0.0827 | 92.0 | 236900 | 0.3683 | 0.9486 | | 0.0799 | 93.0 | 239475 | 0.3667 | 0.9510 | | 0.0413 | 94.0 | 242050 | 0.3604 | 0.9516 | | 0.071 | 95.0 | 244625 | 0.3725 | 0.9483 | | 0.2079 | 96.0 | 247200 | 0.3688 | 0.9483 | | 0.0665 | 97.0 | 249775 | 0.3576 | 0.9521 | | 0.0673 | 98.0 | 252350 | 0.3636 | 0.9513 | | 0.062 | 99.0 | 254925 | 0.3688 | 0.9513 | | 0.1217 | 100.0 | 257500 | 0.3742 | 0.9508 | | 0.0951 | 101.0 | 260075 | 0.3718 | 0.9491 | | 0.0118 | 102.0 | 262650 | 0.3849 | 0.9491 | | 0.0307 | 103.0 | 265225 | 0.3644 | 0.9535 | | 0.0157 | 104.0 | 267800 | 0.3647 | 0.9524 | | 0.0125 | 105.0 | 270375 | 0.3994 | 0.9486 | | 0.0213 | 106.0 | 272950 | 0.3775 | 0.9499 | | 0.1249 | 107.0 | 275525 | 0.3902 | 0.9491 | | 0.0333 | 108.0 | 278100 | 0.3637 | 0.9516 | | 0.0545 | 109.0 | 280675 | 0.3663 | 0.9521 | | 0.1136 | 110.0 | 283250 | 0.3847 | 0.9502 | | 0.0751 | 111.0 | 285825 | 0.3818 | 0.9513 | | 0.001 | 112.0 | 288400 | 0.3811 | 0.9521 | | 0.0282 | 113.0 | 290975 | 0.3843 | 0.9510 | | 0.1117 | 114.0 | 293550 | 0.3790 | 0.9521 | | 0.0022 | 115.0 | 296125 | 0.3717 | 0.9521 | | 0.0203 | 116.0 | 298700 | 0.3794 | 0.9530 | | 0.0437 | 117.0 | 301275 | 0.3807 | 0.9527 | | 0.0045 | 118.0 | 303850 | 0.3821 | 0.9530 | | 0.0015 | 119.0 | 306425 | 0.3867 | 0.9527 | | 0.1152 | 120.0 | 309000 | 0.3842 | 0.9521 | | 0.0748 | 121.0 | 311575 | 0.3839 | 0.9527 | | 0.0955 | 122.0 | 314150 | 0.3805 | 0.9516 | | 0.0043 | 123.0 | 316725 | 0.3833 | 0.9521 | | 0.0249 | 124.0 | 319300 | 0.3745 | 0.9497 | | 0.0002 | 125.0 | 321875 | 0.3744 | 0.9519 | | 0.0169 | 126.0 | 324450 | 0.3808 | 0.9510 | | 0.0277 | 127.0 | 327025 | 0.3735 | 0.9524 | | 0.0082 | 128.0 | 329600 | 0.3831 | 0.9527 | | 0.0737 | 129.0 | 332175 | 0.3891 | 0.9524 | | 0.0517 | 130.0 | 334750 | 0.3839 | 0.9530 | | 0.0218 | 131.0 | 337325 | 0.3863 | 0.9527 | | 0.0228 | 132.0 | 339900 | 0.3913 | 0.9519 | | 0.0094 | 133.0 | 342475 | 0.3968 | 0.9513 | | 0.0784 | 134.0 | 345050 | 0.3871 | 0.9532 | | 0.0116 | 135.0 | 347625 | 0.3890 | 0.9538 | | 0.015 | 136.0 | 350200 | 0.3846 | 0.9530 | | 0.0307 | 137.0 | 352775 | 0.3850 | 0.9530 | | 0.0081 | 138.0 | 355350 | 0.3852 | 0.9532 | | 0.0705 | 139.0 | 357925 | 0.3859 | 0.9527 | | 0.0442 | 140.0 | 360500 | 0.3871 | 0.9524 | | 0.0888 | 141.0 | 363075 | 0.3851 | 0.9535 | | 0.0169 | 142.0 | 365650 | 0.3908 | 0.9527 | | 0.0132 | 143.0 | 368225 | 0.3923 | 0.9527 | | 0.0349 | 144.0 | 370800 | 0.3880 | 0.9527 | | 0.0014 | 145.0 | 373375 | 0.3875 | 0.9535 | | 0.0495 | 146.0 | 375950 | 0.3898 | 0.9535 | | 0.0006 | 147.0 | 378525 | 0.3908 | 0.9530 | | 0.0226 | 148.0 | 381100 | 0.3899 | 0.9527 | | 0.0927 | 149.0 | 383675 | 0.3895 | 0.9527 | | 0.081 | 150.0 | 386250 | 0.3896 | 0.9527 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "diffuse_nebula", "galaxy", "sun", "supernova_remnant", "globular_cluster", "jupiter", "mars", "milky_way", "moon", "open_cluster", "planetary_nebula", "saturn" ]

hkivancoral/hushem_5x_deit_base_adamax_00001_fold3

# hushem_5x_deit_base_adamax_00001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4419 - Accuracy: 0.8372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3275 | 1.0 | 28 | 1.2372 | 0.5814 | | 1.0641 | 2.0 | 56 | 1.0484 | 0.6977 | | 0.7591 | 3.0 | 84 | 0.8760 | 0.7442 | | 0.5652 | 4.0 | 112 | 0.7360 | 0.8140 | | 0.3906 | 5.0 | 140 | 0.6489 | 0.8372 | | 0.3059 | 6.0 | 168 | 0.5954 | 0.8605 | | 0.1994 | 7.0 | 196 | 0.5269 | 0.8372 | | 0.134 | 8.0 | 224 | 0.5174 | 0.8605 | | 0.0783 | 9.0 | 252 | 0.4602 | 0.8605 | | 0.0454 | 10.0 | 280 | 0.4569 | 0.8372 | | 0.0318 | 11.0 | 308 | 0.4393 | 0.8837 | | 0.018 | 12.0 | 336 | 0.4222 | 0.8605 | | 0.0132 | 13.0 | 364 | 0.4453 | 0.8837 | | 0.0088 | 14.0 | 392 | 0.4098 | 0.8837 | | 0.0068 | 15.0 | 420 | 0.4226 | 0.8605 | | 0.0058 | 16.0 | 448 | 0.4268 | 0.8605 | | 0.0055 | 17.0 | 476 | 0.4132 | 0.8605 | | 0.0045 | 18.0 | 504 | 0.4342 | 0.8605 | | 0.004 | 19.0 | 532 | 0.4228 | 0.8605 | | 0.0033 | 20.0 | 560 | 0.4271 | 0.8372 | | 0.0033 | 21.0 | 588 | 0.4254 | 0.8372 | | 0.0029 | 22.0 | 616 | 0.4205 | 0.8372 | | 0.0027 | 23.0 | 644 | 0.4207 | 0.8372 | | 0.0024 | 24.0 | 672 | 0.4248 | 0.8605 | | 0.0022 | 25.0 | 700 | 0.4229 | 0.8372 | | 0.0021 | 26.0 | 728 | 0.4293 | 0.8372 | | 0.002 | 27.0 | 756 | 0.4267 | 0.8372 | | 0.002 | 28.0 | 784 | 0.4239 | 0.8605 | | 0.0018 | 29.0 | 812 | 0.4273 | 0.8372 | | 0.0018 | 30.0 | 840 | 0.4313 | 0.8372 | | 0.0016 | 31.0 | 868 | 0.4289 | 0.8372 | | 0.0016 | 32.0 | 896 | 0.4329 | 0.8372 | | 0.0016 | 33.0 | 924 | 0.4313 | 0.8372 | | 0.0014 | 34.0 | 952 | 0.4362 | 0.8372 | | 0.0016 | 35.0 | 980 | 0.4336 | 0.8372 | | 0.0014 | 36.0 | 1008 | 0.4353 | 0.8372 | | 0.0014 | 37.0 | 1036 | 0.4446 | 0.8372 | | 0.0013 | 38.0 | 1064 | 0.4482 | 0.8372 | | 0.0013 | 39.0 | 1092 | 0.4496 | 0.8372 | | 0.0012 | 40.0 | 1120 | 0.4442 | 0.8372 | | 0.0013 | 41.0 | 1148 | 0.4456 | 0.8372 | | 0.0013 | 42.0 | 1176 | 0.4450 | 0.8372 | | 0.0012 | 43.0 | 1204 | 0.4433 | 0.8372 | | 0.0012 | 44.0 | 1232 | 0.4424 | 0.8372 | | 0.0011 | 45.0 | 1260 | 0.4418 | 0.8372 | | 0.0011 | 46.0 | 1288 | 0.4417 | 0.8372 | | 0.0011 | 47.0 | 1316 | 0.4421 | 0.8372 | | 0.0011 | 48.0 | 1344 | 0.4419 | 0.8372 | | 0.0011 | 49.0 | 1372 | 0.4419 | 0.8372 | | 0.0011 | 50.0 | 1400 | 0.4419 | 0.8372 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_adamax_00001_fold4

# hushem_5x_deit_base_adamax_00001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2070 - Accuracy: 0.9286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2779 | 1.0 | 28 | 1.2578 | 0.5 | | 0.9904 | 2.0 | 56 | 1.0864 | 0.5952 | | 0.7136 | 3.0 | 84 | 0.8757 | 0.7381 | | 0.5283 | 4.0 | 112 | 0.7271 | 0.8095 | | 0.3401 | 5.0 | 140 | 0.5900 | 0.8333 | | 0.2667 | 6.0 | 168 | 0.4970 | 0.8571 | | 0.1719 | 7.0 | 196 | 0.4291 | 0.8810 | | 0.1351 | 8.0 | 224 | 0.3736 | 0.8810 | | 0.0756 | 9.0 | 252 | 0.3239 | 0.8571 | | 0.0457 | 10.0 | 280 | 0.2724 | 0.9286 | | 0.0311 | 11.0 | 308 | 0.2513 | 0.9286 | | 0.0183 | 12.0 | 336 | 0.2397 | 0.9524 | | 0.0115 | 13.0 | 364 | 0.2242 | 0.9286 | | 0.0092 | 14.0 | 392 | 0.2124 | 0.9286 | | 0.0064 | 15.0 | 420 | 0.2027 | 0.9286 | | 0.0052 | 16.0 | 448 | 0.2099 | 0.9286 | | 0.0048 | 17.0 | 476 | 0.2208 | 0.9286 | | 0.0041 | 18.0 | 504 | 0.2156 | 0.9286 | | 0.0036 | 19.0 | 532 | 0.2081 | 0.9286 | | 0.0034 | 20.0 | 560 | 0.2100 | 0.9286 | | 0.003 | 21.0 | 588 | 0.2099 | 0.9286 | | 0.0028 | 22.0 | 616 | 0.2113 | 0.9286 | | 0.0024 | 23.0 | 644 | 0.2110 | 0.9286 | | 0.0023 | 24.0 | 672 | 0.2106 | 0.9286 | | 0.0022 | 25.0 | 700 | 0.2101 | 0.9286 | | 0.002 | 26.0 | 728 | 0.2088 | 0.9286 | | 0.002 | 27.0 | 756 | 0.2066 | 0.9286 | | 0.0018 | 28.0 | 784 | 0.2096 | 0.9286 | | 0.0018 | 29.0 | 812 | 0.2064 | 0.9286 | | 0.0016 | 30.0 | 840 | 0.2088 | 0.9286 | | 0.0016 | 31.0 | 868 | 0.2088 | 0.9286 | | 0.0015 | 32.0 | 896 | 0.2078 | 0.9286 | | 0.0015 | 33.0 | 924 | 0.2057 | 0.9286 | | 0.0014 | 34.0 | 952 | 0.2073 | 0.9286 | | 0.0014 | 35.0 | 980 | 0.2070 | 0.9286 | | 0.0014 | 36.0 | 1008 | 0.2069 | 0.9286 | | 0.0013 | 37.0 | 1036 | 0.2071 | 0.9286 | | 0.0013 | 38.0 | 1064 | 0.2055 | 0.9286 | | 0.0013 | 39.0 | 1092 | 0.2077 | 0.9286 | | 0.0011 | 40.0 | 1120 | 0.2076 | 0.9286 | | 0.0012 | 41.0 | 1148 | 0.2068 | 0.9286 | | 0.0012 | 42.0 | 1176 | 0.2086 | 0.9286 | | 0.0011 | 43.0 | 1204 | 0.2084 | 0.9286 | | 0.0011 | 44.0 | 1232 | 0.2077 | 0.9286 | | 0.0011 | 45.0 | 1260 | 0.2078 | 0.9286 | | 0.0011 | 46.0 | 1288 | 0.2072 | 0.9286 | | 0.0011 | 47.0 | 1316 | 0.2070 | 0.9286 | | 0.0011 | 48.0 | 1344 | 0.2070 | 0.9286 | | 0.0012 | 49.0 | 1372 | 0.2070 | 0.9286 | | 0.0011 | 50.0 | 1400 | 0.2070 | 0.9286 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_adamax_00001_fold5

# hushem_5x_deit_base_adamax_00001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5126 - Accuracy: 0.8780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2842 | 1.0 | 28 | 1.2327 | 0.4634 | | 1.0428 | 2.0 | 56 | 1.0859 | 0.5854 | | 0.7338 | 3.0 | 84 | 0.9001 | 0.6585 | | 0.526 | 4.0 | 112 | 0.7803 | 0.6829 | | 0.3644 | 5.0 | 140 | 0.6952 | 0.6829 | | 0.2797 | 6.0 | 168 | 0.5854 | 0.7073 | | 0.1574 | 7.0 | 196 | 0.5546 | 0.7561 | | 0.1102 | 8.0 | 224 | 0.5073 | 0.7805 | | 0.0668 | 9.0 | 252 | 0.4703 | 0.8049 | | 0.0352 | 10.0 | 280 | 0.4612 | 0.8293 | | 0.0202 | 11.0 | 308 | 0.4468 | 0.8537 | | 0.0126 | 12.0 | 336 | 0.4508 | 0.8537 | | 0.0083 | 13.0 | 364 | 0.4495 | 0.8537 | | 0.0078 | 14.0 | 392 | 0.4584 | 0.8780 | | 0.0058 | 15.0 | 420 | 0.4565 | 0.8537 | | 0.0051 | 16.0 | 448 | 0.4617 | 0.8780 | | 0.0041 | 17.0 | 476 | 0.4620 | 0.8537 | | 0.0038 | 18.0 | 504 | 0.4684 | 0.8780 | | 0.0035 | 19.0 | 532 | 0.4714 | 0.8780 | | 0.0033 | 20.0 | 560 | 0.4764 | 0.8780 | | 0.0027 | 21.0 | 588 | 0.4824 | 0.8780 | | 0.0027 | 22.0 | 616 | 0.4828 | 0.8780 | | 0.0023 | 23.0 | 644 | 0.4858 | 0.8780 | | 0.0021 | 24.0 | 672 | 0.4874 | 0.8780 | | 0.002 | 25.0 | 700 | 0.4903 | 0.8780 | | 0.002 | 26.0 | 728 | 0.4912 | 0.8780 | | 0.0019 | 27.0 | 756 | 0.4926 | 0.8780 | | 0.0017 | 28.0 | 784 | 0.4940 | 0.8780 | | 0.0016 | 29.0 | 812 | 0.4962 | 0.8780 | | 0.0016 | 30.0 | 840 | 0.4964 | 0.8780 | | 0.0015 | 31.0 | 868 | 0.4991 | 0.8780 | | 0.0015 | 32.0 | 896 | 0.5002 | 0.8780 | | 0.0013 | 33.0 | 924 | 0.5024 | 0.8780 | | 0.0014 | 34.0 | 952 | 0.5035 | 0.8780 | | 0.0013 | 35.0 | 980 | 0.5045 | 0.8780 | | 0.0013 | 36.0 | 1008 | 0.5049 | 0.8780 | | 0.0012 | 37.0 | 1036 | 0.5067 | 0.8780 | | 0.0012 | 38.0 | 1064 | 0.5085 | 0.8780 | | 0.0012 | 39.0 | 1092 | 0.5083 | 0.8780 | | 0.0011 | 40.0 | 1120 | 0.5095 | 0.8780 | | 0.0011 | 41.0 | 1148 | 0.5101 | 0.8780 | | 0.0011 | 42.0 | 1176 | 0.5103 | 0.8780 | | 0.0011 | 43.0 | 1204 | 0.5116 | 0.8780 | | 0.0011 | 44.0 | 1232 | 0.5122 | 0.8780 | | 0.0011 | 45.0 | 1260 | 0.5120 | 0.8780 | | 0.0011 | 46.0 | 1288 | 0.5123 | 0.8780 | | 0.001 | 47.0 | 1316 | 0.5124 | 0.8780 | | 0.0011 | 48.0 | 1344 | 0.5126 | 0.8780 | | 0.001 | 49.0 | 1372 | 0.5126 | 0.8780 | | 0.0011 | 50.0 | 1400 | 0.5126 | 0.8780 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_00001_fold1

# hushem_5x_deit_base_rms_00001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3013 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7665 | 1.0 | 27 | 0.8626 | 0.6667 | | 0.1538 | 2.0 | 54 | 0.9929 | 0.6444 | | 0.0306 | 3.0 | 81 | 1.0027 | 0.6667 | | 0.0064 | 4.0 | 108 | 0.7842 | 0.7778 | | 0.0027 | 5.0 | 135 | 0.8679 | 0.7556 | | 0.0017 | 6.0 | 162 | 0.8696 | 0.7778 | | 0.0013 | 7.0 | 189 | 0.8740 | 0.8 | | 0.0009 | 8.0 | 216 | 0.8908 | 0.8 | | 0.0007 | 9.0 | 243 | 0.9347 | 0.8 | | 0.0006 | 10.0 | 270 | 0.9426 | 0.8 | | 0.0005 | 11.0 | 297 | 0.9525 | 0.8 | | 0.0004 | 12.0 | 324 | 0.9729 | 0.8 | | 0.0003 | 13.0 | 351 | 0.9639 | 0.8 | | 0.0003 | 14.0 | 378 | 0.9770 | 0.8 | | 0.0002 | 15.0 | 405 | 1.0081 | 0.8 | | 0.0002 | 16.0 | 432 | 1.0083 | 0.8 | | 0.0002 | 17.0 | 459 | 1.0108 | 0.8 | | 0.0002 | 18.0 | 486 | 1.0375 | 0.8 | | 0.0001 | 19.0 | 513 | 1.0395 | 0.8 | | 0.0001 | 20.0 | 540 | 1.0606 | 0.8 | | 0.0001 | 21.0 | 567 | 1.0558 | 0.8 | | 0.0001 | 22.0 | 594 | 1.0825 | 0.8 | | 0.0001 | 23.0 | 621 | 1.0953 | 0.8 | | 0.0001 | 24.0 | 648 | 1.1052 | 0.8 | | 0.0001 | 25.0 | 675 | 1.1190 | 0.8 | | 0.0001 | 26.0 | 702 | 1.1262 | 0.8 | | 0.0001 | 27.0 | 729 | 1.1329 | 0.8 | | 0.0 | 28.0 | 756 | 1.1463 | 0.8 | | 0.0 | 29.0 | 783 | 1.1643 | 0.8 | | 0.0 | 30.0 | 810 | 1.1628 | 0.8 | | 0.0 | 31.0 | 837 | 1.1766 | 0.8 | | 0.0 | 32.0 | 864 | 1.1948 | 0.8 | | 0.0 | 33.0 | 891 | 1.2037 | 0.8 | | 0.0 | 34.0 | 918 | 1.2175 | 0.8 | | 0.0 | 35.0 | 945 | 1.2224 | 0.8 | | 0.0 | 36.0 | 972 | 1.2274 | 0.8 | | 0.0 | 37.0 | 999 | 1.2352 | 0.8 | | 0.0 | 38.0 | 1026 | 1.2512 | 0.8 | | 0.0 | 39.0 | 1053 | 1.2560 | 0.8 | | 0.0 | 40.0 | 1080 | 1.2629 | 0.8 | | 0.0 | 41.0 | 1107 | 1.2729 | 0.8 | | 0.0 | 42.0 | 1134 | 1.2812 | 0.8 | | 0.0 | 43.0 | 1161 | 1.2836 | 0.8 | | 0.0 | 44.0 | 1188 | 1.2893 | 0.8 | | 0.0 | 45.0 | 1215 | 1.2944 | 0.8 | | 0.0 | 46.0 | 1242 | 1.2996 | 0.8 | | 0.0 | 47.0 | 1269 | 1.3018 | 0.8 | | 0.0 | 48.0 | 1296 | 1.3014 | 0.8 | | 0.0 | 49.0 | 1323 | 1.3013 | 0.8 | | 0.0 | 50.0 | 1350 | 1.3013 | 0.8 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_00001_fold2

# hushem_5x_deit_base_rms_00001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6978 - Accuracy: 0.7333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7885 | 1.0 | 27 | 1.0771 | 0.6222 | | 0.1275 | 2.0 | 54 | 1.2167 | 0.6667 | | 0.0229 | 3.0 | 81 | 1.0976 | 0.6667 | | 0.0044 | 4.0 | 108 | 1.1187 | 0.6889 | | 0.0023 | 5.0 | 135 | 1.1431 | 0.7111 | | 0.0016 | 6.0 | 162 | 1.1708 | 0.7111 | | 0.0013 | 7.0 | 189 | 1.2048 | 0.7333 | | 0.0009 | 8.0 | 216 | 1.2211 | 0.7111 | | 0.0007 | 9.0 | 243 | 1.2394 | 0.7333 | | 0.0006 | 10.0 | 270 | 1.2768 | 0.7111 | | 0.0005 | 11.0 | 297 | 1.2868 | 0.7333 | | 0.0004 | 12.0 | 324 | 1.2957 | 0.7333 | | 0.0003 | 13.0 | 351 | 1.3184 | 0.7333 | | 0.0003 | 14.0 | 378 | 1.3307 | 0.7333 | | 0.0003 | 15.0 | 405 | 1.3520 | 0.7333 | | 0.0002 | 16.0 | 432 | 1.3706 | 0.7333 | | 0.0002 | 17.0 | 459 | 1.3793 | 0.7333 | | 0.0002 | 18.0 | 486 | 1.3973 | 0.7333 | | 0.0001 | 19.0 | 513 | 1.4099 | 0.7333 | | 0.0001 | 20.0 | 540 | 1.4201 | 0.7333 | | 0.0001 | 21.0 | 567 | 1.4342 | 0.7333 | | 0.0001 | 22.0 | 594 | 1.4529 | 0.7333 | | 0.0001 | 23.0 | 621 | 1.4659 | 0.7333 | | 0.0001 | 24.0 | 648 | 1.4737 | 0.7333 | | 0.0001 | 25.0 | 675 | 1.4832 | 0.7333 | | 0.0001 | 26.0 | 702 | 1.5042 | 0.7333 | | 0.0001 | 27.0 | 729 | 1.5167 | 0.7333 | | 0.0001 | 28.0 | 756 | 1.5245 | 0.7333 | | 0.0 | 29.0 | 783 | 1.5454 | 0.7333 | | 0.0 | 30.0 | 810 | 1.5513 | 0.7333 | | 0.0 | 31.0 | 837 | 1.5670 | 0.7333 | | 0.0 | 32.0 | 864 | 1.5721 | 0.7333 | | 0.0 | 33.0 | 891 | 1.5854 | 0.7333 | | 0.0 | 34.0 | 918 | 1.5951 | 0.7333 | | 0.0 | 35.0 | 945 | 1.6025 | 0.7333 | | 0.0 | 36.0 | 972 | 1.6174 | 0.7333 | | 0.0 | 37.0 | 999 | 1.6316 | 0.7333 | | 0.0 | 38.0 | 1026 | 1.6382 | 0.7333 | | 0.0 | 39.0 | 1053 | 1.6457 | 0.7333 | | 0.0 | 40.0 | 1080 | 1.6550 | 0.7333 | | 0.0 | 41.0 | 1107 | 1.6656 | 0.7333 | | 0.0 | 42.0 | 1134 | 1.6708 | 0.7333 | | 0.0 | 43.0 | 1161 | 1.6757 | 0.7333 | | 0.0 | 44.0 | 1188 | 1.6846 | 0.7333 | | 0.0 | 45.0 | 1215 | 1.6907 | 0.7333 | | 0.0 | 46.0 | 1242 | 1.6942 | 0.7333 | | 0.0 | 47.0 | 1269 | 1.6967 | 0.7333 | | 0.0 | 48.0 | 1296 | 1.6977 | 0.7333 | | 0.0 | 49.0 | 1323 | 1.6978 | 0.7333 | | 0.0 | 50.0 | 1350 | 1.6978 | 0.7333 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

phuong-tk-nguyen/resnet-50-finetuned-cifar10-finetuned-cifar10

# resnet-50-finetuned-cifar10-finetuned-cifar10 This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.7363 - Accuracy: 0.561 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9934 | 0.14 | 10 | 1.8738 | 0.517 | | 1.9493 | 0.28 | 20 | 1.8358 | 0.532 | | 1.9328 | 0.43 | 30 | 1.7941 | 0.515 | | 1.9175 | 0.57 | 40 | 1.7683 | 0.531 | | 1.8875 | 0.71 | 50 | 1.7649 | 0.545 | | 1.8752 | 0.85 | 60 | 1.7309 | 0.559 | | 1.8881 | 0.99 | 70 | 1.7363 | 0.561 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

notepsk/food_classifier

# notepsk/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7870 - Validation Loss: 1.5762 - Train Accuracy: 0.869 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.7870 | 1.5762 | 0.869 | 0 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

hkivancoral/hushem_5x_deit_base_rms_00001_fold3

# hushem_5x_deit_base_rms_00001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6600 - Accuracy: 0.8837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7956 | 1.0 | 28 | 0.6205 | 0.7674 | | 0.1411 | 2.0 | 56 | 0.3123 | 0.8837 | | 0.014 | 3.0 | 84 | 0.3844 | 0.9070 | | 0.0034 | 4.0 | 112 | 0.3408 | 0.8837 | | 0.0022 | 5.0 | 140 | 0.4472 | 0.8605 | | 0.0015 | 6.0 | 168 | 0.3917 | 0.8605 | | 0.0011 | 7.0 | 196 | 0.3836 | 0.8837 | | 0.0008 | 8.0 | 224 | 0.4047 | 0.8837 | | 0.0006 | 9.0 | 252 | 0.4079 | 0.8605 | | 0.0005 | 10.0 | 280 | 0.4138 | 0.8837 | | 0.0004 | 11.0 | 308 | 0.4271 | 0.8837 | | 0.0004 | 12.0 | 336 | 0.4048 | 0.8837 | | 0.0003 | 13.0 | 364 | 0.4452 | 0.8837 | | 0.0002 | 14.0 | 392 | 0.4491 | 0.8837 | | 0.0002 | 15.0 | 420 | 0.4640 | 0.8837 | | 0.0002 | 16.0 | 448 | 0.4755 | 0.8837 | | 0.0002 | 17.0 | 476 | 0.4421 | 0.8837 | | 0.0001 | 18.0 | 504 | 0.4868 | 0.8837 | | 0.0001 | 19.0 | 532 | 0.5095 | 0.8837 | | 0.0001 | 20.0 | 560 | 0.5094 | 0.8837 | | 0.0001 | 21.0 | 588 | 0.5135 | 0.8837 | | 0.0001 | 22.0 | 616 | 0.5162 | 0.8837 | | 0.0001 | 23.0 | 644 | 0.5296 | 0.8837 | | 0.0001 | 24.0 | 672 | 0.5403 | 0.8837 | | 0.0001 | 25.0 | 700 | 0.5417 | 0.8837 | | 0.0001 | 26.0 | 728 | 0.5505 | 0.8837 | | 0.0 | 27.0 | 756 | 0.5557 | 0.8837 | | 0.0 | 28.0 | 784 | 0.5868 | 0.8837 | | 0.0 | 29.0 | 812 | 0.5803 | 0.8837 | | 0.0 | 30.0 | 840 | 0.5730 | 0.8837 | | 0.0 | 31.0 | 868 | 0.5921 | 0.8837 | | 0.0 | 32.0 | 896 | 0.5971 | 0.8837 | | 0.0 | 33.0 | 924 | 0.5949 | 0.8837 | | 0.0 | 34.0 | 952 | 0.6083 | 0.8837 | | 0.0 | 35.0 | 980 | 0.5834 | 0.8837 | | 0.0 | 36.0 | 1008 | 0.6025 | 0.8605 | | 0.0 | 37.0 | 1036 | 0.6316 | 0.8837 | | 0.0 | 38.0 | 1064 | 0.6619 | 0.8837 | | 0.0 | 39.0 | 1092 | 0.6540 | 0.8837 | | 0.0 | 40.0 | 1120 | 0.6507 | 0.8837 | | 0.0 | 41.0 | 1148 | 0.6507 | 0.8837 | | 0.0 | 42.0 | 1176 | 0.6547 | 0.8837 | | 0.0 | 43.0 | 1204 | 0.6523 | 0.8837 | | 0.0 | 44.0 | 1232 | 0.6524 | 0.8837 | | 0.0 | 45.0 | 1260 | 0.6538 | 0.8837 | | 0.0 | 46.0 | 1288 | 0.6554 | 0.8837 | | 0.0 | 47.0 | 1316 | 0.6605 | 0.8837 | | 0.0 | 48.0 | 1344 | 0.6600 | 0.8837 | | 0.0 | 49.0 | 1372 | 0.6600 | 0.8837 | | 0.0 | 50.0 | 1400 | 0.6600 | 0.8837 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_00001_fold4

# hushem_5x_deit_base_rms_00001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1652 - Accuracy: 0.9524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8112 | 1.0 | 28 | 0.6674 | 0.6667 | | 0.1706 | 2.0 | 56 | 0.2999 | 0.8810 | | 0.0445 | 3.0 | 84 | 0.2095 | 0.9524 | | 0.0064 | 4.0 | 112 | 0.0984 | 0.9762 | | 0.0024 | 5.0 | 140 | 0.0814 | 0.9762 | | 0.0019 | 6.0 | 168 | 0.0875 | 0.9762 | | 0.0013 | 7.0 | 196 | 0.1085 | 0.9762 | | 0.001 | 8.0 | 224 | 0.0872 | 0.9762 | | 0.0008 | 9.0 | 252 | 0.0829 | 0.9762 | | 0.0006 | 10.0 | 280 | 0.0900 | 0.9762 | | 0.0005 | 11.0 | 308 | 0.0916 | 0.9762 | | 0.0004 | 12.0 | 336 | 0.0984 | 0.9762 | | 0.0004 | 13.0 | 364 | 0.0990 | 0.9762 | | 0.0003 | 14.0 | 392 | 0.0990 | 0.9762 | | 0.0003 | 15.0 | 420 | 0.0986 | 0.9762 | | 0.0002 | 16.0 | 448 | 0.1009 | 0.9762 | | 0.0002 | 17.0 | 476 | 0.1045 | 0.9762 | | 0.0002 | 18.0 | 504 | 0.1034 | 0.9762 | | 0.0001 | 19.0 | 532 | 0.1105 | 0.9762 | | 0.0001 | 20.0 | 560 | 0.1077 | 0.9762 | | 0.0001 | 21.0 | 588 | 0.1166 | 0.9762 | | 0.0001 | 22.0 | 616 | 0.1228 | 0.9762 | | 0.0001 | 23.0 | 644 | 0.1152 | 0.9762 | | 0.0001 | 24.0 | 672 | 0.1166 | 0.9762 | | 0.0001 | 25.0 | 700 | 0.1199 | 0.9762 | | 0.0001 | 26.0 | 728 | 0.1209 | 0.9762 | | 0.0001 | 27.0 | 756 | 0.1278 | 0.9762 | | 0.0001 | 28.0 | 784 | 0.1240 | 0.9762 | | 0.0 | 29.0 | 812 | 0.1343 | 0.9762 | | 0.0 | 30.0 | 840 | 0.1301 | 0.9762 | | 0.0 | 31.0 | 868 | 0.1433 | 0.9762 | | 0.0 | 32.0 | 896 | 0.1322 | 0.9762 | | 0.0 | 33.0 | 924 | 0.1376 | 0.9762 | | 0.0 | 34.0 | 952 | 0.1447 | 0.9524 | | 0.0 | 35.0 | 980 | 0.1392 | 0.9762 | | 0.0 | 36.0 | 1008 | 0.1480 | 0.9524 | | 0.0 | 37.0 | 1036 | 0.1479 | 0.9762 | | 0.0 | 38.0 | 1064 | 0.1555 | 0.9524 | | 0.0 | 39.0 | 1092 | 0.1571 | 0.9524 | | 0.0 | 40.0 | 1120 | 0.1585 | 0.9524 | | 0.0 | 41.0 | 1148 | 0.1656 | 0.9524 | | 0.0 | 42.0 | 1176 | 0.1596 | 0.9524 | | 0.0 | 43.0 | 1204 | 0.1611 | 0.9524 | | 0.0 | 44.0 | 1232 | 0.1624 | 0.9524 | | 0.0 | 45.0 | 1260 | 0.1633 | 0.9524 | | 0.0 | 46.0 | 1288 | 0.1648 | 0.9524 | | 0.0 | 47.0 | 1316 | 0.1649 | 0.9524 | | 0.0 | 48.0 | 1344 | 0.1652 | 0.9524 | | 0.0 | 49.0 | 1372 | 0.1652 | 0.9524 | | 0.0 | 50.0 | 1400 | 0.1652 | 0.9524 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_00001_fold5

# hushem_5x_deit_base_rms_00001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6351 - Accuracy: 0.9024 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8457 | 1.0 | 28 | 0.5947 | 0.7317 | | 0.1932 | 2.0 | 56 | 0.3789 | 0.8780 | | 0.0378 | 3.0 | 84 | 0.3371 | 0.9024 | | 0.0061 | 4.0 | 112 | 0.3727 | 0.9024 | | 0.0027 | 5.0 | 140 | 0.3487 | 0.9024 | | 0.0018 | 6.0 | 168 | 0.3750 | 0.9024 | | 0.0012 | 7.0 | 196 | 0.3872 | 0.9024 | | 0.0009 | 8.0 | 224 | 0.3976 | 0.9024 | | 0.0007 | 9.0 | 252 | 0.4053 | 0.9024 | | 0.0006 | 10.0 | 280 | 0.4125 | 0.9024 | | 0.0005 | 11.0 | 308 | 0.4192 | 0.9024 | | 0.0004 | 12.0 | 336 | 0.4329 | 0.9024 | | 0.0003 | 13.0 | 364 | 0.4400 | 0.9024 | | 0.0003 | 14.0 | 392 | 0.4408 | 0.9024 | | 0.0002 | 15.0 | 420 | 0.4473 | 0.9024 | | 0.0002 | 16.0 | 448 | 0.4630 | 0.9024 | | 0.0002 | 17.0 | 476 | 0.4703 | 0.9024 | | 0.0002 | 18.0 | 504 | 0.4685 | 0.9024 | | 0.0001 | 19.0 | 532 | 0.4848 | 0.9024 | | 0.0001 | 20.0 | 560 | 0.5034 | 0.9024 | | 0.0001 | 21.0 | 588 | 0.5008 | 0.9024 | | 0.0001 | 22.0 | 616 | 0.5129 | 0.9024 | | 0.0001 | 23.0 | 644 | 0.5167 | 0.9024 | | 0.0001 | 24.0 | 672 | 0.5213 | 0.9024 | | 0.0001 | 25.0 | 700 | 0.5209 | 0.9024 | | 0.0001 | 26.0 | 728 | 0.5340 | 0.9024 | | 0.0001 | 27.0 | 756 | 0.5439 | 0.9024 | | 0.0 | 28.0 | 784 | 0.5491 | 0.9024 | | 0.0 | 29.0 | 812 | 0.5502 | 0.9024 | | 0.0 | 30.0 | 840 | 0.5577 | 0.9024 | | 0.0 | 31.0 | 868 | 0.5662 | 0.9024 | | 0.0 | 32.0 | 896 | 0.5801 | 0.9024 | | 0.0 | 33.0 | 924 | 0.5760 | 0.9024 | | 0.0 | 34.0 | 952 | 0.5820 | 0.9024 | | 0.0 | 35.0 | 980 | 0.5825 | 0.9024 | | 0.0 | 36.0 | 1008 | 0.5963 | 0.9024 | | 0.0 | 37.0 | 1036 | 0.6052 | 0.9024 | | 0.0 | 38.0 | 1064 | 0.6015 | 0.9024 | | 0.0 | 39.0 | 1092 | 0.6109 | 0.9024 | | 0.0 | 40.0 | 1120 | 0.6162 | 0.9024 | | 0.0 | 41.0 | 1148 | 0.6213 | 0.9024 | | 0.0 | 42.0 | 1176 | 0.6284 | 0.9024 | | 0.0 | 43.0 | 1204 | 0.6259 | 0.9024 | | 0.0 | 44.0 | 1232 | 0.6257 | 0.9024 | | 0.0 | 45.0 | 1260 | 0.6306 | 0.9024 | | 0.0 | 46.0 | 1288 | 0.6336 | 0.9024 | | 0.0 | 47.0 | 1316 | 0.6353 | 0.9024 | | 0.0 | 48.0 | 1344 | 0.6351 | 0.9024 | | 0.0 | 49.0 | 1372 | 0.6351 | 0.9024 | | 0.0 | 50.0 | 1400 | 0.6351 | 0.9024 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_0001_fold1

# hushem_5x_deit_base_rms_0001_fold1 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.9961 - Accuracy: 0.7111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4401 | 1.0 | 27 | 1.3889 | 0.2444 | | 1.4795 | 2.0 | 54 | 1.6032 | 0.2444 | | 1.2229 | 3.0 | 81 | 1.1436 | 0.5111 | | 0.8987 | 4.0 | 108 | 1.0040 | 0.5556 | | 0.4853 | 5.0 | 135 | 1.0534 | 0.6222 | | 0.1456 | 6.0 | 162 | 1.8360 | 0.5556 | | 0.0696 | 7.0 | 189 | 1.2156 | 0.7333 | | 0.0874 | 8.0 | 216 | 0.7950 | 0.7556 | | 0.0365 | 9.0 | 243 | 1.6830 | 0.7111 | | 0.0006 | 10.0 | 270 | 1.6730 | 0.7111 | | 0.0002 | 11.0 | 297 | 1.6991 | 0.7111 | | 0.0002 | 12.0 | 324 | 1.7182 | 0.7111 | | 0.0001 | 13.0 | 351 | 1.7320 | 0.7111 | | 0.0001 | 14.0 | 378 | 1.7414 | 0.7111 | | 0.0001 | 15.0 | 405 | 1.7505 | 0.7111 | | 0.0001 | 16.0 | 432 | 1.7579 | 0.7111 | | 0.0001 | 17.0 | 459 | 1.7666 | 0.7111 | | 0.0001 | 18.0 | 486 | 1.7749 | 0.7111 | | 0.0001 | 19.0 | 513 | 1.7836 | 0.7333 | | 0.0 | 20.0 | 540 | 1.7919 | 0.7333 | | 0.0 | 21.0 | 567 | 1.8002 | 0.7111 | | 0.0 | 22.0 | 594 | 1.8101 | 0.7111 | | 0.0 | 23.0 | 621 | 1.8191 | 0.7111 | | 0.0 | 24.0 | 648 | 1.8264 | 0.7111 | | 0.0 | 25.0 | 675 | 1.8362 | 0.7111 | | 0.0 | 26.0 | 702 | 1.8441 | 0.7111 | | 0.0 | 27.0 | 729 | 1.8521 | 0.7111 | | 0.0 | 28.0 | 756 | 1.8613 | 0.7111 | | 0.0 | 29.0 | 783 | 1.8701 | 0.7111 | | 0.0 | 30.0 | 810 | 1.8780 | 0.7111 | | 0.0 | 31.0 | 837 | 1.8862 | 0.7111 | | 0.0 | 32.0 | 864 | 1.8953 | 0.7111 | | 0.0 | 33.0 | 891 | 1.9042 | 0.7111 | | 0.0 | 34.0 | 918 | 1.9125 | 0.7111 | | 0.0 | 35.0 | 945 | 1.9206 | 0.7111 | | 0.0 | 36.0 | 972 | 1.9289 | 0.7111 | | 0.0 | 37.0 | 999 | 1.9371 | 0.7111 | | 0.0 | 38.0 | 1026 | 1.9452 | 0.7111 | | 0.0 | 39.0 | 1053 | 1.9530 | 0.7111 | | 0.0 | 40.0 | 1080 | 1.9602 | 0.7111 | | 0.0 | 41.0 | 1107 | 1.9674 | 0.7111 | | 0.0 | 42.0 | 1134 | 1.9741 | 0.7111 | | 0.0 | 43.0 | 1161 | 1.9798 | 0.7111 | | 0.0 | 44.0 | 1188 | 1.9852 | 0.7111 | | 0.0 | 45.0 | 1215 | 1.9896 | 0.7111 | | 0.0 | 46.0 | 1242 | 1.9931 | 0.7111 | | 0.0 | 47.0 | 1269 | 1.9953 | 0.7111 | | 0.0 | 48.0 | 1296 | 1.9961 | 0.7111 | | 0.0 | 49.0 | 1323 | 1.9961 | 0.7111 | | 0.0 | 50.0 | 1350 | 1.9961 | 0.7111 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_0001_fold2

# hushem_5x_deit_base_rms_0001_fold2 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.1764 - Accuracy: 0.5333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4732 | 1.0 | 27 | 1.5871 | 0.2667 | | 1.4137 | 2.0 | 54 | 1.4271 | 0.2667 | | 1.462 | 3.0 | 81 | 1.4098 | 0.2667 | | 1.4423 | 4.0 | 108 | 1.4316 | 0.2444 | | 1.4677 | 5.0 | 135 | 1.1736 | 0.6 | | 1.1753 | 6.0 | 162 | 1.3090 | 0.4889 | | 1.0628 | 7.0 | 189 | 1.1008 | 0.4 | | 0.8856 | 8.0 | 216 | 1.3194 | 0.4667 | | 0.7266 | 9.0 | 243 | 1.5517 | 0.4667 | | 0.7206 | 10.0 | 270 | 1.5964 | 0.4222 | | 0.6825 | 11.0 | 297 | 1.9511 | 0.5333 | | 0.6024 | 12.0 | 324 | 1.1289 | 0.5111 | | 0.7093 | 13.0 | 351 | 1.6051 | 0.4667 | | 0.5446 | 14.0 | 378 | 1.0604 | 0.5333 | | 0.4716 | 15.0 | 405 | 2.6293 | 0.5778 | | 0.4728 | 16.0 | 432 | 3.2908 | 0.4889 | | 0.5099 | 17.0 | 459 | 2.0246 | 0.5333 | | 0.4809 | 18.0 | 486 | 3.4545 | 0.5333 | | 0.3484 | 19.0 | 513 | 2.2451 | 0.5111 | | 0.352 | 20.0 | 540 | 2.8572 | 0.4889 | | 0.3258 | 21.0 | 567 | 3.5970 | 0.5556 | | 0.2785 | 22.0 | 594 | 3.6404 | 0.5556 | | 0.3005 | 23.0 | 621 | 3.6333 | 0.5111 | | 0.2089 | 24.0 | 648 | 4.2561 | 0.5333 | | 0.1996 | 25.0 | 675 | 3.8526 | 0.5111 | | 0.1044 | 26.0 | 702 | 4.1245 | 0.5333 | | 0.2042 | 27.0 | 729 | 3.9154 | 0.5556 | | 0.1371 | 28.0 | 756 | 3.3906 | 0.5556 | | 0.1014 | 29.0 | 783 | 4.2534 | 0.5556 | | 0.0761 | 30.0 | 810 | 3.8328 | 0.5778 | | 0.0321 | 31.0 | 837 | 4.5117 | 0.5556 | | 0.1194 | 32.0 | 864 | 4.5296 | 0.5333 | | 0.0072 | 33.0 | 891 | 4.9299 | 0.5333 | | 0.0276 | 34.0 | 918 | 5.0433 | 0.5111 | | 0.0121 | 35.0 | 945 | 4.9519 | 0.5333 | | 0.0051 | 36.0 | 972 | 4.9546 | 0.5333 | | 0.0001 | 37.0 | 999 | 4.9700 | 0.5111 | | 0.0001 | 38.0 | 1026 | 4.9962 | 0.5111 | | 0.0 | 39.0 | 1053 | 5.0319 | 0.5111 | | 0.0 | 40.0 | 1080 | 5.0566 | 0.5111 | | 0.0001 | 41.0 | 1107 | 5.0812 | 0.5333 | | 0.0 | 42.0 | 1134 | 5.1051 | 0.5333 | | 0.0 | 43.0 | 1161 | 5.1228 | 0.5333 | | 0.0 | 44.0 | 1188 | 5.1393 | 0.5333 | | 0.0 | 45.0 | 1215 | 5.1531 | 0.5333 | | 0.0 | 46.0 | 1242 | 5.1647 | 0.5333 | | 0.0 | 47.0 | 1269 | 5.1724 | 0.5333 | | 0.0 | 48.0 | 1296 | 5.1763 | 0.5333 | | 0.0 | 49.0 | 1323 | 5.1764 | 0.5333 | | 0.0 | 50.0 | 1350 | 5.1764 | 0.5333 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_0001_fold3

# hushem_5x_deit_base_rms_0001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4738 - Accuracy: 0.7907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4909 | 1.0 | 28 | 1.4982 | 0.2558 | | 1.0101 | 2.0 | 56 | 0.9495 | 0.6744 | | 0.4042 | 3.0 | 84 | 0.4221 | 0.7442 | | 0.1388 | 4.0 | 112 | 0.7310 | 0.7442 | | 0.1089 | 5.0 | 140 | 1.2245 | 0.8140 | | 0.0672 | 6.0 | 168 | 0.8270 | 0.7907 | | 0.0498 | 7.0 | 196 | 0.7672 | 0.7674 | | 0.0008 | 8.0 | 224 | 1.0591 | 0.7907 | | 0.0002 | 9.0 | 252 | 1.0196 | 0.8140 | | 0.0001 | 10.0 | 280 | 1.0272 | 0.8140 | | 0.0001 | 11.0 | 308 | 1.0400 | 0.8140 | | 0.0001 | 12.0 | 336 | 1.0507 | 0.8140 | | 0.0001 | 13.0 | 364 | 1.0623 | 0.7907 | | 0.0001 | 14.0 | 392 | 1.0738 | 0.7907 | | 0.0001 | 15.0 | 420 | 1.0809 | 0.7907 | | 0.0 | 16.0 | 448 | 1.0928 | 0.7907 | | 0.0 | 17.0 | 476 | 1.1047 | 0.7907 | | 0.0 | 18.0 | 504 | 1.1192 | 0.7907 | | 0.0 | 19.0 | 532 | 1.1317 | 0.7907 | | 0.0 | 20.0 | 560 | 1.1436 | 0.7907 | | 0.0 | 21.0 | 588 | 1.1568 | 0.7907 | | 0.0 | 22.0 | 616 | 1.1654 | 0.7907 | | 0.0 | 23.0 | 644 | 1.1790 | 0.7907 | | 0.0 | 24.0 | 672 | 1.1944 | 0.7907 | | 0.0 | 25.0 | 700 | 1.2077 | 0.7907 | | 0.0 | 26.0 | 728 | 1.2229 | 0.7907 | | 0.0 | 27.0 | 756 | 1.2379 | 0.7907 | | 0.0 | 28.0 | 784 | 1.2518 | 0.7907 | | 0.0 | 29.0 | 812 | 1.2649 | 0.7907 | | 0.0 | 30.0 | 840 | 1.2762 | 0.7907 | | 0.0 | 31.0 | 868 | 1.2927 | 0.7907 | | 0.0 | 32.0 | 896 | 1.3064 | 0.7907 | | 0.0 | 33.0 | 924 | 1.3200 | 0.7907 | | 0.0 | 34.0 | 952 | 1.3332 | 0.7907 | | 0.0 | 35.0 | 980 | 1.3480 | 0.7907 | | 0.0 | 36.0 | 1008 | 1.3592 | 0.7907 | | 0.0 | 37.0 | 1036 | 1.3743 | 0.7907 | | 0.0 | 38.0 | 1064 | 1.3941 | 0.7907 | | 0.0 | 39.0 | 1092 | 1.4057 | 0.7907 | | 0.0 | 40.0 | 1120 | 1.4180 | 0.7907 | | 0.0 | 41.0 | 1148 | 1.4282 | 0.7907 | | 0.0 | 42.0 | 1176 | 1.4383 | 0.7907 | | 0.0 | 43.0 | 1204 | 1.4471 | 0.7907 | | 0.0 | 44.0 | 1232 | 1.4565 | 0.7907 | | 0.0 | 45.0 | 1260 | 1.4629 | 0.7907 | | 0.0 | 46.0 | 1288 | 1.4680 | 0.7907 | | 0.0 | 47.0 | 1316 | 1.4718 | 0.7907 | | 0.0 | 48.0 | 1344 | 1.4738 | 0.7907 | | 0.0 | 49.0 | 1372 | 1.4738 | 0.7907 | | 0.0 | 50.0 | 1400 | 1.4738 | 0.7907 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_5x_deit_base_rms_0001_fold4

# hushem_5x_deit_base_rms_0001_fold4 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5888 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6398 | 1.0 | 28 | 1.4620 | 0.2381 | | 1.4471 | 2.0 | 56 | 1.4867 | 0.2619 | | 1.4043 | 3.0 | 84 | 1.4639 | 0.2381 | | 1.6225 | 4.0 | 112 | 1.1986 | 0.4524 | | 1.0459 | 5.0 | 140 | 1.1310 | 0.4762 | | 0.7275 | 6.0 | 168 | 0.7753 | 0.6429 | | 0.4185 | 7.0 | 196 | 0.5503 | 0.7857 | | 0.2249 | 8.0 | 224 | 0.5491 | 0.8571 | | 0.0749 | 9.0 | 252 | 0.2650 | 0.9286 | | 0.0643 | 10.0 | 280 | 0.5070 | 0.8333 | | 0.083 | 11.0 | 308 | 0.5183 | 0.8810 | | 0.0258 | 12.0 | 336 | 0.5166 | 0.8571 | | 0.0004 | 13.0 | 364 | 0.4395 | 0.9524 | | 0.03 | 14.0 | 392 | 0.5344 | 0.9048 | | 0.0374 | 15.0 | 420 | 1.0859 | 0.8095 | | 0.032 | 16.0 | 448 | 0.4372 | 0.9048 | | 0.0018 | 17.0 | 476 | 0.4691 | 0.9048 | | 0.0319 | 18.0 | 504 | 0.5620 | 0.8810 | | 0.022 | 19.0 | 532 | 0.4782 | 0.9048 | | 0.0002 | 20.0 | 560 | 0.4687 | 0.9048 | | 0.0001 | 21.0 | 588 | 0.4749 | 0.9048 | | 0.0001 | 22.0 | 616 | 0.4799 | 0.9048 | | 0.0001 | 23.0 | 644 | 0.4865 | 0.9048 | | 0.0001 | 24.0 | 672 | 0.4924 | 0.9048 | | 0.0001 | 25.0 | 700 | 0.4977 | 0.9048 | | 0.0001 | 26.0 | 728 | 0.5030 | 0.9048 | | 0.0 | 27.0 | 756 | 0.5085 | 0.9048 | | 0.0 | 28.0 | 784 | 0.5132 | 0.9048 | | 0.0 | 29.0 | 812 | 0.5184 | 0.9048 | | 0.0 | 30.0 | 840 | 0.5233 | 0.9048 | | 0.0 | 31.0 | 868 | 0.5283 | 0.9048 | | 0.0 | 32.0 | 896 | 0.5333 | 0.9048 | | 0.0 | 33.0 | 924 | 0.5383 | 0.9048 | | 0.0 | 34.0 | 952 | 0.5430 | 0.9048 | | 0.0 | 35.0 | 980 | 0.5476 | 0.9048 | | 0.0 | 36.0 | 1008 | 0.5522 | 0.9048 | | 0.0 | 37.0 | 1036 | 0.5569 | 0.9048 | | 0.0 | 38.0 | 1064 | 0.5613 | 0.9048 | | 0.0 | 39.0 | 1092 | 0.5655 | 0.9048 | | 0.0 | 40.0 | 1120 | 0.5694 | 0.9048 | | 0.0 | 41.0 | 1148 | 0.5725 | 0.9048 | | 0.0 | 42.0 | 1176 | 0.5761 | 0.9048 | | 0.0 | 43.0 | 1204 | 0.5794 | 0.9048 | | 0.0 | 44.0 | 1232 | 0.5824 | 0.9048 | | 0.0 | 45.0 | 1260 | 0.5848 | 0.9048 | | 0.0 | 46.0 | 1288 | 0.5868 | 0.9048 | | 0.0 | 47.0 | 1316 | 0.5882 | 0.9048 | | 0.0 | 48.0 | 1344 | 0.5888 | 0.9048 | | 0.0 | 49.0 | 1372 | 0.5888 | 0.9048 | | 0.0 | 50.0 | 1400 | 0.5888 | 0.9048 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

harrytechiz/vit-base-patch16-224-blur_vs_clean

# vit-base-patch16-224-blur_vs_clean This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0714 - Accuracy: 0.9754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0539 | 1.0 | 151 | 0.1078 | 0.9596 | | 0.0611 | 2.0 | 302 | 0.0846 | 0.9698 | | 0.049 | 3.0 | 453 | 0.0714 | 0.9754 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

[ "blur", "clean" ]

phuong-tk-nguyen/vit-base-patch16-224-newly-trained

# vit-base-patch16-224-newly-trained This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - Accuracy: 0.964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2183 | 0.14 | 10 | 1.6296 | 0.629 | | 1.4213 | 0.28 | 20 | 0.8637 | 0.899 | | 0.86 | 0.43 | 30 | 0.4598 | 0.949 | | 0.614 | 0.57 | 40 | 0.2998 | 0.96 | | 0.48 | 0.71 | 50 | 0.2337 | 0.967 | | 0.4123 | 0.85 | 60 | 0.2091 | 0.964 | | 0.4511 | 0.99 | 70 | 0.1996 | 0.964 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

hkivancoral/hushem_5x_deit_base_rms_0001_fold5

# hushem_5x_deit_base_rms_0001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3118 - Accuracy: 0.8537 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5446 | 1.0 | 28 | 1.3850 | 0.2195 | | 1.371 | 2.0 | 56 | 1.0037 | 0.4878 | | 0.7358 | 3.0 | 84 | 0.5519 | 0.7561 | | 0.2869 | 4.0 | 112 | 0.6592 | 0.7561 | | 0.1411 | 5.0 | 140 | 0.6324 | 0.8780 | | 0.0266 | 6.0 | 168 | 0.8126 | 0.8049 | | 0.0011 | 7.0 | 196 | 0.7003 | 0.8537 | | 0.0012 | 8.0 | 224 | 1.2708 | 0.8049 | | 0.0223 | 9.0 | 252 | 0.7784 | 0.8780 | | 0.0109 | 10.0 | 280 | 1.2289 | 0.7805 | | 0.0002 | 11.0 | 308 | 0.9688 | 0.8537 | | 0.03 | 12.0 | 336 | 0.8929 | 0.8537 | | 0.0037 | 13.0 | 364 | 0.7649 | 0.8537 | | 0.0119 | 14.0 | 392 | 0.9677 | 0.8049 | | 0.0001 | 15.0 | 420 | 1.0107 | 0.7805 | | 0.0001 | 16.0 | 448 | 1.0261 | 0.7805 | | 0.0001 | 17.0 | 476 | 1.0390 | 0.7805 | | 0.0001 | 18.0 | 504 | 1.0514 | 0.7805 | | 0.0001 | 19.0 | 532 | 1.0626 | 0.7805 | | 0.0 | 20.0 | 560 | 1.0741 | 0.7805 | | 0.0 | 21.0 | 588 | 1.0847 | 0.7805 | | 0.0 | 22.0 | 616 | 1.0958 | 0.7805 | | 0.0 | 23.0 | 644 | 1.1069 | 0.7805 | | 0.0 | 24.0 | 672 | 1.1169 | 0.7805 | | 0.0 | 25.0 | 700 | 1.1262 | 0.8049 | | 0.0 | 26.0 | 728 | 1.1359 | 0.8049 | | 0.0 | 27.0 | 756 | 1.1455 | 0.8049 | | 0.0 | 28.0 | 784 | 1.1554 | 0.8049 | | 0.0 | 29.0 | 812 | 1.1647 | 0.8049 | | 0.0 | 30.0 | 840 | 1.1746 | 0.8049 | | 0.0 | 31.0 | 868 | 1.1846 | 0.8049 | | 0.0 | 32.0 | 896 | 1.1951 | 0.8049 | | 0.0 | 33.0 | 924 | 1.2053 | 0.8293 | | 0.0 | 34.0 | 952 | 1.2145 | 0.8293 | | 0.0 | 35.0 | 980 | 1.2243 | 0.8537 | | 0.0 | 36.0 | 1008 | 1.2340 | 0.8537 | | 0.0 | 37.0 | 1036 | 1.2436 | 0.8537 | | 0.0 | 38.0 | 1064 | 1.2528 | 0.8537 | | 0.0 | 39.0 | 1092 | 1.2615 | 0.8537 | | 0.0 | 40.0 | 1120 | 1.2699 | 0.8537 | | 0.0 | 41.0 | 1148 | 1.2781 | 0.8537 | | 0.0 | 42.0 | 1176 | 1.2859 | 0.8537 | | 0.0 | 43.0 | 1204 | 1.2920 | 0.8537 | | 0.0 | 44.0 | 1232 | 1.2978 | 0.8537 | | 0.0 | 45.0 | 1260 | 1.3031 | 0.8537 | | 0.0 | 46.0 | 1288 | 1.3073 | 0.8537 | | 0.0 | 47.0 | 1316 | 1.3103 | 0.8537 | | 0.0 | 48.0 | 1344 | 1.3117 | 0.8537 | | 0.0 | 49.0 | 1372 | 1.3118 | 0.8537 | | 0.0 | 50.0 | 1400 | 1.3118 | 0.8537 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

phuong-tk-nguyen/swin-base-patch4-window7-224-in22k-newly-trained

# swin-base-patch4-window7-224-in22k-newly-trained This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1335 - Accuracy: 0.959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2459 | 0.14 | 10 | 1.7346 | 0.575 | | 1.4338 | 0.28 | 20 | 0.7222 | 0.841 | | 0.8059 | 0.43 | 30 | 0.3252 | 0.915 | | 0.5772 | 0.57 | 40 | 0.2071 | 0.942 | | 0.5599 | 0.71 | 50 | 0.1553 | 0.958 | | 0.4473 | 0.85 | 60 | 0.1373 | 0.958 | | 0.4292 | 0.99 | 70 | 0.1335 | 0.959 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

MaksymDrobchak/vit-base-patch16-224-finetuned-flower

# vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]

mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon

# swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon This model is a fine-tuned version of [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co/100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0971 - Accuracy: 0.9652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2504 | 0.99 | 20 | 0.1401 | 0.9512 | | 0.2051 | 1.98 | 40 | 0.1083 | 0.9652 | | 0.1894 | 2.96 | 60 | 0.0939 | 0.9652 | | 0.1115 | 4.0 | 81 | 0.0880 | 0.9686 | | 0.117 | 4.94 | 100 | 0.0971 | 0.9652 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "ambience", "hair_style", "manicure", "massage_room", "others", "pedicure" ]

shubhamWi91/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4049 - Accuracy: 0.8243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5093 | 0.98 | 37 | 0.4578 | 0.7776 | | 0.4411 | 1.99 | 75 | 0.4189 | 0.8131 | | 0.4177 | 2.94 | 111 | 0.4049 | 0.8243 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "cap", "no_cap" ]

dima806/card_type_image_detection

Returns card type given an image with about 66% accuracy. See https://www.kaggle.com/code/dima806/card-types-image-detection-vit for more details. ``` Classification report: precision recall f1-score support ace of clubs 0.8000 0.9474 0.8675 38 ace of diamonds 0.6604 0.9211 0.7692 38 ace of hearts 0.7727 0.8947 0.8293 38 ace of spades 0.6129 1.0000 0.7600 38 eight of clubs 0.6500 0.3421 0.4483 38 eight of diamonds 0.7500 0.5385 0.6269 39 eight of hearts 0.5000 0.1842 0.2692 38 eight of spades 0.7273 0.2105 0.3265 38 five of clubs 0.8438 0.6923 0.7606 39 five of diamonds 0.7750 0.8158 0.7949 38 five of hearts 0.7949 0.8158 0.8052 38 five of spades 0.7368 0.7368 0.7368 38 four of clubs 0.7333 0.8684 0.7952 38 four of diamonds 0.8571 0.6316 0.7273 38 four of hearts 0.7368 0.7368 0.7368 38 four of spades 0.9000 0.6923 0.7826 39 jack of clubs 0.7037 0.5000 0.5846 38 jack of diamonds 0.5806 0.4737 0.5217 38 jack of hearts 0.8889 0.2105 0.3404 38 jack of spades 0.4000 0.2051 0.2712 39 joker 0.9487 0.9737 0.9610 38 king of clubs 0.3721 0.8421 0.5161 38 king of diamonds 0.4865 0.9474 0.6429 38 king of hearts 0.5472 0.7436 0.6304 39 king of spades 0.4203 0.7632 0.5421 38 nine of clubs 0.5909 0.6842 0.6341 38 nine of diamonds 0.8095 0.4474 0.5763 38 nine of hearts 0.5455 0.6154 0.5783 39 nine of spades 0.4615 0.7895 0.5825 38 queen of clubs 0.2727 0.1538 0.1967 39 queen of diamonds 0.6250 0.1282 0.2128 39 queen of hearts 0.6216 0.6053 0.6133 38 queen of spades 0.7353 0.6579 0.6944 38 seven of clubs 0.5333 0.6316 0.5783 38 seven of diamonds 0.3571 0.3947 0.3750 38 seven of hearts 0.7143 0.7895 0.7500 38 seven of spades 0.7742 0.6316 0.6957 38 six of clubs 0.7368 0.7179 0.7273 39 six of diamonds 0.4462 0.7632 0.5631 38 six of hearts 0.8462 0.5789 0.6875 38 six of spades 0.7879 0.6842 0.7324 38 ten of clubs 0.8889 0.6316 0.7385 38 ten of diamonds 0.6136 0.7105 0.6585 38 ten of hearts 0.7021 0.8684 0.7765 38 ten of spades 0.8529 0.7632 0.8056 38 three of clubs 0.7561 0.7949 0.7750 39 three of diamonds 0.7419 0.6053 0.6667 38 three of hearts 0.7273 0.8205 0.7711 39 three of spades 0.6744 0.7632 0.7160 38 two of clubs 0.7179 0.7368 0.7273 38 two of diamonds 0.7667 0.6053 0.6765 38 two of hearts 0.7647 0.6842 0.7222 38 two of spades 0.7949 0.8158 0.8052 38 accuracy 0.6553 2025 macro avg 0.6804 0.6559 0.6431 2025 weighted avg 0.6802 0.6553 0.6427 2025 ```

[ "ace of clubs", "ace of diamonds", "ace of hearts", "ace of spades", "eight of clubs", "eight of diamonds", "eight of hearts", "eight of spades", "five of clubs", "five of diamonds", "five of hearts", "five of spades", "four of clubs", "four of diamonds", "four of hearts", "four of spades", "jack of clubs", "jack of diamonds", "jack of hearts", "jack of spades", "joker", "king of clubs", "king of diamonds", "king of hearts", "king of spades", "nine of clubs", "nine of diamonds", "nine of hearts", "nine of spades", "queen of clubs", "queen of diamonds", "queen of hearts", "queen of spades", "seven of clubs", "seven of diamonds", "seven of hearts", "seven of spades", "six of clubs", "six of diamonds", "six of hearts", "six of spades", "ten of clubs", "ten of diamonds", "ten of hearts", "ten of spades", "three of clubs", "three of diamonds", "three of hearts", "three of spades", "two of clubs", "two of diamonds", "two of hearts", "two of spades" ]

Zendel/my_awesome_food_model

# my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.5709 - Accuracy: 0.918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7167 | 0.99 | 62 | 2.5215 | 0.862 | | 1.8648 | 2.0 | 125 | 1.7438 | 0.891 | | 1.6405 | 2.98 | 186 | 1.5709 | 0.918 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

Artef/results

# results This model is a fine-tuned version of [umm-maybe/AI-image-detector](https://huggingface.co/umm-maybe/AI-image-detector) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1365 - eval_accuracy: 0.9612 - eval_runtime: 20.7986 - eval_samples_per_second: 28.511 - eval_steps_per_second: 3.606 - epoch: 1.86 - step: 550 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.2

[ "artificial", "human" ]

hkivancoral/hushem_5x_beit_base_adamax_001_fold1

# hushem_5x_beit_base_adamax_001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 6.2787 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4418 | 1.0 | 27 | 1.4079 | 0.2444 | | 1.2606 | 2.0 | 54 | 1.4566 | 0.4 | | 1.1141 | 3.0 | 81 | 1.4147 | 0.3111 | | 0.9738 | 4.0 | 108 | 1.7371 | 0.3556 | | 0.7887 | 5.0 | 135 | 1.5516 | 0.3778 | | 0.7198 | 6.0 | 162 | 1.3626 | 0.4 | | 0.8269 | 7.0 | 189 | 1.5448 | 0.3778 | | 0.8171 | 8.0 | 216 | 1.4576 | 0.4 | | 0.7255 | 9.0 | 243 | 2.3915 | 0.3778 | | 0.6369 | 10.0 | 270 | 1.6627 | 0.3778 | | 0.6809 | 11.0 | 297 | 1.5201 | 0.3556 | | 0.6237 | 12.0 | 324 | 1.3289 | 0.4222 | | 0.6768 | 13.0 | 351 | 1.6115 | 0.3556 | | 0.6336 | 14.0 | 378 | 2.0397 | 0.3778 | | 0.5238 | 15.0 | 405 | 1.5857 | 0.3778 | | 0.5016 | 16.0 | 432 | 1.4047 | 0.4444 | | 0.4321 | 17.0 | 459 | 2.2039 | 0.3556 | | 0.4791 | 18.0 | 486 | 2.3823 | 0.3778 | | 0.484 | 19.0 | 513 | 1.4706 | 0.4222 | | 0.4812 | 20.0 | 540 | 1.6485 | 0.4222 | | 0.4413 | 21.0 | 567 | 1.7092 | 0.4 | | 0.4306 | 22.0 | 594 | 1.8582 | 0.4 | | 0.37 | 23.0 | 621 | 1.8653 | 0.3778 | | 0.3048 | 24.0 | 648 | 1.6342 | 0.4444 | | 0.3515 | 25.0 | 675 | 1.5211 | 0.4889 | | 0.3558 | 26.0 | 702 | 1.9714 | 0.4222 | | 0.2599 | 27.0 | 729 | 1.7243 | 0.4667 | | 0.267 | 28.0 | 756 | 1.7049 | 0.5111 | | 0.2625 | 29.0 | 783 | 2.1704 | 0.4222 | | 0.2368 | 30.0 | 810 | 2.2942 | 0.4667 | | 0.2036 | 31.0 | 837 | 2.0691 | 0.4667 | | 0.1938 | 32.0 | 864 | 2.7340 | 0.4 | | 0.1597 | 33.0 | 891 | 3.0661 | 0.4 | | 0.1166 | 34.0 | 918 | 2.8536 | 0.4667 | | 0.1248 | 35.0 | 945 | 2.9508 | 0.4444 | | 0.121 | 36.0 | 972 | 3.2153 | 0.4667 | | 0.0801 | 37.0 | 999 | 3.0021 | 0.4222 | | 0.0529 | 38.0 | 1026 | 3.3247 | 0.4222 | | 0.0434 | 39.0 | 1053 | 4.0394 | 0.4667 | | 0.0599 | 40.0 | 1080 | 4.1062 | 0.4889 | | 0.0437 | 41.0 | 1107 | 5.3485 | 0.4667 | | 0.0045 | 42.0 | 1134 | 5.3122 | 0.4667 | | 0.0368 | 43.0 | 1161 | 5.1937 | 0.4667 | | 0.0032 | 44.0 | 1188 | 5.6803 | 0.4889 | | 0.0061 | 45.0 | 1215 | 5.8620 | 0.4444 | | 0.0035 | 46.0 | 1242 | 5.9016 | 0.4889 | | 0.0011 | 47.0 | 1269 | 6.3136 | 0.4444 | | 0.0277 | 48.0 | 1296 | 6.2816 | 0.4444 | | 0.0067 | 49.0 | 1323 | 6.2787 | 0.4444 | | 0.0372 | 50.0 | 1350 | 6.2787 | 0.4444 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_001_fold1

# hushem_1x_beit_base_adamax_001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 3.6981 - Accuracy: 0.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.5800 | 0.2444 | | 2.0893 | 2.0 | 12 | 1.3869 | 0.3333 | | 2.0893 | 3.0 | 18 | 1.3893 | 0.2444 | | 1.4148 | 4.0 | 24 | 1.3366 | 0.3333 | | 1.3117 | 5.0 | 30 | 1.3938 | 0.2889 | | 1.3117 | 6.0 | 36 | 1.5221 | 0.3778 | | 1.2096 | 7.0 | 42 | 1.7519 | 0.4222 | | 1.2096 | 8.0 | 48 | 1.6213 | 0.2444 | | 1.1162 | 9.0 | 54 | 1.4721 | 0.2889 | | 1.0871 | 10.0 | 60 | 1.3748 | 0.3333 | | 1.0871 | 11.0 | 66 | 1.7274 | 0.4667 | | 1.0753 | 12.0 | 72 | 3.1976 | 0.3333 | | 1.0753 | 13.0 | 78 | 1.3693 | 0.4222 | | 1.2635 | 14.0 | 84 | 1.5090 | 0.3778 | | 0.9248 | 15.0 | 90 | 1.4886 | 0.5333 | | 0.9248 | 16.0 | 96 | 1.4765 | 0.4444 | | 0.8798 | 17.0 | 102 | 1.9348 | 0.4222 | | 0.8798 | 18.0 | 108 | 1.3064 | 0.4667 | | 0.8666 | 19.0 | 114 | 1.5832 | 0.4444 | | 0.7171 | 20.0 | 120 | 2.1360 | 0.4444 | | 0.7171 | 21.0 | 126 | 1.7636 | 0.4444 | | 0.7588 | 22.0 | 132 | 2.3529 | 0.3556 | | 0.7588 | 23.0 | 138 | 2.7880 | 0.3556 | | 0.6002 | 24.0 | 144 | 1.8764 | 0.4222 | | 0.5204 | 25.0 | 150 | 2.9921 | 0.4 | | 0.5204 | 26.0 | 156 | 2.6311 | 0.4444 | | 0.4748 | 27.0 | 162 | 2.1490 | 0.4889 | | 0.4748 | 28.0 | 168 | 2.4874 | 0.4889 | | 0.4423 | 29.0 | 174 | 1.9273 | 0.4444 | | 0.3826 | 30.0 | 180 | 3.0375 | 0.4222 | | 0.3826 | 31.0 | 186 | 3.0775 | 0.4667 | | 0.3486 | 32.0 | 192 | 2.5400 | 0.4 | | 0.3486 | 33.0 | 198 | 3.1424 | 0.4444 | | 0.3116 | 34.0 | 204 | 2.9144 | 0.4667 | | 0.2168 | 35.0 | 210 | 3.3792 | 0.4444 | | 0.2168 | 36.0 | 216 | 3.7895 | 0.4667 | | 0.2383 | 37.0 | 222 | 3.1800 | 0.4889 | | 0.2383 | 38.0 | 228 | 3.3532 | 0.4444 | | 0.1463 | 39.0 | 234 | 3.6524 | 0.4222 | | 0.1584 | 40.0 | 240 | 3.6346 | 0.4444 | | 0.1584 | 41.0 | 246 | 3.6838 | 0.4444 | | 0.1431 | 42.0 | 252 | 3.6981 | 0.4444 | | 0.1431 | 43.0 | 258 | 3.6981 | 0.4444 | | 0.1356 | 44.0 | 264 | 3.6981 | 0.4444 | | 0.139 | 45.0 | 270 | 3.6981 | 0.4444 | | 0.139 | 46.0 | 276 | 3.6981 | 0.4444 | | 0.1502 | 47.0 | 282 | 3.6981 | 0.4444 | | 0.1502 | 48.0 | 288 | 3.6981 | 0.4444 | | 0.128 | 49.0 | 294 | 3.6981 | 0.4444 | | 0.1474 | 50.0 | 300 | 3.6981 | 0.4444 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

Sharon8y/my_hotdog_model

# my_hotdog_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5346 - Accuracy: 0.81 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.95 | 9 | 2.1083 | 0.5967 | | 2.2301 | 2.0 | 19 | 1.8377 | 0.7067 | | 1.9275 | 2.95 | 28 | 1.6582 | 0.78 | | 1.6897 | 4.0 | 38 | 1.5653 | 0.79 | | 1.5374 | 4.74 | 45 | 1.5346 | 0.81 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "baked potato", "burger", "crispy chicken", "donut", "fries", "hot dog", "pizza", "sandwich", "taco", "taquito" ]

hkivancoral/hushem_1x_beit_base_adamax_001_fold2

# hushem_1x_beit_base_adamax_001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3645 - Accuracy: 0.5556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.4123 | 0.2444 | | 1.8567 | 2.0 | 12 | 1.3969 | 0.2444 | | 1.8567 | 3.0 | 18 | 1.3773 | 0.4 | | 1.4001 | 4.0 | 24 | 1.3688 | 0.3778 | | 1.3691 | 5.0 | 30 | 1.3640 | 0.2444 | | 1.3691 | 6.0 | 36 | 1.2556 | 0.5111 | | 1.3116 | 7.0 | 42 | 1.4009 | 0.2667 | | 1.3116 | 8.0 | 48 | 1.2324 | 0.4222 | | 1.1799 | 9.0 | 54 | 1.1289 | 0.5111 | | 1.1098 | 10.0 | 60 | 1.5348 | 0.2667 | | 1.1098 | 11.0 | 66 | 1.2341 | 0.4222 | | 1.0933 | 12.0 | 72 | 1.3191 | 0.4667 | | 1.0933 | 13.0 | 78 | 1.3567 | 0.4 | | 0.986 | 14.0 | 84 | 1.1728 | 0.3778 | | 0.9075 | 15.0 | 90 | 1.1993 | 0.5111 | | 0.9075 | 16.0 | 96 | 1.1869 | 0.3556 | | 0.8205 | 17.0 | 102 | 1.3241 | 0.5333 | | 0.8205 | 18.0 | 108 | 1.2073 | 0.5333 | | 0.9036 | 19.0 | 114 | 1.2788 | 0.4889 | | 0.7712 | 20.0 | 120 | 1.2208 | 0.4667 | | 0.7712 | 21.0 | 126 | 1.2263 | 0.5333 | | 0.6949 | 22.0 | 132 | 1.1609 | 0.4889 | | 0.6949 | 23.0 | 138 | 1.1919 | 0.4222 | | 0.7053 | 24.0 | 144 | 1.2190 | 0.5111 | | 0.6439 | 25.0 | 150 | 1.2569 | 0.5556 | | 0.6439 | 26.0 | 156 | 1.3636 | 0.5333 | | 0.6537 | 27.0 | 162 | 1.4293 | 0.5778 | | 0.6537 | 28.0 | 168 | 1.2396 | 0.5111 | | 0.6181 | 29.0 | 174 | 1.3037 | 0.5556 | | 0.5097 | 30.0 | 180 | 1.3049 | 0.5778 | | 0.5097 | 31.0 | 186 | 1.1406 | 0.5333 | | 0.5782 | 32.0 | 192 | 1.2396 | 0.5333 | | 0.5782 | 33.0 | 198 | 1.2877 | 0.5111 | | 0.5897 | 34.0 | 204 | 1.3944 | 0.5778 | | 0.4972 | 35.0 | 210 | 1.2439 | 0.5556 | | 0.4972 | 36.0 | 216 | 1.2993 | 0.5556 | | 0.4729 | 37.0 | 222 | 1.3034 | 0.5556 | | 0.4729 | 38.0 | 228 | 1.3631 | 0.5556 | | 0.3719 | 39.0 | 234 | 1.4220 | 0.5778 | | 0.4329 | 40.0 | 240 | 1.3836 | 0.5111 | | 0.4329 | 41.0 | 246 | 1.3661 | 0.5556 | | 0.3819 | 42.0 | 252 | 1.3645 | 0.5556 | | 0.3819 | 43.0 | 258 | 1.3645 | 0.5556 | | 0.3664 | 44.0 | 264 | 1.3645 | 0.5556 | | 0.4152 | 45.0 | 270 | 1.3645 | 0.5556 | | 0.4152 | 46.0 | 276 | 1.3645 | 0.5556 | | 0.3637 | 47.0 | 282 | 1.3645 | 0.5556 | | 0.3637 | 48.0 | 288 | 1.3645 | 0.5556 | | 0.394 | 49.0 | 294 | 1.3645 | 0.5556 | | 0.3776 | 50.0 | 300 | 1.3645 | 0.5556 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_001_fold3

# hushem_1x_beit_base_adamax_001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.8097 - Accuracy: 0.5349 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.4381 | 0.2326 | | 2.0527 | 2.0 | 12 | 1.4022 | 0.2558 | | 2.0527 | 3.0 | 18 | 1.3682 | 0.3256 | | 1.3782 | 4.0 | 24 | 1.3387 | 0.3953 | | 1.2679 | 5.0 | 30 | 1.3721 | 0.3256 | | 1.2679 | 6.0 | 36 | 1.7451 | 0.3488 | | 1.2756 | 7.0 | 42 | 1.3183 | 0.3953 | | 1.2756 | 8.0 | 48 | 1.4225 | 0.3023 | | 1.173 | 9.0 | 54 | 1.4215 | 0.3953 | | 1.1959 | 10.0 | 60 | 1.4072 | 0.3721 | | 1.1959 | 11.0 | 66 | 1.4852 | 0.4186 | | 1.1344 | 12.0 | 72 | 1.4523 | 0.2791 | | 1.1344 | 13.0 | 78 | 1.4043 | 0.4651 | | 1.0854 | 14.0 | 84 | 1.3638 | 0.3953 | | 1.1124 | 15.0 | 90 | 1.4323 | 0.3953 | | 1.1124 | 16.0 | 96 | 1.4664 | 0.4884 | | 1.0108 | 17.0 | 102 | 1.5473 | 0.3721 | | 1.0108 | 18.0 | 108 | 1.2300 | 0.4651 | | 0.9443 | 19.0 | 114 | 1.2523 | 0.4419 | | 0.9125 | 20.0 | 120 | 1.4134 | 0.3721 | | 0.9125 | 21.0 | 126 | 1.1280 | 0.4884 | | 0.8328 | 22.0 | 132 | 1.1054 | 0.4884 | | 0.8328 | 23.0 | 138 | 1.6081 | 0.4419 | | 0.7565 | 24.0 | 144 | 1.0331 | 0.5349 | | 0.7135 | 25.0 | 150 | 1.6384 | 0.5116 | | 0.7135 | 26.0 | 156 | 1.9524 | 0.4651 | | 0.7048 | 27.0 | 162 | 1.1399 | 0.5349 | | 0.7048 | 28.0 | 168 | 1.0504 | 0.5581 | | 0.7074 | 29.0 | 174 | 1.0452 | 0.5581 | | 0.7008 | 30.0 | 180 | 1.4757 | 0.5581 | | 0.7008 | 31.0 | 186 | 1.0663 | 0.4419 | | 0.5976 | 32.0 | 192 | 1.0991 | 0.5349 | | 0.5976 | 33.0 | 198 | 1.5330 | 0.5814 | | 0.5565 | 34.0 | 204 | 1.1511 | 0.5349 | | 0.458 | 35.0 | 210 | 1.5836 | 0.5349 | | 0.458 | 36.0 | 216 | 1.4225 | 0.5581 | | 0.5542 | 37.0 | 222 | 1.4182 | 0.6047 | | 0.5542 | 38.0 | 228 | 1.3407 | 0.5581 | | 0.3706 | 39.0 | 234 | 1.4368 | 0.5581 | | 0.3087 | 40.0 | 240 | 1.6899 | 0.5814 | | 0.3087 | 41.0 | 246 | 1.8110 | 0.5116 | | 0.3001 | 42.0 | 252 | 1.8097 | 0.5349 | | 0.3001 | 43.0 | 258 | 1.8097 | 0.5349 | | 0.3061 | 44.0 | 264 | 1.8097 | 0.5349 | | 0.2986 | 45.0 | 270 | 1.8097 | 0.5349 | | 0.2986 | 46.0 | 276 | 1.8097 | 0.5349 | | 0.2791 | 47.0 | 282 | 1.8097 | 0.5349 | | 0.2791 | 48.0 | 288 | 1.8097 | 0.5349 | | 0.2908 | 49.0 | 294 | 1.8097 | 0.5349 | | 0.2986 | 50.0 | 300 | 1.8097 | 0.5349 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_001_fold4

# hushem_1x_beit_base_adamax_001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 4.3503 - Accuracy: 0.4524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.4229 | 0.2381 | | 2.0151 | 2.0 | 12 | 1.3893 | 0.2619 | | 2.0151 | 3.0 | 18 | 1.3408 | 0.3333 | | 1.3963 | 4.0 | 24 | 1.3326 | 0.3095 | | 1.3169 | 5.0 | 30 | 1.2412 | 0.4762 | | 1.3169 | 6.0 | 36 | 1.0247 | 0.5476 | | 1.2588 | 7.0 | 42 | 1.2101 | 0.3571 | | 1.2588 | 8.0 | 48 | 1.0013 | 0.5238 | | 1.1685 | 9.0 | 54 | 1.3288 | 0.4524 | | 1.1624 | 10.0 | 60 | 1.0173 | 0.5 | | 1.1624 | 11.0 | 66 | 1.2213 | 0.4762 | | 1.163 | 12.0 | 72 | 1.3131 | 0.4286 | | 1.163 | 13.0 | 78 | 1.0794 | 0.5238 | | 1.0128 | 14.0 | 84 | 1.2744 | 0.3810 | | 1.1156 | 15.0 | 90 | 1.2253 | 0.5 | | 1.1156 | 16.0 | 96 | 1.2674 | 0.4048 | | 0.9374 | 17.0 | 102 | 1.1623 | 0.4524 | | 0.9374 | 18.0 | 108 | 1.5694 | 0.4048 | | 0.9149 | 19.0 | 114 | 1.0570 | 0.5476 | | 0.912 | 20.0 | 120 | 1.2919 | 0.4286 | | 0.912 | 21.0 | 126 | 1.4307 | 0.5 | | 0.6869 | 22.0 | 132 | 1.5771 | 0.5238 | | 0.6869 | 23.0 | 138 | 2.1692 | 0.3571 | | 0.6883 | 24.0 | 144 | 1.5822 | 0.5714 | | 0.7288 | 25.0 | 150 | 2.0687 | 0.4524 | | 0.7288 | 26.0 | 156 | 2.1992 | 0.4524 | | 0.4823 | 27.0 | 162 | 2.2715 | 0.5238 | | 0.4823 | 28.0 | 168 | 3.3968 | 0.4286 | | 0.4173 | 29.0 | 174 | 2.2538 | 0.5476 | | 0.4253 | 30.0 | 180 | 3.6242 | 0.3810 | | 0.4253 | 31.0 | 186 | 2.4386 | 0.5952 | | 0.3088 | 32.0 | 192 | 3.2728 | 0.4762 | | 0.3088 | 33.0 | 198 | 3.5241 | 0.5476 | | 0.1666 | 34.0 | 204 | 3.5230 | 0.5 | | 0.2645 | 35.0 | 210 | 3.7888 | 0.4286 | | 0.2645 | 36.0 | 216 | 4.2240 | 0.5238 | | 0.1416 | 37.0 | 222 | 4.2393 | 0.5 | | 0.1416 | 38.0 | 228 | 4.0612 | 0.4762 | | 0.1169 | 39.0 | 234 | 4.3686 | 0.4524 | | 0.0781 | 40.0 | 240 | 4.2437 | 0.4762 | | 0.0781 | 41.0 | 246 | 4.2703 | 0.4286 | | 0.06 | 42.0 | 252 | 4.3503 | 0.4524 | | 0.06 | 43.0 | 258 | 4.3503 | 0.4524 | | 0.0264 | 44.0 | 264 | 4.3503 | 0.4524 | | 0.1093 | 45.0 | 270 | 4.3503 | 0.4524 | | 0.1093 | 46.0 | 276 | 4.3503 | 0.4524 | | 0.0479 | 47.0 | 282 | 4.3503 | 0.4524 | | 0.0479 | 48.0 | 288 | 4.3503 | 0.4524 | | 0.0488 | 49.0 | 294 | 4.3503 | 0.4524 | | 0.0619 | 50.0 | 300 | 4.3503 | 0.4524 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_001_fold5

# hushem_1x_beit_base_adamax_001_fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2278 - Accuracy: 0.7317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.4268 | 0.2439 | | 1.7859 | 2.0 | 12 | 1.3982 | 0.2439 | | 1.7859 | 3.0 | 18 | 1.3119 | 0.4878 | | 1.3869 | 4.0 | 24 | 1.2627 | 0.4146 | | 1.329 | 5.0 | 30 | 1.0564 | 0.5610 | | 1.329 | 6.0 | 36 | 1.2486 | 0.2927 | | 1.2971 | 7.0 | 42 | 1.2260 | 0.3415 | | 1.2971 | 8.0 | 48 | 1.1669 | 0.5122 | | 1.2043 | 9.0 | 54 | 1.2078 | 0.4390 | | 1.166 | 10.0 | 60 | 1.1291 | 0.4390 | | 1.166 | 11.0 | 66 | 1.4793 | 0.2683 | | 1.2368 | 12.0 | 72 | 1.1712 | 0.4390 | | 1.2368 | 13.0 | 78 | 1.1600 | 0.4146 | | 1.0841 | 14.0 | 84 | 1.1286 | 0.4146 | | 1.1358 | 15.0 | 90 | 1.0309 | 0.4878 | | 1.1358 | 16.0 | 96 | 1.0536 | 0.3902 | | 1.0304 | 17.0 | 102 | 0.9535 | 0.4878 | | 1.0304 | 18.0 | 108 | 1.1738 | 0.3659 | | 0.9971 | 19.0 | 114 | 0.9220 | 0.5122 | | 0.9482 | 20.0 | 120 | 1.0234 | 0.6829 | | 0.9482 | 21.0 | 126 | 1.0465 | 0.5366 | | 0.9578 | 22.0 | 132 | 1.0713 | 0.5854 | | 0.9578 | 23.0 | 138 | 1.1190 | 0.5122 | | 1.0032 | 24.0 | 144 | 1.0303 | 0.6341 | | 0.9765 | 25.0 | 150 | 0.9143 | 0.6098 | | 0.9765 | 26.0 | 156 | 0.9675 | 0.6098 | | 0.8768 | 27.0 | 162 | 0.8561 | 0.6341 | | 0.8768 | 28.0 | 168 | 1.0406 | 0.4878 | | 0.813 | 29.0 | 174 | 1.2443 | 0.6098 | | 0.8566 | 30.0 | 180 | 0.8255 | 0.6341 | | 0.8566 | 31.0 | 186 | 0.8471 | 0.6829 | | 0.7675 | 32.0 | 192 | 0.9851 | 0.6829 | | 0.7675 | 33.0 | 198 | 1.1042 | 0.6829 | | 0.7167 | 34.0 | 204 | 1.0172 | 0.6829 | | 0.6799 | 35.0 | 210 | 1.1228 | 0.5366 | | 0.6799 | 36.0 | 216 | 1.1880 | 0.7317 | | 0.6558 | 37.0 | 222 | 1.1922 | 0.7317 | | 0.6558 | 38.0 | 228 | 1.4663 | 0.6585 | | 0.5997 | 39.0 | 234 | 1.0459 | 0.7317 | | 0.579 | 40.0 | 240 | 1.1555 | 0.7073 | | 0.579 | 41.0 | 246 | 1.1889 | 0.7073 | | 0.5728 | 42.0 | 252 | 1.2278 | 0.7317 | | 0.5728 | 43.0 | 258 | 1.2278 | 0.7317 | | 0.5177 | 44.0 | 264 | 1.2278 | 0.7317 | | 0.5591 | 45.0 | 270 | 1.2278 | 0.7317 | | 0.5591 | 46.0 | 276 | 1.2278 | 0.7317 | | 0.5528 | 47.0 | 282 | 1.2278 | 0.7317 | | 0.5528 | 48.0 | 288 | 1.2278 | 0.7317 | | 0.575 | 49.0 | 294 | 1.2278 | 0.7317 | | 0.5528 | 50.0 | 300 | 1.2278 | 0.7317 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_0001_fold1

# hushem_1x_beit_base_adamax_0001_fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1942 - Accuracy: 0.7333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.1737 | 0.5111 | | 1.2996 | 2.0 | 12 | 0.6731 | 0.7111 | | 1.2996 | 3.0 | 18 | 0.5816 | 0.7778 | | 0.3034 | 4.0 | 24 | 0.5950 | 0.7778 | | 0.0484 | 5.0 | 30 | 0.7873 | 0.7333 | | 0.0484 | 6.0 | 36 | 0.7472 | 0.7556 | | 0.0106 | 7.0 | 42 | 0.8528 | 0.8 | | 0.0106 | 8.0 | 48 | 0.7211 | 0.7778 | | 0.0205 | 9.0 | 54 | 0.6347 | 0.7778 | | 0.0012 | 10.0 | 60 | 0.6115 | 0.8 | | 0.0012 | 11.0 | 66 | 0.6050 | 0.8222 | | 0.0005 | 12.0 | 72 | 0.6253 | 0.8222 | | 0.0005 | 13.0 | 78 | 0.7723 | 0.8 | | 0.0021 | 14.0 | 84 | 0.9287 | 0.8 | | 0.0003 | 15.0 | 90 | 1.0136 | 0.7778 | | 0.0003 | 16.0 | 96 | 0.9985 | 0.7778 | | 0.0004 | 17.0 | 102 | 0.9348 | 0.7778 | | 0.0004 | 18.0 | 108 | 0.8985 | 0.8 | | 0.0003 | 19.0 | 114 | 0.8733 | 0.8222 | | 0.0009 | 20.0 | 120 | 0.8790 | 0.8222 | | 0.0009 | 21.0 | 126 | 1.1330 | 0.7778 | | 0.0002 | 22.0 | 132 | 1.2620 | 0.7556 | | 0.0002 | 23.0 | 138 | 1.3184 | 0.7556 | | 0.0003 | 24.0 | 144 | 1.3104 | 0.7778 | | 0.0003 | 25.0 | 150 | 1.2554 | 0.7556 | | 0.0003 | 26.0 | 156 | 1.2162 | 0.7556 | | 0.0002 | 27.0 | 162 | 1.1923 | 0.7333 | | 0.0002 | 28.0 | 168 | 1.1869 | 0.7333 | | 0.0002 | 29.0 | 174 | 1.1546 | 0.7333 | | 0.0002 | 30.0 | 180 | 1.1302 | 0.7556 | | 0.0002 | 31.0 | 186 | 1.1214 | 0.7556 | | 0.0003 | 32.0 | 192 | 1.1205 | 0.7556 | | 0.0003 | 33.0 | 198 | 1.1222 | 0.7556 | | 0.0018 | 34.0 | 204 | 1.1316 | 0.7556 | | 0.0004 | 35.0 | 210 | 1.1630 | 0.7556 | | 0.0004 | 36.0 | 216 | 1.1838 | 0.7333 | | 0.0002 | 37.0 | 222 | 1.1946 | 0.7333 | | 0.0002 | 38.0 | 228 | 1.1949 | 0.7333 | | 0.0004 | 39.0 | 234 | 1.1930 | 0.7333 | | 0.0002 | 40.0 | 240 | 1.1932 | 0.7333 | | 0.0002 | 41.0 | 246 | 1.1940 | 0.7333 | | 0.0002 | 42.0 | 252 | 1.1942 | 0.7333 | | 0.0002 | 43.0 | 258 | 1.1942 | 0.7333 | | 0.0002 | 44.0 | 264 | 1.1942 | 0.7333 | | 0.0002 | 45.0 | 270 | 1.1942 | 0.7333 | | 0.0002 | 46.0 | 276 | 1.1942 | 0.7333 | | 0.0002 | 47.0 | 282 | 1.1942 | 0.7333 | | 0.0002 | 48.0 | 288 | 1.1942 | 0.7333 | | 0.0003 | 49.0 | 294 | 1.1942 | 0.7333 | | 0.0001 | 50.0 | 300 | 1.1942 | 0.7333 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_0001_fold2

# hushem_1x_beit_base_adamax_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0153 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.2331 | 0.5556 | | 1.3348 | 2.0 | 12 | 0.8218 | 0.6889 | | 1.3348 | 3.0 | 18 | 0.6484 | 0.7556 | | 0.3555 | 4.0 | 24 | 0.8513 | 0.7556 | | 0.1239 | 5.0 | 30 | 0.7326 | 0.7333 | | 0.1239 | 6.0 | 36 | 0.6190 | 0.8 | | 0.0625 | 7.0 | 42 | 1.0407 | 0.7333 | | 0.0625 | 8.0 | 48 | 0.7902 | 0.8 | | 0.0045 | 9.0 | 54 | 0.8103 | 0.7778 | | 0.0021 | 10.0 | 60 | 1.0314 | 0.8 | | 0.0021 | 11.0 | 66 | 1.1219 | 0.7556 | | 0.0013 | 12.0 | 72 | 1.0834 | 0.7556 | | 0.0013 | 13.0 | 78 | 1.0270 | 0.7333 | | 0.0006 | 14.0 | 84 | 1.0518 | 0.7556 | | 0.0005 | 15.0 | 90 | 1.0755 | 0.7556 | | 0.0005 | 16.0 | 96 | 1.1073 | 0.7556 | | 0.0005 | 17.0 | 102 | 1.1726 | 0.7556 | | 0.0005 | 18.0 | 108 | 1.2002 | 0.7556 | | 0.0005 | 19.0 | 114 | 1.1838 | 0.7556 | | 0.0007 | 20.0 | 120 | 1.1860 | 0.7556 | | 0.0007 | 21.0 | 126 | 1.2997 | 0.7556 | | 0.0003 | 22.0 | 132 | 1.3311 | 0.7556 | | 0.0003 | 23.0 | 138 | 1.3197 | 0.7556 | | 0.0002 | 24.0 | 144 | 1.2630 | 0.7556 | | 0.0003 | 25.0 | 150 | 1.1925 | 0.7556 | | 0.0003 | 26.0 | 156 | 1.1444 | 0.7778 | | 0.0002 | 27.0 | 162 | 1.1105 | 0.7778 | | 0.0002 | 28.0 | 168 | 1.0790 | 0.7778 | | 0.0002 | 29.0 | 174 | 1.0616 | 0.7778 | | 0.0002 | 30.0 | 180 | 1.0495 | 0.7778 | | 0.0002 | 31.0 | 186 | 1.0431 | 0.7778 | | 0.0002 | 32.0 | 192 | 1.0407 | 0.7778 | | 0.0002 | 33.0 | 198 | 1.0375 | 0.8 | | 0.0107 | 34.0 | 204 | 1.0331 | 0.8 | | 0.0002 | 35.0 | 210 | 1.0311 | 0.8 | | 0.0002 | 36.0 | 216 | 1.0289 | 0.8 | | 0.0002 | 37.0 | 222 | 1.0264 | 0.8 | | 0.0002 | 38.0 | 228 | 1.0203 | 0.8 | | 0.0003 | 39.0 | 234 | 1.0167 | 0.8 | | 0.0002 | 40.0 | 240 | 1.0146 | 0.8 | | 0.0002 | 41.0 | 246 | 1.0152 | 0.8 | | 0.0002 | 42.0 | 252 | 1.0153 | 0.8 | | 0.0002 | 43.0 | 258 | 1.0153 | 0.8 | | 0.0002 | 44.0 | 264 | 1.0153 | 0.8 | | 0.0002 | 45.0 | 270 | 1.0153 | 0.8 | | 0.0002 | 46.0 | 276 | 1.0153 | 0.8 | | 0.002 | 47.0 | 282 | 1.0153 | 0.8 | | 0.002 | 48.0 | 288 | 1.0153 | 0.8 | | 0.0006 | 49.0 | 294 | 1.0153 | 0.8 | | 0.0001 | 50.0 | 300 | 1.0153 | 0.8 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_0001_fold3

# hushem_1x_beit_base_adamax_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5855 - Accuracy: 0.8605 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.0849 | 0.5349 | | 1.2852 | 2.0 | 12 | 0.7460 | 0.7907 | | 1.2852 | 3.0 | 18 | 0.5699 | 0.8140 | | 0.4305 | 4.0 | 24 | 0.3649 | 0.8605 | | 0.1805 | 5.0 | 30 | 0.2406 | 0.9535 | | 0.1805 | 6.0 | 36 | 0.4656 | 0.8837 | | 0.0211 | 7.0 | 42 | 0.4915 | 0.8605 | | 0.0211 | 8.0 | 48 | 0.5042 | 0.8372 | | 0.0066 | 9.0 | 54 | 0.6760 | 0.7907 | | 0.0025 | 10.0 | 60 | 0.6098 | 0.8605 | | 0.0025 | 11.0 | 66 | 0.6353 | 0.9070 | | 0.0011 | 12.0 | 72 | 0.6882 | 0.8837 | | 0.0011 | 13.0 | 78 | 0.6437 | 0.8837 | | 0.0022 | 14.0 | 84 | 0.5430 | 0.8605 | | 0.0007 | 15.0 | 90 | 0.5436 | 0.8605 | | 0.0007 | 16.0 | 96 | 0.5847 | 0.8605 | | 0.0007 | 17.0 | 102 | 0.7054 | 0.8605 | | 0.0007 | 18.0 | 108 | 0.7624 | 0.8372 | | 0.0006 | 19.0 | 114 | 0.6619 | 0.8605 | | 0.0007 | 20.0 | 120 | 0.6238 | 0.8372 | | 0.0007 | 21.0 | 126 | 0.6086 | 0.8372 | | 0.0003 | 22.0 | 132 | 0.6074 | 0.8372 | | 0.0003 | 23.0 | 138 | 0.6228 | 0.8605 | | 0.0003 | 24.0 | 144 | 0.6265 | 0.8605 | | 0.0003 | 25.0 | 150 | 0.6139 | 0.8372 | | 0.0003 | 26.0 | 156 | 0.6063 | 0.8372 | | 0.0002 | 27.0 | 162 | 0.5981 | 0.8372 | | 0.0002 | 28.0 | 168 | 0.5901 | 0.8372 | | 0.0002 | 29.0 | 174 | 0.5785 | 0.8605 | | 0.0001 | 30.0 | 180 | 0.5753 | 0.8605 | | 0.0001 | 31.0 | 186 | 0.5775 | 0.8605 | | 0.0002 | 32.0 | 192 | 0.5781 | 0.8605 | | 0.0002 | 33.0 | 198 | 0.5782 | 0.8605 | | 0.0002 | 34.0 | 204 | 0.5804 | 0.8605 | | 0.0003 | 35.0 | 210 | 0.5817 | 0.8605 | | 0.0003 | 36.0 | 216 | 0.5823 | 0.8605 | | 0.0001 | 37.0 | 222 | 0.5831 | 0.8605 | | 0.0001 | 38.0 | 228 | 0.5855 | 0.8605 | | 0.0002 | 39.0 | 234 | 0.5859 | 0.8605 | | 0.0002 | 40.0 | 240 | 0.5858 | 0.8605 | | 0.0002 | 41.0 | 246 | 0.5855 | 0.8605 | | 0.0002 | 42.0 | 252 | 0.5855 | 0.8605 | | 0.0002 | 43.0 | 258 | 0.5855 | 0.8605 | | 0.0002 | 44.0 | 264 | 0.5855 | 0.8605 | | 0.0001 | 45.0 | 270 | 0.5855 | 0.8605 | | 0.0001 | 46.0 | 276 | 0.5855 | 0.8605 | | 0.0005 | 47.0 | 282 | 0.5855 | 0.8605 | | 0.0005 | 48.0 | 288 | 0.5855 | 0.8605 | | 0.0002 | 49.0 | 294 | 0.5855 | 0.8605 | | 0.0001 | 50.0 | 300 | 0.5855 | 0.8605 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]

hkivancoral/hushem_1x_beit_base_adamax_0001_fold4

# hushem_1x_beit_base_adamax_0001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2926 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.1428 | 0.6905 | | 1.3492 | 2.0 | 12 | 0.5681 | 0.7857 | | 1.3492 | 3.0 | 18 | 0.2529 | 0.9286 | | 0.3166 | 4.0 | 24 | 0.2221 | 0.9524 | | 0.0428 | 5.0 | 30 | 0.2913 | 0.9048 | | 0.0428 | 6.0 | 36 | 0.3814 | 0.8571 | | 0.0093 | 7.0 | 42 | 0.2701 | 0.9524 | | 0.0093 | 8.0 | 48 | 0.2796 | 0.9286 | | 0.0019 | 9.0 | 54 | 0.3043 | 0.9048 | | 0.0029 | 10.0 | 60 | 0.4551 | 0.8810 | | 0.0029 | 11.0 | 66 | 0.3262 | 0.9286 | | 0.001 | 12.0 | 72 | 0.2680 | 0.9524 | | 0.001 | 13.0 | 78 | 0.2601 | 0.9524 | | 0.0006 | 14.0 | 84 | 0.3353 | 0.9048 | | 0.0008 | 15.0 | 90 | 0.3915 | 0.9048 | | 0.0008 | 16.0 | 96 | 0.4398 | 0.8810 | | 0.0004 | 17.0 | 102 | 0.3988 | 0.9048 | | 0.0004 | 18.0 | 108 | 0.3416 | 0.9048 | | 0.0053 | 19.0 | 114 | 0.2975 | 0.9286 | | 0.0004 | 20.0 | 120 | 0.2890 | 0.9286 | | 0.0004 | 21.0 | 126 | 0.2852 | 0.9286 | | 0.0061 | 22.0 | 132 | 0.2652 | 0.9286 | | 0.0061 | 23.0 | 138 | 0.2502 | 0.9286 | | 0.0002 | 24.0 | 144 | 0.2495 | 0.9286 | | 0.0003 | 25.0 | 150 | 0.2641 | 0.9286 | | 0.0003 | 26.0 | 156 | 0.2771 | 0.9286 | | 0.0002 | 27.0 | 162 | 0.2877 | 0.9286 | | 0.0002 | 28.0 | 168 | 0.3003 | 0.9286 | | 0.0002 | 29.0 | 174 | 0.3118 | 0.9286 | | 0.0002 | 30.0 | 180 | 0.3215 | 0.9286 | | 0.0002 | 31.0 | 186 | 0.3282 | 0.9286 | | 0.0003 | 32.0 | 192 | 0.3381 | 0.9286 | | 0.0003 | 33.0 | 198 | 0.3472 | 0.9048 | | 0.0002 | 34.0 | 204 | 0.3491 | 0.9048 | | 0.0049 | 35.0 | 210 | 0.3154 | 0.9048 | | 0.0049 | 36.0 | 216 | 0.2965 | 0.9048 | | 0.0002 | 37.0 | 222 | 0.2887 | 0.9048 | | 0.0002 | 38.0 | 228 | 0.2886 | 0.9048 | | 0.0002 | 39.0 | 234 | 0.2894 | 0.9048 | | 0.0002 | 40.0 | 240 | 0.2903 | 0.9048 | | 0.0002 | 41.0 | 246 | 0.2922 | 0.9048 | | 0.0004 | 42.0 | 252 | 0.2926 | 0.9048 | | 0.0004 | 43.0 | 258 | 0.2926 | 0.9048 | | 0.0002 | 44.0 | 264 | 0.2926 | 0.9048 | | 0.0002 | 45.0 | 270 | 0.2926 | 0.9048 | | 0.0002 | 46.0 | 276 | 0.2926 | 0.9048 | | 0.0009 | 47.0 | 282 | 0.2926 | 0.9048 | | 0.0009 | 48.0 | 288 | 0.2926 | 0.9048 | | 0.0004 | 49.0 | 294 | 0.2926 | 0.9048 | | 0.0001 | 50.0 | 300 | 0.2926 | 0.9048 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "01_normal", "02_tapered", "03_pyriform", "04_amorphous" ]