stevenbucaille/image-classification-models-dataset

model_id stringlengths 7 105	model_card stringlengths 1 130k	model_labels listlengths 2 80k
bdpc/resnet101_rvl-cdip-_rvl_cdip-NK1000__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. --> # resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6065 - Accuracy: 0.7915 - Brier Loss: 0.3054 - Nll: 1.9957 - F1 Micro: 0.7915 - F1 Macro: 0.7910 - Ece: 0.0453 - Aurc: 0.0607 ## 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: 64 - eval_batch_size: 64 - 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 \| 250 \| 4.1565 \| 0.1378 \| 0.9318 \| 7.9039 \| 0.1378 \| 0.1073 \| 0.0673 \| 0.8326 \| \| 4.1485 \| 2.0 \| 500 \| 3.6932 \| 0.3235 \| 0.8832 \| 5.1525 \| 0.3235 \| 0.2725 \| 0.2044 \| 0.5507 \| \| 4.1485 \| 3.0 \| 750 \| 2.3374 \| 0.4725 \| 0.6611 \| 3.3127 \| 0.4725 \| 0.4311 \| 0.0839 \| 0.2921 \| \| 2.392 \| 4.0 \| 1000 \| 1.6516 \| 0.588 \| 0.5470 \| 2.8681 \| 0.588 \| 0.5789 \| 0.0620 \| 0.1929 \| \| 2.392 \| 5.0 \| 1250 \| 1.3260 \| 0.6488 \| 0.4782 \| 2.6378 \| 0.6488 \| 0.6444 \| 0.0486 \| 0.1458 \| \| 1.1422 \| 6.0 \| 1500 \| 1.0390 \| 0.702 \| 0.4156 \| 2.4086 \| 0.702 \| 0.7029 \| 0.0576 \| 0.1097 \| \| 1.1422 \| 7.0 \| 1750 \| 0.8420 \| 0.7288 \| 0.3738 \| 2.2222 \| 0.7288 \| 0.7300 \| 0.0553 \| 0.0888 \| \| 0.708 \| 8.0 \| 2000 \| 0.7753 \| 0.7398 \| 0.3586 \| 2.1518 \| 0.7398 \| 0.7396 \| 0.0587 \| 0.0826 \| \| 0.708 \| 9.0 \| 2250 \| 0.7797 \| 0.7462 \| 0.3580 \| 2.1095 \| 0.7462 \| 0.7457 \| 0.0581 \| 0.0820 \| \| 0.5195 \| 10.0 \| 2500 \| 0.7101 \| 0.7602 \| 0.3404 \| 2.0711 \| 0.7602 \| 0.7612 \| 0.0473 \| 0.0733 \| \| 0.5195 \| 11.0 \| 2750 \| 0.6971 \| 0.7645 \| 0.3338 \| 2.0649 \| 0.7645 \| 0.7653 \| 0.0541 \| 0.0715 \| \| 0.4176 \| 12.0 \| 3000 \| 0.6936 \| 0.7712 \| 0.3302 \| 2.0265 \| 0.7712 \| 0.7708 \| 0.0515 \| 0.0702 \| \| 0.4176 \| 13.0 \| 3250 \| 0.6991 \| 0.7662 \| 0.3346 \| 2.0582 \| 0.7663 \| 0.7657 \| 0.0581 \| 0.0723 \| \| 0.3573 \| 14.0 \| 3500 \| 0.6672 \| 0.7722 \| 0.3246 \| 2.0053 \| 0.7722 \| 0.7723 \| 0.0551 \| 0.0683 \| \| 0.3573 \| 15.0 \| 3750 \| 0.6735 \| 0.777 \| 0.3244 \| 2.0387 \| 0.777 \| 0.7782 \| 0.0488 \| 0.0671 \| \| 0.3193 \| 16.0 \| 4000 \| 0.6567 \| 0.776 \| 0.3216 \| 2.0256 \| 0.776 \| 0.7773 \| 0.0499 \| 0.0678 \| \| 0.3193 \| 17.0 \| 4250 \| 0.6498 \| 0.78 \| 0.3184 \| 1.9865 \| 0.78 \| 0.7802 \| 0.0477 \| 0.0662 \| \| 0.2893 \| 18.0 \| 4500 \| 0.6763 \| 0.7755 \| 0.3264 \| 2.0844 \| 0.7755 \| 0.7755 \| 0.0531 \| 0.0697 \| \| 0.2893 \| 19.0 \| 4750 \| 0.6519 \| 0.7815 \| 0.3183 \| 2.0458 \| 0.7815 \| 0.7817 \| 0.0513 \| 0.0658 \| \| 0.271 \| 20.0 \| 5000 \| 0.6432 \| 0.7823 \| 0.3147 \| 2.0291 \| 0.7823 \| 0.7827 \| 0.0440 \| 0.0645 \| \| 0.271 \| 21.0 \| 5250 \| 0.6456 \| 0.781 \| 0.3156 \| 2.0493 \| 0.7810 \| 0.7813 \| 0.0487 \| 0.0652 \| \| 0.2516 \| 22.0 \| 5500 \| 0.6336 \| 0.7823 \| 0.3144 \| 1.9829 \| 0.7823 \| 0.7822 \| 0.0522 \| 0.0642 \| \| 0.2516 \| 23.0 \| 5750 \| 0.6333 \| 0.7837 \| 0.3128 \| 2.0196 \| 0.7837 \| 0.7836 \| 0.0492 \| 0.0641 \| \| 0.2397 \| 24.0 \| 6000 \| 0.6337 \| 0.7817 \| 0.3147 \| 2.0180 \| 0.7817 \| 0.7815 \| 0.0494 \| 0.0644 \| \| 0.2397 \| 25.0 \| 6250 \| 0.6347 \| 0.7857 \| 0.3145 \| 2.0187 \| 0.7857 \| 0.7856 \| 0.0510 \| 0.0641 \| \| 0.23 \| 26.0 \| 6500 \| 0.6311 \| 0.7815 \| 0.3129 \| 2.0132 \| 0.7815 \| 0.7819 \| 0.0495 \| 0.0637 \| \| 0.23 \| 27.0 \| 6750 \| 0.6329 \| 0.7853 \| 0.3125 \| 2.0708 \| 0.7853 \| 0.7852 \| 0.0502 \| 0.0635 \| \| 0.2191 \| 28.0 \| 7000 \| 0.6222 \| 0.786 \| 0.3109 \| 2.0022 \| 0.786 \| 0.7856 \| 0.0483 \| 0.0638 \| \| 0.2191 \| 29.0 \| 7250 \| 0.6195 \| 0.7863 \| 0.3096 \| 2.0028 \| 0.7863 \| 0.7859 \| 0.0550 \| 0.0620 \| \| 0.2155 \| 30.0 \| 7500 \| 0.6196 \| 0.7883 \| 0.3090 \| 1.9972 \| 0.7883 \| 0.7883 \| 0.0486 \| 0.0624 \| \| 0.2155 \| 31.0 \| 7750 \| 0.6167 \| 0.787 \| 0.3080 \| 2.0173 \| 0.787 \| 0.7871 \| 0.0443 \| 0.0623 \| \| 0.2074 \| 32.0 \| 8000 \| 0.6143 \| 0.7897 \| 0.3073 \| 2.0223 \| 0.7897 \| 0.7893 \| 0.0443 \| 0.0614 \| \| 0.2074 \| 33.0 \| 8250 \| 0.6123 \| 0.787 \| 0.3078 \| 1.9869 \| 0.787 \| 0.7866 \| 0.0458 \| 0.0619 \| \| 0.2028 \| 34.0 \| 8500 \| 0.6137 \| 0.7873 \| 0.3070 \| 1.9883 \| 0.7873 \| 0.7868 \| 0.0457 \| 0.0623 \| \| 0.2028 \| 35.0 \| 8750 \| 0.6152 \| 0.786 \| 0.3085 \| 2.0108 \| 0.786 \| 0.7863 \| 0.0497 \| 0.0626 \| \| 0.1982 \| 36.0 \| 9000 \| 0.6133 \| 0.7863 \| 0.3077 \| 2.0205 \| 0.7863 \| 0.7862 \| 0.0515 \| 0.0615 \| \| 0.1982 \| 37.0 \| 9250 \| 0.6145 \| 0.7877 \| 0.3081 \| 1.9930 \| 0.7877 \| 0.7879 \| 0.0444 \| 0.0621 \| \| 0.1948 \| 38.0 \| 9500 \| 0.6116 \| 0.7857 \| 0.3078 \| 2.0072 \| 0.7857 \| 0.7854 \| 0.0508 \| 0.0619 \| \| 0.1948 \| 39.0 \| 9750 \| 0.6090 \| 0.788 \| 0.3059 \| 1.9954 \| 0.788 \| 0.7882 \| 0.0430 \| 0.0614 \| \| 0.1933 \| 40.0 \| 10000 \| 0.6143 \| 0.7897 \| 0.3072 \| 1.9943 \| 0.7897 \| 0.7899 \| 0.0462 \| 0.0618 \| \| 0.1933 \| 41.0 \| 10250 \| 0.6061 \| 0.7887 \| 0.3041 \| 1.9900 \| 0.7887 \| 0.7889 \| 0.0439 \| 0.0606 \| \| 0.1882 \| 42.0 \| 10500 \| 0.6070 \| 0.7865 \| 0.3058 \| 1.9907 \| 0.7865 \| 0.7868 \| 0.0438 \| 0.0607 \| \| 0.1882 \| 43.0 \| 10750 \| 0.6083 \| 0.788 \| 0.3054 \| 2.0095 \| 0.788 \| 0.7877 \| 0.0489 \| 0.0608 \| \| 0.1871 \| 44.0 \| 11000 \| 0.6083 \| 0.787 \| 0.3054 \| 1.9828 \| 0.787 \| 0.7872 \| 0.0469 \| 0.0607 \| \| 0.1871 \| 45.0 \| 11250 \| 0.6092 \| 0.7893 \| 0.3057 \| 2.0140 \| 0.7893 \| 0.7891 \| 0.0483 \| 0.0608 \| \| 0.1862 \| 46.0 \| 11500 \| 0.6057 \| 0.7893 \| 0.3053 \| 2.0064 \| 0.7893 \| 0.7890 \| 0.0450 \| 0.0609 \| \| 0.1862 \| 47.0 \| 11750 \| 0.6042 \| 0.79 \| 0.3044 \| 1.9691 \| 0.79 \| 0.7899 \| 0.0435 \| 0.0607 \| \| 0.1845 \| 48.0 \| 12000 \| 0.6068 \| 0.79 \| 0.3053 \| 2.0052 \| 0.79 \| 0.7899 \| 0.0438 \| 0.0608 \| \| 0.1845 \| 49.0 \| 12250 \| 0.6081 \| 0.7893 \| 0.3062 \| 2.0117 \| 0.7893 \| 0.7890 \| 0.0485 \| 0.0612 \| \| 0.1836 \| 50.0 \| 12500 \| 0.6065 \| 0.7915 \| 0.3054 \| 1.9957 \| 0.7915 \| 0.7910 \| 0.0453 \| 0.0607 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
hansin91/activity_classification	<!-- 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. --> # activity_classification 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: - Loss: 0.7631 - Accuracy: 0.7710 ## 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: 3e-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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1235 \| 1.0 \| 315 \| 1.3182 \| 0.7099 \| \| 1.0404 \| 2.0 \| 630 \| 0.9831 \| 0.7278 \| \| 0.7899 \| 3.0 \| 945 \| 0.9509 \| 0.7175 \| \| 0.6961 \| 4.0 \| 1260 \| 0.8258 \| 0.7460 \| \| 0.615 \| 5.0 \| 1575 \| 0.7890 \| 0.7667 \| \| 0.5534 \| 6.0 \| 1890 \| 0.7876 \| 0.7591 \| \| 0.524 \| 7.0 \| 2205 \| 0.7627 \| 0.7663 \| \| 0.4588 \| 8.0 \| 2520 \| 0.8256 \| 0.7468 \| \| 0.4407 \| 9.0 \| 2835 \| 0.8041 \| 0.7615 \| \| 0.4039 \| 10.0 \| 3150 \| 0.8367 \| 0.7540 \| \| 0.3966 \| 11.0 \| 3465 \| 0.8708 \| 0.7492 \| \| 0.366 \| 12.0 \| 3780 \| 0.8410 \| 0.7544 \| \| 0.3522 \| 13.0 \| 4095 \| 0.9019 \| 0.7365 \| \| 0.3495 \| 14.0 \| 4410 \| 0.8240 \| 0.7567 \| \| 0.3206 \| 15.0 \| 4725 \| 0.8428 \| 0.7607 \| \| 0.3172 \| 16.0 \| 5040 \| 0.8626 \| 0.7607 \| \| 0.2931 \| 17.0 \| 5355 \| 1.0311 \| 0.7306 \| \| 0.2943 \| 18.0 \| 5670 \| 0.9393 \| 0.7544 \| \| 0.2886 \| 19.0 \| 5985 \| 0.9379 \| 0.7472 \| \| 0.2785 \| 20.0 \| 6300 \| 0.8911 \| 0.7552 \| \| 0.274 \| 21.0 \| 6615 \| 0.9730 \| 0.7484 \| \| 0.2716 \| 22.0 \| 6930 \| 0.9546 \| 0.7504 \| \| 0.2686 \| 23.0 \| 7245 \| 0.8939 \| 0.7651 \| \| 0.2489 \| 24.0 \| 7560 \| 0.9397 \| 0.7480 \| \| 0.257 \| 25.0 \| 7875 \| 0.9298 \| 0.7552 \| \| 0.244 \| 26.0 \| 8190 \| 0.9977 \| 0.7437 \| \| 0.2333 \| 27.0 \| 8505 \| 0.9967 \| 0.75 \| \| 0.2376 \| 28.0 \| 8820 \| 1.0012 \| 0.7508 \| \| 0.2428 \| 29.0 \| 9135 \| 0.9674 \| 0.7421 \| \| 0.224 \| 30.0 \| 9450 \| 1.0239 \| 0.7361 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]
anggtpd/snacks_classifier	# snacks_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 the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0090 - Accuracy: 0.8908	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
purabp1249/swin-tiny-patch4-window7-224-finetuned-herbify_plants	<!-- 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-herbify_plants 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: 0.0703 - Accuracy: 0.9868 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7849 \| 0.99 \| 21 \| 0.4281 \| 0.8553 \| \| 0.2498 \| 1.98 \| 42 \| 0.1295 \| 0.9737 \| \| 0.1387 \| 2.96 \| 63 \| 0.0703 \| 0.9868 \| \| 0.1039 \| 3.95 \| 84 \| 0.0741 \| 0.9737 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cpu - Datasets 2.14.5 - Tokenizers 0.13.3	[ "aloevera", "amruta_balli", "avacado", "bamboo", "betel" ]
grahmatagung/flower_classifier	<!-- 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. --> # flower_classifier This model is a fine-tuned version of [jonathanfernandes/vit-base-patch16-224-finetuned-flower](https://huggingface.co/jonathanfernandes/vit-base-patch16-224-finetuned-flower) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2238 - Accuracy: 0.9878 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0568 \| 1.0 \| 102 \| 0.9734 \| 0.9322 \| \| 0.3193 \| 2.0 \| 205 \| 0.3169 \| 0.9805 \| \| 0.1862 \| 2.99 \| 306 \| 0.2250 \| 0.9853 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "1", "10", "16", "98", "99", "17", "18", "19", "2", "20", "21", "22", "23", "24", "100", "25", "26", "27", "28", "29", "3", "30", "31", "32", "33", "101", "34", "35", "36", "37", "38", "39", "4", "40", "41", "42", "102", "43", "44", "45", "46", "47", "48", "49", "5", "50", "51", "11", "52", "53", "54", "55", "56", "57", "58", "59", "6", "60", "12", "61", "62", "63", "64", "65", "66", "67", "68", "69", "7", "13", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "14", "8", "80", "81", "82", "83", "84", "85", "86", "87", "88", "15", "89", "9", "90", "91", "92", "93", "94", "95", "96", "97" ]
LucyintheSky/pose-estimation-front-side-back	# Pose Estimation: front,side,back ## Model description This model predicts the person's body position relative to the camera: front, side, back. It was trained on [Lucy in the Sky](https://www.lucyinthesky.com/shop) images. This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). ## Training and evaluation data It achieves the following results on the evaluation set: - Loss: 0.2524 - Accuracy: 0.9355 ### 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: 20 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "back", "front", "side" ]
stevanojs/pokemon_classification	<!-- 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. --> # pokemon_classification 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 None dataset. It achieves the following results on the evaluation set: - Loss: 1.0586 - Accuracy: 0.9071 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.3925 \| 1.0 \| 350 \| 4.0653 \| 0.6705 \| \| 3.2005 \| 2.0 \| 700 \| 3.1602 \| 0.8227 \| \| 2.3615 \| 3.0 \| 1050 \| 2.4281 \| 0.8656 \| \| 1.5369 \| 4.0 \| 1400 \| 1.8786 \| 0.8821 \| \| 1.0741 \| 5.0 \| 1750 \| 1.4818 \| 0.9014 \| \| 0.7094 \| 6.0 \| 2100 \| 1.2335 \| 0.9014 \| \| 0.544 \| 7.0 \| 2450 \| 1.0976 \| 0.9042 \| \| 0.4622 \| 8.0 \| 2800 \| 1.0586 \| 0.9071 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "golbat", "machoke", "raichu", "dragonite", "fearow", "slowpoke", "weezing", "beedrill", "weedle", "cloyster", "vaporeon", "gyarados", "golduck", "zapdos", "machamp", "hitmonlee", "primeape", "cubone", "sandslash", "scyther", "haunter", "metapod", "tentacruel", "aerodactyl", "raticate", "kabutops", "ninetales", "zubat", "rhydon", "mew", "pinsir", "ditto", "victreebel", "omanyte", "horsea", "magnemite", "pikachu", "blastoise", "venomoth", "charizard", "seadra", "muk", "spearow", "bulbasaur", "bellsprout", "electrode", "ivysaur", "gloom", "poliwhirl", "flareon", "seaking", "hypno", "wartortle", "mankey", "tentacool", "exeggcute", "meowth", "growlithe", "tangela", "drowzee", "rapidash", "venonat", "omastar", "pidgeot", "nidorino", "porygon", "lickitung", "rattata", "machop", "charmeleon", "slowbro", "parasect", "eevee", "diglett", "starmie", "staryu", "psyduck", "dragonair", "magikarp", "vileplume", "marowak", "pidgeotto", "shellder", "mewtwo", "lapras", "farfetchd", "kingler", "seel", "kakuna", "doduo", "electabuzz", "charmander", "rhyhorn", "tauros", "dugtrio", "kabuto", "poliwrath", "gengar", "exeggutor", "dewgong", "jigglypuff", "geodude", "kadabra", "nidorina", "sandshrew", "grimer", "persian", "mrmime", "pidgey", "koffing", "ekans", "alolan sandslash", "venusaur", "snorlax", "paras", "jynx", "chansey", "weepinbell", "hitmonchan", "gastly", "kangaskhan", "oddish", "wigglytuff", "graveler", "arcanine", "clefairy", "articuno", "poliwag", "golem", "abra", "squirtle", "voltorb", "ponyta", "moltres", "nidoqueen", "magmar", "onix", "vulpix", "butterfree", "dodrio", "krabby", "arbok", "clefable", "goldeen", "magneton", "dratini", "caterpie", "jolteon", "nidoking", "alakazam" ]
bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_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. --> # resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5837 - Accuracy: 0.7867 - Brier Loss: 0.3013 - Nll: 1.9882 - F1 Micro: 0.7868 - F1 Macro: 0.7860 - Ece: 0.0529 - Aurc: 0.0581 ## 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: 64 - eval_batch_size: 64 - 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 \| 250 \| 4.1958 \| 0.1035 \| 0.9350 \| 9.1004 \| 0.1035 \| 0.0792 \| 0.0472 \| 0.9013 \| \| 4.2322 \| 2.0 \| 500 \| 4.0778 \| 0.173 \| 0.9251 \| 6.5742 \| 0.173 \| 0.1393 \| 0.0993 \| 0.7501 \| \| 4.2322 \| 3.0 \| 750 \| 3.6484 \| 0.339 \| 0.8778 \| 4.9108 \| 0.339 \| 0.2957 \| 0.2172 \| 0.5305 \| \| 3.5256 \| 4.0 \| 1000 \| 2.5967 \| 0.4592 \| 0.6991 \| 3.3640 \| 0.4592 \| 0.4220 \| 0.1274 \| 0.3285 \| \| 3.5256 \| 5.0 \| 1250 \| 2.0345 \| 0.5417 \| 0.6078 \| 3.0118 \| 0.5417 \| 0.5180 \| 0.0976 \| 0.2447 \| \| 1.9172 \| 6.0 \| 1500 \| 1.4417 \| 0.625 \| 0.5029 \| 2.7890 \| 0.625 \| 0.6123 \| 0.0549 \| 0.1623 \| \| 1.9172 \| 7.0 \| 1750 \| 1.3298 \| 0.639 \| 0.4852 \| 2.6110 \| 0.639 \| 0.6320 \| 0.0558 \| 0.1501 \| \| 1.1801 \| 8.0 \| 2000 \| 1.1697 \| 0.674 \| 0.4473 \| 2.4787 \| 0.674 \| 0.6712 \| 0.0466 \| 0.1283 \| \| 1.1801 \| 9.0 \| 2250 \| 0.9625 \| 0.7093 \| 0.4020 \| 2.3242 \| 0.7093 \| 0.7085 \| 0.0526 \| 0.1017 \| \| 0.8029 \| 10.0 \| 2500 \| 0.9477 \| 0.7215 \| 0.3893 \| 2.3193 \| 0.7215 \| 0.7228 \| 0.0515 \| 0.0971 \| \| 0.8029 \| 11.0 \| 2750 \| 0.8527 \| 0.7375 \| 0.3692 \| 2.2785 \| 0.7375 \| 0.7377 \| 0.0490 \| 0.0870 \| \| 0.5717 \| 12.0 \| 3000 \| 0.7377 \| 0.7515 \| 0.3470 \| 2.1475 \| 0.7515 \| 0.7529 \| 0.0552 \| 0.0757 \| \| 0.5717 \| 13.0 \| 3250 \| 0.7309 \| 0.7498 \| 0.3469 \| 2.1250 \| 0.7498 \| 0.7494 \| 0.0589 \| 0.0758 \| \| 0.4414 \| 14.0 \| 3500 \| 0.7165 \| 0.7558 \| 0.3427 \| 2.1045 \| 0.7558 \| 0.7576 \| 0.0582 \| 0.0721 \| \| 0.4414 \| 15.0 \| 3750 \| 0.6865 \| 0.7678 \| 0.3319 \| 2.0457 \| 0.7678 \| 0.7688 \| 0.0551 \| 0.0697 \| \| 0.3691 \| 16.0 \| 4000 \| 0.7002 \| 0.7662 \| 0.3348 \| 2.1280 \| 0.7663 \| 0.7664 \| 0.0567 \| 0.0698 \| \| 0.3691 \| 17.0 \| 4250 \| 0.6896 \| 0.7628 \| 0.3326 \| 2.0750 \| 0.7628 \| 0.7631 \| 0.0608 \| 0.0691 \| \| 0.3214 \| 18.0 \| 4500 \| 0.6666 \| 0.7715 \| 0.3258 \| 2.0468 \| 0.7715 \| 0.7707 \| 0.0544 \| 0.0680 \| \| 0.3214 \| 19.0 \| 4750 \| 0.6735 \| 0.7702 \| 0.3277 \| 2.0544 \| 0.7702 \| 0.7700 \| 0.0571 \| 0.0681 \| \| 0.2914 \| 20.0 \| 5000 \| 0.6607 \| 0.772 \| 0.3241 \| 2.0364 \| 0.772 \| 0.7729 \| 0.0525 \| 0.0659 \| \| 0.2914 \| 21.0 \| 5250 \| 0.6625 \| 0.7688 \| 0.3217 \| 2.0387 \| 0.7688 \| 0.7703 \| 0.0455 \| 0.0664 \| \| 0.2653 \| 22.0 \| 5500 \| 0.6543 \| 0.775 \| 0.3200 \| 2.0560 \| 0.775 \| 0.7752 \| 0.0507 \| 0.0647 \| \| 0.2653 \| 23.0 \| 5750 \| 0.6409 \| 0.7725 \| 0.3188 \| 2.0091 \| 0.7725 \| 0.7733 \| 0.0554 \| 0.0647 \| \| 0.2482 \| 24.0 \| 6000 \| 0.6452 \| 0.7758 \| 0.3191 \| 2.0256 \| 0.7758 \| 0.7756 \| 0.0502 \| 0.0655 \| \| 0.2482 \| 25.0 \| 6250 \| 0.6401 \| 0.7742 \| 0.3196 \| 2.0668 \| 0.7742 \| 0.7745 \| 0.0528 \| 0.0648 \| \| 0.2354 \| 26.0 \| 6500 \| 0.6316 \| 0.775 \| 0.3171 \| 2.0150 \| 0.775 \| 0.7755 \| 0.0555 \| 0.0634 \| \| 0.2354 \| 27.0 \| 6750 \| 0.6257 \| 0.7808 \| 0.3147 \| 2.0129 \| 0.7808 \| 0.7808 \| 0.0503 \| 0.0624 \| \| 0.2229 \| 28.0 \| 7000 \| 0.6343 \| 0.7778 \| 0.3144 \| 2.0910 \| 0.7778 \| 0.7776 \| 0.0510 \| 0.0624 \| \| 0.2229 \| 29.0 \| 7250 \| 0.6206 \| 0.781 \| 0.3115 \| 2.0399 \| 0.7810 \| 0.7798 \| 0.0555 \| 0.0606 \| \| 0.2147 \| 30.0 \| 7500 \| 0.6262 \| 0.777 \| 0.3124 \| 2.0603 \| 0.777 \| 0.7772 \| 0.0539 \| 0.0616 \| \| 0.2147 \| 31.0 \| 7750 \| 0.6265 \| 0.7788 \| 0.3137 \| 2.0833 \| 0.7788 \| 0.7777 \| 0.0532 \| 0.0614 \| \| 0.2058 \| 32.0 \| 8000 \| 0.6134 \| 0.7815 \| 0.3119 \| 2.0369 \| 0.7815 \| 0.7815 \| 0.0514 \| 0.0615 \| \| 0.2058 \| 33.0 \| 8250 \| 0.6153 \| 0.7772 \| 0.3133 \| 2.0513 \| 0.7773 \| 0.7772 \| 0.0534 \| 0.0623 \| \| 0.1994 \| 34.0 \| 8500 \| 0.6143 \| 0.7853 \| 0.3098 \| 2.0188 \| 0.7853 \| 0.7857 \| 0.0508 \| 0.0611 \| \| 0.1994 \| 35.0 \| 8750 \| 0.6096 \| 0.7827 \| 0.3086 \| 2.0134 \| 0.7828 \| 0.7828 \| 0.0512 \| 0.0606 \| \| 0.1932 \| 36.0 \| 9000 \| 0.6094 \| 0.784 \| 0.3067 \| 2.0151 \| 0.7840 \| 0.7847 \| 0.0471 \| 0.0602 \| \| 0.1932 \| 37.0 \| 9250 \| 0.6142 \| 0.7833 \| 0.3111 \| 2.0213 \| 0.7833 \| 0.7829 \| 0.0542 \| 0.0608 \| \| 0.1895 \| 38.0 \| 9500 \| 0.6103 \| 0.7812 \| 0.3094 \| 2.0594 \| 0.7812 \| 0.7799 \| 0.0529 \| 0.0603 \| \| 0.1895 \| 39.0 \| 9750 \| 0.6059 \| 0.781 \| 0.3078 \| 2.0386 \| 0.7810 \| 0.7806 \| 0.0545 \| 0.0607 \| \| 0.1848 \| 40.0 \| 10000 \| 0.6042 \| 0.782 \| 0.3072 \| 2.0133 \| 0.782 \| 0.7824 \| 0.0527 \| 0.0603 \| \| 0.1848 \| 41.0 \| 10250 \| 0.5991 \| 0.785 \| 0.3043 \| 2.0124 \| 0.785 \| 0.7853 \| 0.0496 \| 0.0594 \| \| 0.1793 \| 42.0 \| 10500 \| 0.6034 \| 0.784 \| 0.3058 \| 2.0607 \| 0.7840 \| 0.7838 \| 0.0490 \| 0.0599 \| \| 0.1793 \| 43.0 \| 10750 \| 0.6047 \| 0.7827 \| 0.3068 \| 2.0139 \| 0.7828 \| 0.7819 \| 0.0492 \| 0.0595 \| \| 0.1768 \| 44.0 \| 11000 \| 0.5982 \| 0.785 \| 0.3057 \| 2.0303 \| 0.785 \| 0.7843 \| 0.0473 \| 0.0596 \| \| 0.1768 \| 45.0 \| 11250 \| 0.6036 \| 0.7795 \| 0.3087 \| 2.0173 \| 0.7795 \| 0.7788 \| 0.0549 \| 0.0607 \| \| 0.1743 \| 46.0 \| 11500 \| 0.5974 \| 0.785 \| 0.3060 \| 2.0026 \| 0.785 \| 0.7839 \| 0.0478 \| 0.0596 \| \| 0.1743 \| 47.0 \| 11750 \| 0.5996 \| 0.782 \| 0.3068 \| 2.0144 \| 0.782 \| 0.7825 \| 0.0480 \| 0.0598 \| \| 0.1707 \| 48.0 \| 12000 \| 0.5958 \| 0.7833 \| 0.3079 \| 2.0344 \| 0.7833 \| 0.7827 \| 0.0500 \| 0.0598 \| \| 0.1707 \| 49.0 \| 12250 \| 0.5969 \| 0.782 \| 0.3060 \| 2.0162 \| 0.782 \| 0.7820 \| 0.0482 \| 0.0597 \| \| 0.1683 \| 50.0 \| 12500 \| 0.5933 \| 0.784 \| 0.3043 \| 1.9897 \| 0.7840 \| 0.7836 \| 0.0496 \| 0.0589 \| \| 0.1683 \| 51.0 \| 12750 \| 0.5935 \| 0.7833 \| 0.3042 \| 2.0142 \| 0.7833 \| 0.7829 \| 0.0501 \| 0.0586 \| \| 0.1649 \| 52.0 \| 13000 \| 0.5950 \| 0.7847 \| 0.3050 \| 2.0125 \| 0.7847 \| 0.7851 \| 0.0475 \| 0.0591 \| \| 0.1649 \| 53.0 \| 13250 \| 0.5904 \| 0.7837 \| 0.3020 \| 1.9830 \| 0.7837 \| 0.7837 \| 0.0504 \| 0.0584 \| \| 0.1636 \| 54.0 \| 13500 \| 0.5926 \| 0.785 \| 0.3042 \| 2.0006 \| 0.785 \| 0.7845 \| 0.0493 \| 0.0588 \| \| 0.1636 \| 55.0 \| 13750 \| 0.5885 \| 0.7847 \| 0.3029 \| 2.0025 \| 0.7847 \| 0.7843 \| 0.0505 \| 0.0585 \| \| 0.1616 \| 56.0 \| 14000 \| 0.5920 \| 0.788 \| 0.3041 \| 2.0174 \| 0.788 \| 0.7878 \| 0.0520 \| 0.0591 \| \| 0.1616 \| 57.0 \| 14250 \| 0.5927 \| 0.7863 \| 0.3033 \| 2.0321 \| 0.7863 \| 0.7858 \| 0.0521 \| 0.0588 \| \| 0.1592 \| 58.0 \| 14500 \| 0.5878 \| 0.787 \| 0.3017 \| 1.9751 \| 0.787 \| 0.7874 \| 0.0461 \| 0.0584 \| \| 0.1592 \| 59.0 \| 14750 \| 0.5888 \| 0.7867 \| 0.3030 \| 1.9996 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0582 \| \| 0.1585 \| 60.0 \| 15000 \| 0.5929 \| 0.786 \| 0.3052 \| 2.0237 \| 0.786 \| 0.7857 \| 0.0512 \| 0.0584 \| \| 0.1585 \| 61.0 \| 15250 \| 0.5894 \| 0.7865 \| 0.3026 \| 1.9895 \| 0.7865 \| 0.7864 \| 0.0548 \| 0.0585 \| \| 0.1562 \| 62.0 \| 15500 \| 0.5903 \| 0.7873 \| 0.3033 \| 1.9670 \| 0.7873 \| 0.7870 \| 0.0481 \| 0.0584 \| \| 0.1562 \| 63.0 \| 15750 \| 0.5896 \| 0.7853 \| 0.3023 \| 1.9681 \| 0.7853 \| 0.7850 \| 0.0520 \| 0.0587 \| \| 0.1548 \| 64.0 \| 16000 \| 0.5903 \| 0.7847 \| 0.3027 \| 1.9865 \| 0.7847 \| 0.7846 \| 0.0506 \| 0.0587 \| \| 0.1548 \| 65.0 \| 16250 \| 0.5910 \| 0.7853 \| 0.3039 \| 2.0009 \| 0.7853 \| 0.7849 \| 0.0515 \| 0.0593 \| \| 0.1537 \| 66.0 \| 16500 \| 0.5866 \| 0.7883 \| 0.3012 \| 1.9561 \| 0.7883 \| 0.7881 \| 0.0447 \| 0.0581 \| \| 0.1537 \| 67.0 \| 16750 \| 0.5858 \| 0.7867 \| 0.3009 \| 1.9868 \| 0.7868 \| 0.7861 \| 0.0486 \| 0.0577 \| \| 0.1526 \| 68.0 \| 17000 \| 0.5886 \| 0.7867 \| 0.3024 \| 2.0009 \| 0.7868 \| 0.7862 \| 0.0530 \| 0.0587 \| \| 0.1526 \| 69.0 \| 17250 \| 0.5850 \| 0.7863 \| 0.3010 \| 2.0095 \| 0.7863 \| 0.7860 \| 0.0510 \| 0.0581 \| \| 0.1508 \| 70.0 \| 17500 \| 0.5867 \| 0.7865 \| 0.3019 \| 2.0304 \| 0.7865 \| 0.7861 \| 0.0525 \| 0.0583 \| \| 0.1508 \| 71.0 \| 17750 \| 0.5895 \| 0.7857 \| 0.3038 \| 2.0013 \| 0.7857 \| 0.7853 \| 0.0478 \| 0.0586 \| \| 0.15 \| 72.0 \| 18000 \| 0.5894 \| 0.7847 \| 0.3025 \| 2.0051 \| 0.7847 \| 0.7845 \| 0.0500 \| 0.0586 \| \| 0.15 \| 73.0 \| 18250 \| 0.5867 \| 0.7865 \| 0.3022 \| 1.9634 \| 0.7865 \| 0.7860 \| 0.0489 \| 0.0582 \| \| 0.149 \| 74.0 \| 18500 \| 0.5888 \| 0.7857 \| 0.3026 \| 1.9817 \| 0.7857 \| 0.7851 \| 0.0497 \| 0.0584 \| \| 0.149 \| 75.0 \| 18750 \| 0.5823 \| 0.7885 \| 0.2994 \| 1.9873 \| 0.7885 \| 0.7880 \| 0.0476 \| 0.0577 \| \| 0.1483 \| 76.0 \| 19000 \| 0.5866 \| 0.7853 \| 0.3025 \| 1.9870 \| 0.7853 \| 0.7849 \| 0.0531 \| 0.0583 \| \| 0.1483 \| 77.0 \| 19250 \| 0.5866 \| 0.7867 \| 0.3013 \| 1.9933 \| 0.7868 \| 0.7862 \| 0.0498 \| 0.0577 \| \| 0.1478 \| 78.0 \| 19500 \| 0.5844 \| 0.787 \| 0.3010 \| 1.9793 \| 0.787 \| 0.7868 \| 0.0465 \| 0.0579 \| \| 0.1478 \| 79.0 \| 19750 \| 0.5850 \| 0.7857 \| 0.3005 \| 1.9856 \| 0.7857 \| 0.7855 \| 0.0489 \| 0.0580 \| \| 0.1463 \| 80.0 \| 20000 \| 0.5829 \| 0.7893 \| 0.2999 \| 2.0003 \| 0.7893 \| 0.7890 \| 0.0543 \| 0.0578 \| \| 0.1463 \| 81.0 \| 20250 \| 0.5845 \| 0.7867 \| 0.3011 \| 2.0178 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0580 \| \| 0.1457 \| 82.0 \| 20500 \| 0.5878 \| 0.7865 \| 0.3022 \| 2.0108 \| 0.7865 \| 0.7861 \| 0.0507 \| 0.0583 \| \| 0.1457 \| 83.0 \| 20750 \| 0.5862 \| 0.7865 \| 0.3016 \| 1.9996 \| 0.7865 \| 0.7865 \| 0.0505 \| 0.0585 \| \| 0.1452 \| 84.0 \| 21000 \| 0.5851 \| 0.7863 \| 0.3011 \| 2.0002 \| 0.7863 \| 0.7859 \| 0.0481 \| 0.0582 \| \| 0.1452 \| 85.0 \| 21250 \| 0.5850 \| 0.787 \| 0.3013 \| 1.9659 \| 0.787 \| 0.7867 \| 0.0524 \| 0.0582 \| \| 0.1449 \| 86.0 \| 21500 \| 0.5878 \| 0.7867 \| 0.3023 \| 1.9837 \| 0.7868 \| 0.7866 \| 0.0526 \| 0.0581 \| \| 0.1449 \| 87.0 \| 21750 \| 0.5844 \| 0.7873 \| 0.3010 \| 1.9807 \| 0.7873 \| 0.7865 \| 0.0522 \| 0.0577 \| \| 0.1437 \| 88.0 \| 22000 \| 0.5846 \| 0.7877 \| 0.3012 \| 1.9947 \| 0.7877 \| 0.7869 \| 0.0464 \| 0.0580 \| \| 0.1437 \| 89.0 \| 22250 \| 0.5859 \| 0.787 \| 0.3016 \| 2.0002 \| 0.787 \| 0.7867 \| 0.0503 \| 0.0581 \| \| 0.143 \| 90.0 \| 22500 \| 0.5838 \| 0.7865 \| 0.3010 \| 1.9996 \| 0.7865 \| 0.7859 \| 0.0496 \| 0.0576 \| \| 0.143 \| 91.0 \| 22750 \| 0.5843 \| 0.7837 \| 0.3011 \| 1.9683 \| 0.7837 \| 0.7834 \| 0.0501 \| 0.0583 \| \| 0.1426 \| 92.0 \| 23000 \| 0.5843 \| 0.7873 \| 0.3010 \| 1.9960 \| 0.7873 \| 0.7870 \| 0.0524 \| 0.0578 \| \| 0.1426 \| 93.0 \| 23250 \| 0.5827 \| 0.7847 \| 0.3005 \| 1.9719 \| 0.7847 \| 0.7844 \| 0.0506 \| 0.0579 \| \| 0.1428 \| 94.0 \| 23500 \| 0.5831 \| 0.7865 \| 0.3009 \| 1.9781 \| 0.7865 \| 0.7862 \| 0.0517 \| 0.0579 \| \| 0.1428 \| 95.0 \| 23750 \| 0.5821 \| 0.784 \| 0.3001 \| 1.9641 \| 0.7840 \| 0.7838 \| 0.0505 \| 0.0579 \| \| 0.1424 \| 96.0 \| 24000 \| 0.5850 \| 0.7845 \| 0.3020 \| 1.9667 \| 0.7845 \| 0.7842 \| 0.0526 \| 0.0584 \| \| 0.1424 \| 97.0 \| 24250 \| 0.5850 \| 0.7847 \| 0.3012 \| 1.9776 \| 0.7847 \| 0.7844 \| 0.0508 \| 0.0579 \| \| 0.142 \| 98.0 \| 24500 \| 0.5845 \| 0.7877 \| 0.3011 \| 1.9745 \| 0.7877 \| 0.7870 \| 0.0491 \| 0.0579 \| \| 0.142 \| 99.0 \| 24750 \| 0.5834 \| 0.7853 \| 0.3010 \| 1.9679 \| 0.7853 \| 0.7852 \| 0.0506 \| 0.0581 \| \| 0.1416 \| 100.0 \| 25000 \| 0.5837 \| 0.7867 \| 0.3013 \| 1.9882 \| 0.7868 \| 0.7860 \| 0.0529 \| 0.0581 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_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_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7429 - Accuracy: 0.7853 - Brier Loss: 0.3044 - Nll: 2.0364 - F1 Micro: 0.7853 - F1 Macro: 0.7862 - Ece: 0.0430 - Aurc: 0.0599 ## 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: 64 - eval_batch_size: 64 - 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 \| 250 \| 9.5443 \| 0.0765 \| 0.9365 \| 3.7373 \| 0.0765 \| 0.0522 \| 0.0360 \| 0.9336 \| \| 9.4095 \| 2.0 \| 500 \| 7.4542 \| 0.0757 \| 0.9312 \| 2.8468 \| 0.0757 \| 0.0316 \| 0.0425 \| 0.8840 \| \| 9.4095 \| 3.0 \| 750 \| 5.8933 \| 0.0975 \| 0.9356 \| 3.2058 \| 0.0975 \| 0.0408 \| 0.0798 \| 0.8593 \| \| 5.9994 \| 4.0 \| 1000 \| 4.3665 \| 0.2125 \| 0.8700 \| 5.3759 \| 0.2125 \| 0.1290 \| 0.0743 \| 0.7029 \| \| 5.9994 \| 5.0 \| 1250 \| 3.0367 \| 0.4415 \| 0.6924 \| 4.9073 \| 0.4415 \| 0.4283 \| 0.0806 \| 0.3570 \| \| 3.2184 \| 6.0 \| 1500 \| 2.1589 \| 0.579 \| 0.5587 \| 3.7412 \| 0.579 \| 0.5771 \| 0.0572 \| 0.2172 \| \| 3.2184 \| 7.0 \| 1750 \| 1.5582 \| 0.652 \| 0.4673 \| 3.0701 \| 0.652 \| 0.6456 \| 0.0517 \| 0.1478 \| \| 1.6737 \| 8.0 \| 2000 \| 1.3502 \| 0.6893 \| 0.4266 \| 2.8575 \| 0.6893 \| 0.6860 \| 0.0544 \| 0.1175 \| \| 1.6737 \| 9.0 \| 2250 \| 1.1389 \| 0.7188 \| 0.3914 \| 2.5937 \| 0.7188 \| 0.7195 \| 0.0544 \| 0.1006 \| \| 1.0789 \| 10.0 \| 2500 \| 1.0563 \| 0.7302 \| 0.3742 \| 2.5043 \| 0.7302 \| 0.7305 \| 0.0618 \| 0.0912 \| \| 1.0789 \| 11.0 \| 2750 \| 1.0035 \| 0.7428 \| 0.3604 \| 2.4375 \| 0.7428 \| 0.7441 \| 0.0587 \| 0.0823 \| \| 0.7934 \| 12.0 \| 3000 \| 0.9169 \| 0.7548 \| 0.3472 \| 2.2921 \| 0.7548 \| 0.7555 \| 0.0547 \| 0.0762 \| \| 0.7934 \| 13.0 \| 3250 \| 0.8628 \| 0.7598 \| 0.3386 \| 2.2849 \| 0.7598 \| 0.7600 \| 0.0550 \| 0.0739 \| \| 0.6268 \| 14.0 \| 3500 \| 0.8773 \| 0.7675 \| 0.3362 \| 2.2170 \| 0.7675 \| 0.7692 \| 0.0490 \| 0.0718 \| \| 0.6268 \| 15.0 \| 3750 \| 0.8263 \| 0.7682 \| 0.3306 \| 2.1617 \| 0.7682 \| 0.7702 \| 0.0534 \| 0.0704 \| \| 0.5269 \| 16.0 \| 4000 \| 0.8422 \| 0.7708 \| 0.3289 \| 2.1907 \| 0.7707 \| 0.7717 \| 0.0524 \| 0.0687 \| \| 0.5269 \| 17.0 \| 4250 \| 0.8100 \| 0.7745 \| 0.3241 \| 2.1664 \| 0.7745 \| 0.7761 \| 0.0509 \| 0.0667 \| \| 0.4516 \| 18.0 \| 4500 \| 0.8013 \| 0.7778 \| 0.3215 \| 2.1216 \| 0.7778 \| 0.7790 \| 0.0473 \| 0.0669 \| \| 0.4516 \| 19.0 \| 4750 \| 0.7911 \| 0.7802 \| 0.3183 \| 2.1224 \| 0.7802 \| 0.7812 \| 0.0476 \| 0.0648 \| \| 0.4039 \| 20.0 \| 5000 \| 0.7900 \| 0.7775 \| 0.3197 \| 2.0969 \| 0.7775 \| 0.7797 \| 0.0473 \| 0.0647 \| \| 0.4039 \| 21.0 \| 5250 \| 0.7919 \| 0.7792 \| 0.3191 \| 2.1445 \| 0.7792 \| 0.7810 \| 0.0531 \| 0.0652 \| \| 0.3563 \| 22.0 \| 5500 \| 0.7960 \| 0.7802 \| 0.3166 \| 2.0849 \| 0.7802 \| 0.7818 \| 0.0478 \| 0.0649 \| \| 0.3563 \| 23.0 \| 5750 \| 0.7615 \| 0.7825 \| 0.3128 \| 2.0834 \| 0.7825 \| 0.7833 \| 0.0478 \| 0.0638 \| \| 0.3251 \| 24.0 \| 6000 \| 0.7840 \| 0.7792 \| 0.3151 \| 2.0841 \| 0.7792 \| 0.7800 \| 0.0513 \| 0.0648 \| \| 0.3251 \| 25.0 \| 6250 \| 0.7837 \| 0.7792 \| 0.3159 \| 2.0889 \| 0.7792 \| 0.7808 \| 0.0485 \| 0.0643 \| \| 0.2949 \| 26.0 \| 6500 \| 0.7827 \| 0.7802 \| 0.3158 \| 2.0416 \| 0.7802 \| 0.7819 \| 0.0548 \| 0.0648 \| \| 0.2949 \| 27.0 \| 6750 \| 0.7650 \| 0.78 \| 0.3130 \| 2.0411 \| 0.78 \| 0.7807 \| 0.0506 \| 0.0629 \| \| 0.2669 \| 28.0 \| 7000 \| 0.7787 \| 0.7802 \| 0.3133 \| 2.0843 \| 0.7802 \| 0.7810 \| 0.0454 \| 0.0627 \| \| 0.2669 \| 29.0 \| 7250 \| 0.7892 \| 0.782 \| 0.3163 \| 2.0953 \| 0.782 \| 0.7826 \| 0.0508 \| 0.0635 \| \| 0.2512 \| 30.0 \| 7500 \| 0.7775 \| 0.7825 \| 0.3126 \| 2.0904 \| 0.7825 \| 0.7837 \| 0.0451 \| 0.0633 \| \| 0.2512 \| 31.0 \| 7750 \| 0.7601 \| 0.7817 \| 0.3124 \| 2.0251 \| 0.7817 \| 0.7827 \| 0.0485 \| 0.0627 \| \| 0.231 \| 32.0 \| 8000 \| 0.7669 \| 0.7833 \| 0.3120 \| 2.0685 \| 0.7833 \| 0.7842 \| 0.0472 \| 0.0629 \| \| 0.231 \| 33.0 \| 8250 \| 0.7652 \| 0.7847 \| 0.3116 \| 2.0661 \| 0.7847 \| 0.7858 \| 0.0519 \| 0.0625 \| \| 0.2172 \| 34.0 \| 8500 \| 0.7637 \| 0.7837 \| 0.3107 \| 2.0264 \| 0.7837 \| 0.7852 \| 0.0487 \| 0.0628 \| \| 0.2172 \| 35.0 \| 8750 \| 0.7691 \| 0.783 \| 0.3120 \| 2.0535 \| 0.7830 \| 0.7844 \| 0.0438 \| 0.0634 \| \| 0.2032 \| 36.0 \| 9000 \| 0.7647 \| 0.7845 \| 0.3093 \| 2.0480 \| 0.7845 \| 0.7852 \| 0.0471 \| 0.0620 \| \| 0.2032 \| 37.0 \| 9250 \| 0.7727 \| 0.782 \| 0.3122 \| 2.0610 \| 0.782 \| 0.7830 \| 0.0493 \| 0.0628 \| \| 0.1925 \| 38.0 \| 9500 \| 0.7563 \| 0.7843 \| 0.3085 \| 2.0267 \| 0.7843 \| 0.7849 \| 0.0459 \| 0.0608 \| \| 0.1925 \| 39.0 \| 9750 \| 0.7597 \| 0.7835 \| 0.3087 \| 2.0062 \| 0.7835 \| 0.7845 \| 0.0485 \| 0.0614 \| \| 0.1823 \| 40.0 \| 10000 \| 0.7611 \| 0.7833 \| 0.3107 \| 2.0007 \| 0.7833 \| 0.7853 \| 0.0479 \| 0.0625 \| \| 0.1823 \| 41.0 \| 10250 \| 0.7608 \| 0.7843 \| 0.3076 \| 2.0335 \| 0.7843 \| 0.7854 \| 0.0486 \| 0.0602 \| \| 0.17 \| 42.0 \| 10500 \| 0.7535 \| 0.7833 \| 0.3096 \| 2.0121 \| 0.7833 \| 0.7844 \| 0.0505 \| 0.0613 \| \| 0.17 \| 43.0 \| 10750 \| 0.7524 \| 0.7845 \| 0.3066 \| 2.0425 \| 0.7845 \| 0.7856 \| 0.0476 \| 0.0605 \| \| 0.1639 \| 44.0 \| 11000 \| 0.7608 \| 0.7808 \| 0.3108 \| 2.0739 \| 0.7808 \| 0.7816 \| 0.0503 \| 0.0618 \| \| 0.1639 \| 45.0 \| 11250 \| 0.7560 \| 0.786 \| 0.3063 \| 1.9876 \| 0.786 \| 0.7868 \| 0.0496 \| 0.0607 \| \| 0.1575 \| 46.0 \| 11500 \| 0.7494 \| 0.784 \| 0.3063 \| 2.0311 \| 0.7840 \| 0.7846 \| 0.0416 \| 0.0601 \| \| 0.1575 \| 47.0 \| 11750 \| 0.7515 \| 0.7857 \| 0.3069 \| 2.0539 \| 0.7857 \| 0.7866 \| 0.0456 \| 0.0609 \| \| 0.1493 \| 48.0 \| 12000 \| 0.7511 \| 0.7843 \| 0.3086 \| 2.0325 \| 0.7843 \| 0.7852 \| 0.0552 \| 0.0612 \| \| 0.1493 \| 49.0 \| 12250 \| 0.7495 \| 0.787 \| 0.3067 \| 2.0231 \| 0.787 \| 0.7880 \| 0.0475 \| 0.0605 \| \| 0.1425 \| 50.0 \| 12500 \| 0.7538 \| 0.7867 \| 0.3052 \| 2.0267 \| 0.7868 \| 0.7870 \| 0.0507 \| 0.0603 \| \| 0.1425 \| 51.0 \| 12750 \| 0.7529 \| 0.7847 \| 0.3081 \| 2.0592 \| 0.7847 \| 0.7859 \| 0.0467 \| 0.0604 \| \| 0.1356 \| 52.0 \| 13000 \| 0.7527 \| 0.7808 \| 0.3071 \| 2.0349 \| 0.7808 \| 0.7818 \| 0.0473 \| 0.0607 \| \| 0.1356 \| 53.0 \| 13250 \| 0.7451 \| 0.7865 \| 0.3049 \| 2.0368 \| 0.7865 \| 0.7879 \| 0.0484 \| 0.0595 \| \| 0.1325 \| 54.0 \| 13500 \| 0.7481 \| 0.7857 \| 0.3056 \| 2.0223 \| 0.7857 \| 0.7869 \| 0.0468 \| 0.0603 \| \| 0.1325 \| 55.0 \| 13750 \| 0.7470 \| 0.7835 \| 0.3057 \| 2.0306 \| 0.7835 \| 0.7844 \| 0.0492 \| 0.0601 \| \| 0.1264 \| 56.0 \| 14000 \| 0.7471 \| 0.7873 \| 0.3053 \| 2.0336 \| 0.7873 \| 0.7880 \| 0.0519 \| 0.0601 \| \| 0.1264 \| 57.0 \| 14250 \| 0.7429 \| 0.7895 \| 0.3032 \| 2.0149 \| 0.7895 \| 0.7903 \| 0.0468 \| 0.0595 \| \| 0.1208 \| 58.0 \| 14500 \| 0.7399 \| 0.7885 \| 0.3035 \| 2.0147 \| 0.7885 \| 0.7895 \| 0.0433 \| 0.0596 \| \| 0.1208 \| 59.0 \| 14750 \| 0.7518 \| 0.786 \| 0.3076 \| 2.0481 \| 0.786 \| 0.7873 \| 0.0403 \| 0.0607 \| \| 0.119 \| 60.0 \| 15000 \| 0.7483 \| 0.7903 \| 0.3058 \| 2.0138 \| 0.7903 \| 0.7914 \| 0.0471 \| 0.0601 \| \| 0.119 \| 61.0 \| 15250 \| 0.7463 \| 0.7845 \| 0.3043 \| 2.0617 \| 0.7845 \| 0.7855 \| 0.0458 \| 0.0599 \| \| 0.1128 \| 62.0 \| 15500 \| 0.7478 \| 0.7875 \| 0.3056 \| 2.0187 \| 0.7875 \| 0.7888 \| 0.0452 \| 0.0604 \| \| 0.1128 \| 63.0 \| 15750 \| 0.7510 \| 0.784 \| 0.3061 \| 2.0204 \| 0.7840 \| 0.7850 \| 0.0495 \| 0.0605 \| \| 0.1109 \| 64.0 \| 16000 \| 0.7424 \| 0.786 \| 0.3053 \| 2.0167 \| 0.786 \| 0.7871 \| 0.0449 \| 0.0603 \| \| 0.1109 \| 65.0 \| 16250 \| 0.7473 \| 0.7885 \| 0.3054 \| 2.0200 \| 0.7885 \| 0.7893 \| 0.0471 \| 0.0600 \| \| 0.1078 \| 66.0 \| 16500 \| 0.7467 \| 0.7873 \| 0.3054 \| 2.0224 \| 0.7873 \| 0.7883 \| 0.0482 \| 0.0599 \| \| 0.1078 \| 67.0 \| 16750 \| 0.7445 \| 0.7893 \| 0.3039 \| 2.0082 \| 0.7893 \| 0.7895 \| 0.0456 \| 0.0593 \| \| 0.1051 \| 68.0 \| 17000 \| 0.7490 \| 0.7873 \| 0.3063 \| 2.0152 \| 0.7873 \| 0.7883 \| 0.0505 \| 0.0602 \| \| 0.1051 \| 69.0 \| 17250 \| 0.7490 \| 0.785 \| 0.3061 \| 2.0103 \| 0.785 \| 0.7861 \| 0.0465 \| 0.0602 \| \| 0.1009 \| 70.0 \| 17500 \| 0.7445 \| 0.7875 \| 0.3049 \| 2.0308 \| 0.7875 \| 0.7884 \| 0.0483 \| 0.0598 \| \| 0.1009 \| 71.0 \| 17750 \| 0.7490 \| 0.7863 \| 0.3068 \| 2.0260 \| 0.7863 \| 0.7875 \| 0.0495 \| 0.0604 \| \| 0.0984 \| 72.0 \| 18000 \| 0.7465 \| 0.7893 \| 0.3059 \| 2.0161 \| 0.7893 \| 0.7906 \| 0.0427 \| 0.0601 \| \| 0.0984 \| 73.0 \| 18250 \| 0.7451 \| 0.7873 \| 0.3058 \| 2.0204 \| 0.7873 \| 0.7882 \| 0.0511 \| 0.0605 \| \| 0.0966 \| 74.0 \| 18500 \| 0.7445 \| 0.7875 \| 0.3042 \| 2.0227 \| 0.7875 \| 0.7886 \| 0.0495 \| 0.0599 \| \| 0.0966 \| 75.0 \| 18750 \| 0.7443 \| 0.7863 \| 0.3040 \| 2.0138 \| 0.7863 \| 0.7872 \| 0.0442 \| 0.0598 \| \| 0.0947 \| 76.0 \| 19000 \| 0.7448 \| 0.7865 \| 0.3054 \| 2.0234 \| 0.7865 \| 0.7873 \| 0.0457 \| 0.0598 \| \| 0.0947 \| 77.0 \| 19250 \| 0.7448 \| 0.7865 \| 0.3041 \| 2.0110 \| 0.7865 \| 0.7875 \| 0.0508 \| 0.0596 \| \| 0.0931 \| 78.0 \| 19500 \| 0.7460 \| 0.7883 \| 0.3040 \| 2.0125 \| 0.7883 \| 0.7895 \| 0.0467 \| 0.0595 \| \| 0.0931 \| 79.0 \| 19750 \| 0.7456 \| 0.7883 \| 0.3038 \| 2.0302 \| 0.7883 \| 0.7894 \| 0.0455 \| 0.0596 \| \| 0.0899 \| 80.0 \| 20000 \| 0.7469 \| 0.788 \| 0.3040 \| 2.0188 \| 0.788 \| 0.7892 \| 0.0487 \| 0.0597 \| \| 0.0899 \| 81.0 \| 20250 \| 0.7421 \| 0.788 \| 0.3041 \| 2.0359 \| 0.788 \| 0.7888 \| 0.0427 \| 0.0595 \| \| 0.0882 \| 82.0 \| 20500 \| 0.7444 \| 0.7865 \| 0.3051 \| 2.0219 \| 0.7865 \| 0.7875 \| 0.0479 \| 0.0600 \| \| 0.0882 \| 83.0 \| 20750 \| 0.7439 \| 0.788 \| 0.3039 \| 2.0197 \| 0.788 \| 0.7894 \| 0.0439 \| 0.0597 \| \| 0.0871 \| 84.0 \| 21000 \| 0.7421 \| 0.7865 \| 0.3040 \| 1.9910 \| 0.7865 \| 0.7876 \| 0.0445 \| 0.0598 \| \| 0.0871 \| 85.0 \| 21250 \| 0.7429 \| 0.7887 \| 0.3043 \| 2.0253 \| 0.7887 \| 0.7898 \| 0.0426 \| 0.0597 \| \| 0.0869 \| 86.0 \| 21500 \| 0.7442 \| 0.7873 \| 0.3041 \| 2.0156 \| 0.7873 \| 0.7885 \| 0.0488 \| 0.0596 \| \| 0.0869 \| 87.0 \| 21750 \| 0.7439 \| 0.7857 \| 0.3051 \| 2.0099 \| 0.7857 \| 0.7867 \| 0.0465 \| 0.0599 \| \| 0.084 \| 88.0 \| 22000 \| 0.7434 \| 0.786 \| 0.3040 \| 1.9926 \| 0.786 \| 0.7869 \| 0.0469 \| 0.0598 \| \| 0.084 \| 89.0 \| 22250 \| 0.7431 \| 0.7873 \| 0.3048 \| 2.0028 \| 0.7873 \| 0.7880 \| 0.0442 \| 0.0599 \| \| 0.0821 \| 90.0 \| 22500 \| 0.7447 \| 0.7867 \| 0.3040 \| 2.0349 \| 0.7868 \| 0.7876 \| 0.0477 \| 0.0596 \| \| 0.0821 \| 91.0 \| 22750 \| 0.7443 \| 0.7877 \| 0.3051 \| 2.0356 \| 0.7877 \| 0.7887 \| 0.0486 \| 0.0601 \| \| 0.0813 \| 92.0 \| 23000 \| 0.7500 \| 0.7873 \| 0.3053 \| 2.0465 \| 0.7873 \| 0.7880 \| 0.0484 \| 0.0601 \| \| 0.0813 \| 93.0 \| 23250 \| 0.7449 \| 0.788 \| 0.3037 \| 1.9966 \| 0.788 \| 0.7890 \| 0.0441 \| 0.0594 \| \| 0.0811 \| 94.0 \| 23500 \| 0.7466 \| 0.7897 \| 0.3048 \| 2.0297 \| 0.7897 \| 0.7907 \| 0.0429 \| 0.0600 \| \| 0.0811 \| 95.0 \| 23750 \| 0.7482 \| 0.7875 \| 0.3058 \| 2.0319 \| 0.7875 \| 0.7885 \| 0.0464 \| 0.0601 \| \| 0.0808 \| 96.0 \| 24000 \| 0.7473 \| 0.7863 \| 0.3055 \| 2.0219 \| 0.7863 \| 0.7874 \| 0.0477 \| 0.0603 \| \| 0.0808 \| 97.0 \| 24250 \| 0.7451 \| 0.7855 \| 0.3044 \| 2.0356 \| 0.7855 \| 0.7865 \| 0.0481 \| 0.0594 \| \| 0.08 \| 98.0 \| 24500 \| 0.7442 \| 0.7857 \| 0.3042 \| 2.0213 \| 0.7857 \| 0.7868 \| 0.0475 \| 0.0595 \| \| 0.08 \| 99.0 \| 24750 \| 0.7462 \| 0.7863 \| 0.3053 \| 2.0354 \| 0.7863 \| 0.7874 \| 0.0425 \| 0.0599 \| \| 0.079 \| 100.0 \| 25000 \| 0.7429 \| 0.7853 \| 0.3044 \| 2.0364 \| 0.7853 \| 0.7862 \| 0.0430 \| 0.0599 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_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_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9013 - Accuracy: 0.7933 - Brier Loss: 0.3080 - Nll: 1.8102 - F1 Micro: 0.7932 - F1 Macro: 0.7937 - Ece: 0.0719 - Aurc: 0.0635 ## 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: 64 - eval_batch_size: 64 - 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 \| 250 \| 6.0054 \| 0.098 \| 0.9327 \| 9.3196 \| 0.0980 \| 0.0481 \| 0.0462 \| 0.8670 \| \| 6.0141 \| 2.0 \| 500 \| 5.4713 \| 0.2195 \| 0.8933 \| 5.2235 \| 0.2195 \| 0.1452 \| 0.1046 \| 0.7129 \| \| 6.0141 \| 3.0 \| 750 \| 4.4006 \| 0.4535 \| 0.7034 \| 3.0178 \| 0.4535 \| 0.4351 \| 0.1373 \| 0.3334 \| \| 4.5079 \| 4.0 \| 1000 \| 3.8431 \| 0.59 \| 0.5686 \| 2.5843 \| 0.59 \| 0.5822 \| 0.1309 \| 0.2072 \| \| 4.5079 \| 5.0 \| 1250 \| 3.5315 \| 0.6552 \| 0.4864 \| 2.4330 \| 0.6552 \| 0.6537 \| 0.1048 \| 0.1504 \| \| 3.5028 \| 6.0 \| 1500 \| 3.2850 \| 0.707 \| 0.4163 \| 2.2375 \| 0.707 \| 0.7082 \| 0.0790 \| 0.1111 \| \| 3.5028 \| 7.0 \| 1750 \| 3.0974 \| 0.7312 \| 0.3721 \| 2.0933 \| 0.7312 \| 0.7328 \| 0.0452 \| 0.0899 \| \| 3.0599 \| 8.0 \| 2000 \| 3.0385 \| 0.7455 \| 0.3561 \| 2.0148 \| 0.7455 \| 0.7456 \| 0.0432 \| 0.0838 \| \| 3.0599 \| 9.0 \| 2250 \| 2.9978 \| 0.7565 \| 0.3432 \| 1.9780 \| 0.7565 \| 0.7572 \| 0.0437 \| 0.0777 \| \| 2.8562 \| 10.0 \| 2500 \| 2.9853 \| 0.7622 \| 0.3397 \| 1.9176 \| 0.7622 \| 0.7619 \| 0.0495 \| 0.0751 \| \| 2.8562 \| 11.0 \| 2750 \| 2.9803 \| 0.7615 \| 0.3385 \| 1.9327 \| 0.7615 \| 0.7627 \| 0.0547 \| 0.0760 \| \| 2.7414 \| 12.0 \| 3000 \| 2.9711 \| 0.7658 \| 0.3322 \| 1.9439 \| 0.7658 \| 0.7661 \| 0.0495 \| 0.0740 \| \| 2.7414 \| 13.0 \| 3250 \| 2.9618 \| 0.771 \| 0.3276 \| 1.8599 \| 0.771 \| 0.7718 \| 0.0548 \| 0.0704 \| \| 2.6658 \| 14.0 \| 3500 \| 2.9534 \| 0.7762 \| 0.3252 \| 1.8935 \| 0.7762 \| 0.7770 \| 0.0581 \| 0.0699 \| \| 2.6658 \| 15.0 \| 3750 \| 2.9568 \| 0.776 \| 0.3248 \| 1.8836 \| 0.776 \| 0.7776 \| 0.0588 \| 0.0699 \| \| 2.6197 \| 16.0 \| 4000 \| 2.9196 \| 0.7812 \| 0.3169 \| 1.8338 \| 0.7812 \| 0.7814 \| 0.0601 \| 0.0655 \| \| 2.6197 \| 17.0 \| 4250 \| 2.9267 \| 0.7785 \| 0.3202 \| 1.8430 \| 0.7785 \| 0.7783 \| 0.0647 \| 0.0677 \| \| 2.5794 \| 18.0 \| 4500 \| 2.9189 \| 0.779 \| 0.3155 \| 1.8279 \| 0.779 \| 0.7794 \| 0.0631 \| 0.0661 \| \| 2.5794 \| 19.0 \| 4750 \| 2.9324 \| 0.7823 \| 0.3177 \| 1.8508 \| 0.7823 \| 0.7823 \| 0.0665 \| 0.0669 \| \| 2.5553 \| 20.0 \| 5000 \| 2.9192 \| 0.7837 \| 0.3146 \| 1.8312 \| 0.7837 \| 0.7840 \| 0.0641 \| 0.0654 \| \| 2.5553 \| 21.0 \| 5250 \| 2.9160 \| 0.7817 \| 0.3140 \| 1.8366 \| 0.7817 \| 0.7828 \| 0.0682 \| 0.0658 \| \| 2.53 \| 22.0 \| 5500 \| 2.9172 \| 0.7837 \| 0.3139 \| 1.8138 \| 0.7837 \| 0.7842 \| 0.0602 \| 0.0652 \| \| 2.53 \| 23.0 \| 5750 \| 2.9132 \| 0.7875 \| 0.3134 \| 1.8254 \| 0.7875 \| 0.7877 \| 0.0656 \| 0.0646 \| \| 2.5127 \| 24.0 \| 6000 \| 2.9108 \| 0.7875 \| 0.3132 \| 1.8367 \| 0.7875 \| 0.7869 \| 0.0669 \| 0.0652 \| \| 2.5127 \| 25.0 \| 6250 \| 2.9272 \| 0.7837 \| 0.3139 \| 1.8551 \| 0.7837 \| 0.7843 \| 0.0632 \| 0.0653 \| \| 2.4979 \| 26.0 \| 6500 \| 2.9157 \| 0.7867 \| 0.3128 \| 1.8101 \| 0.7868 \| 0.7876 \| 0.0655 \| 0.0647 \| \| 2.4979 \| 27.0 \| 6750 \| 2.9031 \| 0.785 \| 0.3112 \| 1.8089 \| 0.785 \| 0.7856 \| 0.0688 \| 0.0639 \| \| 2.4814 \| 28.0 \| 7000 \| 2.9094 \| 0.7875 \| 0.3110 \| 1.8594 \| 0.7875 \| 0.7880 \| 0.0677 \| 0.0646 \| \| 2.4814 \| 29.0 \| 7250 \| 2.9110 \| 0.7885 \| 0.3116 \| 1.8150 \| 0.7885 \| 0.7891 \| 0.0696 \| 0.0639 \| \| 2.4741 \| 30.0 \| 7500 \| 2.9039 \| 0.7877 \| 0.3091 \| 1.8471 \| 0.7877 \| 0.7887 \| 0.0694 \| 0.0632 \| \| 2.4741 \| 31.0 \| 7750 \| 2.9029 \| 0.7907 \| 0.3087 \| 1.7604 \| 0.7907 \| 0.7917 \| 0.0691 \| 0.0633 \| \| 2.4626 \| 32.0 \| 8000 \| 2.8983 \| 0.7877 \| 0.3094 \| 1.8191 \| 0.7877 \| 0.7884 \| 0.0677 \| 0.0625 \| \| 2.4626 \| 33.0 \| 8250 \| 2.9024 \| 0.7897 \| 0.3088 \| 1.8025 \| 0.7897 \| 0.7905 \| 0.0720 \| 0.0635 \| \| 2.4558 \| 34.0 \| 8500 \| 2.9055 \| 0.792 \| 0.3070 \| 1.7869 \| 0.792 \| 0.7920 \| 0.0667 \| 0.0628 \| \| 2.4558 \| 35.0 \| 8750 \| 2.9055 \| 0.788 \| 0.3104 \| 1.8349 \| 0.788 \| 0.7883 \| 0.0733 \| 0.0645 \| \| 2.4481 \| 36.0 \| 9000 \| 2.9061 \| 0.7887 \| 0.3078 \| 1.7840 \| 0.7887 \| 0.7898 \| 0.0676 \| 0.0642 \| \| 2.4481 \| 37.0 \| 9250 \| 2.9086 \| 0.7917 \| 0.3102 \| 1.7942 \| 0.7917 \| 0.7923 \| 0.0716 \| 0.0644 \| \| 2.4422 \| 38.0 \| 9500 \| 2.9067 \| 0.7897 \| 0.3084 \| 1.7915 \| 0.7897 \| 0.7900 \| 0.0704 \| 0.0637 \| \| 2.4422 \| 39.0 \| 9750 \| 2.9080 \| 0.7927 \| 0.3092 \| 1.7951 \| 0.7927 \| 0.7930 \| 0.0709 \| 0.0631 \| \| 2.4386 \| 40.0 \| 10000 \| 2.9064 \| 0.7943 \| 0.3084 \| 1.8079 \| 0.7943 \| 0.7949 \| 0.0734 \| 0.0635 \| \| 2.4386 \| 41.0 \| 10250 \| 2.8990 \| 0.792 \| 0.3056 \| 1.7918 \| 0.792 \| 0.7924 \| 0.0699 \| 0.0623 \| \| 2.4312 \| 42.0 \| 10500 \| 2.9057 \| 0.7893 \| 0.3090 \| 1.7892 \| 0.7893 \| 0.7901 \| 0.0735 \| 0.0641 \| \| 2.4312 \| 43.0 \| 10750 \| 2.8998 \| 0.7923 \| 0.3079 \| 1.7909 \| 0.7923 \| 0.7932 \| 0.0707 \| 0.0630 \| \| 2.4294 \| 44.0 \| 11000 \| 2.9108 \| 0.7905 \| 0.3090 \| 1.8220 \| 0.7905 \| 0.7916 \| 0.0773 \| 0.0636 \| \| 2.4294 \| 45.0 \| 11250 \| 2.9030 \| 0.7927 \| 0.3086 \| 1.8126 \| 0.7927 \| 0.7932 \| 0.0710 \| 0.0631 \| \| 2.4282 \| 46.0 \| 11500 \| 2.9033 \| 0.7915 \| 0.3077 \| 1.8234 \| 0.7915 \| 0.7920 \| 0.0712 \| 0.0631 \| \| 2.4282 \| 47.0 \| 11750 \| 2.8975 \| 0.7957 \| 0.3063 \| 1.8070 \| 0.7957 \| 0.7968 \| 0.0702 \| 0.0630 \| \| 2.4246 \| 48.0 \| 12000 \| 2.9049 \| 0.7935 \| 0.3085 \| 1.8090 \| 0.7935 \| 0.7944 \| 0.0722 \| 0.0635 \| \| 2.4246 \| 49.0 \| 12250 \| 2.9020 \| 0.792 \| 0.3075 \| 1.8233 \| 0.792 \| 0.7927 \| 0.0700 \| 0.0638 \| \| 2.4227 \| 50.0 \| 12500 \| 2.9013 \| 0.7933 \| 0.3080 \| 1.8102 \| 0.7932 \| 0.7937 \| 0.0719 \| 0.0635 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
bdpc/resnet101-base_tobacco-cnn_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. --> # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 - Accuracy: 0.69 - Brier Loss: 0.4919 - Nll: 2.4969 - F1 Micro: 0.69 - F1 Macro: 0.6317 - Ece: 0.3029 - Aurc: 0.1260 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.4796 \| 0.165 \| 0.8965 \| 8.4885 \| 0.165 \| 0.1123 \| 0.2151 \| 0.8341 \| \| No log \| 2.0 \| 26 \| 1.4679 \| 0.165 \| 0.8954 \| 8.3391 \| 0.165 \| 0.1066 \| 0.2136 \| 0.8332 \| \| No log \| 3.0 \| 39 \| 1.4170 \| 0.21 \| 0.8858 \| 6.1941 \| 0.2100 \| 0.0969 \| 0.2433 \| 0.7991 \| \| No log \| 4.0 \| 52 \| 1.3472 \| 0.21 \| 0.8711 \| 6.0602 \| 0.2100 \| 0.0728 \| 0.2320 \| 0.7271 \| \| No log \| 5.0 \| 65 \| 1.2776 \| 0.19 \| 0.8572 \| 6.1293 \| 0.19 \| 0.0537 \| 0.2422 \| 0.7473 \| \| No log \| 6.0 \| 78 \| 1.1840 \| 0.245 \| 0.8353 \| 6.2405 \| 0.245 \| 0.1060 \| 0.2810 \| 0.6690 \| \| No log \| 7.0 \| 91 \| 1.0740 \| 0.365 \| 0.7936 \| 6.3617 \| 0.3650 \| 0.1739 \| 0.3136 \| 0.3646 \| \| No log \| 8.0 \| 104 \| 1.1102 \| 0.345 \| 0.8081 \| 5.8896 \| 0.345 \| 0.1812 \| 0.3046 \| 0.4292 \| \| No log \| 9.0 \| 117 \| 1.0735 \| 0.34 \| 0.7963 \| 5.9970 \| 0.34 \| 0.1842 \| 0.3028 \| 0.4286 \| \| No log \| 10.0 \| 130 \| 1.1145 \| 0.265 \| 0.8110 \| 5.9054 \| 0.265 \| 0.1300 \| 0.2511 \| 0.6350 \| \| No log \| 11.0 \| 143 \| 0.9981 \| 0.325 \| 0.7659 \| 5.3834 \| 0.325 \| 0.1655 \| 0.2790 \| 0.4860 \| \| No log \| 12.0 \| 156 \| 1.0500 \| 0.285 \| 0.7898 \| 4.9696 \| 0.285 \| 0.1594 \| 0.2604 \| 0.6636 \| \| No log \| 13.0 \| 169 \| 0.8764 \| 0.445 \| 0.6976 \| 4.6456 \| 0.445 \| 0.2647 \| 0.2779 \| 0.3020 \| \| No log \| 14.0 \| 182 \| 0.9147 \| 0.48 \| 0.7108 \| 4.4793 \| 0.48 \| 0.2942 \| 0.3262 \| 0.2862 \| \| No log \| 15.0 \| 195 \| 0.9776 \| 0.38 \| 0.7434 \| 4.4065 \| 0.38 \| 0.2269 \| 0.2938 \| 0.5297 \| \| No log \| 16.0 \| 208 \| 0.8066 \| 0.47 \| 0.6494 \| 3.9671 \| 0.47 \| 0.2966 \| 0.2791 \| 0.2907 \| \| No log \| 17.0 \| 221 \| 0.7766 \| 0.535 \| 0.6305 \| 3.5250 \| 0.535 \| 0.3866 \| 0.3003 \| 0.2424 \| \| No log \| 18.0 \| 234 \| 0.8186 \| 0.535 \| 0.6458 \| 3.3670 \| 0.535 \| 0.3792 \| 0.3005 \| 0.2311 \| \| No log \| 19.0 \| 247 \| 0.8156 \| 0.52 \| 0.6430 \| 3.1633 \| 0.52 \| 0.3675 \| 0.3072 \| 0.2667 \| \| No log \| 20.0 \| 260 \| 0.8386 \| 0.55 \| 0.6462 \| 3.2549 \| 0.55 \| 0.4251 \| 0.3103 \| 0.2703 \| \| No log \| 21.0 \| 273 \| 0.7996 \| 0.515 \| 0.6342 \| 3.1396 \| 0.515 \| 0.3969 \| 0.3177 \| 0.2867 \| \| No log \| 22.0 \| 286 \| 0.8605 \| 0.6 \| 0.6472 \| 3.2563 \| 0.6 \| 0.4717 \| 0.3810 \| 0.2113 \| \| No log \| 23.0 \| 299 \| 0.7138 \| 0.595 \| 0.5713 \| 3.1171 \| 0.595 \| 0.4657 \| 0.2773 \| 0.2034 \| \| No log \| 24.0 \| 312 \| 0.7212 \| 0.665 \| 0.5740 \| 2.9688 \| 0.665 \| 0.5474 \| 0.3366 \| 0.1754 \| \| No log \| 25.0 \| 325 \| 0.7463 \| 0.63 \| 0.5843 \| 2.8998 \| 0.63 \| 0.5502 \| 0.3432 \| 0.2072 \| \| No log \| 26.0 \| 338 \| 0.7231 \| 0.67 \| 0.5626 \| 3.1334 \| 0.67 \| 0.5564 \| 0.3160 \| 0.1521 \| \| No log \| 27.0 \| 351 \| 0.6913 \| 0.68 \| 0.5427 \| 2.8906 \| 0.68 \| 0.5702 \| 0.3354 \| 0.1406 \| \| No log \| 28.0 \| 364 \| 0.6825 \| 0.66 \| 0.5342 \| 2.8619 \| 0.66 \| 0.5615 \| 0.2902 \| 0.1625 \| \| No log \| 29.0 \| 377 \| 0.7015 \| 0.665 \| 0.5549 \| 2.7315 \| 0.665 \| 0.5741 \| 0.3305 \| 0.1769 \| \| No log \| 30.0 \| 390 \| 0.6939 \| 0.67 \| 0.5406 \| 2.7114 \| 0.67 \| 0.5720 \| 0.3353 \| 0.1420 \| \| No log \| 31.0 \| 403 \| 0.6836 \| 0.69 \| 0.5265 \| 2.7567 \| 0.69 \| 0.5982 \| 0.3216 \| 0.1455 \| \| No log \| 32.0 \| 416 \| 0.6728 \| 0.69 \| 0.5211 \| 2.6858 \| 0.69 \| 0.6056 \| 0.3124 \| 0.1453 \| \| No log \| 33.0 \| 429 \| 0.6926 \| 0.675 \| 0.5403 \| 2.5815 \| 0.675 \| 0.6095 \| 0.3258 \| 0.1683 \| \| No log \| 34.0 \| 442 \| 0.6673 \| 0.66 \| 0.5090 \| 2.5591 \| 0.66 \| 0.5722 \| 0.2950 \| 0.1385 \| \| No log \| 35.0 \| 455 \| 0.6811 \| 0.675 \| 0.5207 \| 2.5813 \| 0.675 \| 0.5841 \| 0.3324 \| 0.1273 \| \| No log \| 36.0 \| 468 \| 0.6648 \| 0.69 \| 0.5119 \| 2.5745 \| 0.69 \| 0.6225 \| 0.3433 \| 0.1320 \| \| No log \| 37.0 \| 481 \| 0.6623 \| 0.67 \| 0.5092 \| 2.6134 \| 0.67 \| 0.6129 \| 0.3204 \| 0.1471 \| \| No log \| 38.0 \| 494 \| 0.6635 \| 0.69 \| 0.5088 \| 2.3862 \| 0.69 \| 0.6192 \| 0.3201 \| 0.1311 \| \| 0.7628 \| 39.0 \| 507 \| 0.6554 \| 0.685 \| 0.5008 \| 2.5849 \| 0.685 \| 0.6210 \| 0.3179 \| 0.1377 \| \| 0.7628 \| 40.0 \| 520 \| 0.6567 \| 0.685 \| 0.5022 \| 2.6498 \| 0.685 \| 0.6310 \| 0.3127 \| 0.1414 \| \| 0.7628 \| 41.0 \| 533 \| 0.6558 \| 0.695 \| 0.4996 \| 2.5917 \| 0.695 \| 0.6347 \| 0.3115 \| 0.1321 \| \| 0.7628 \| 42.0 \| 546 \| 0.6578 \| 0.695 \| 0.5021 \| 2.4864 \| 0.695 \| 0.6259 \| 0.3098 \| 0.1306 \| \| 0.7628 \| 43.0 \| 559 \| 0.6544 \| 0.685 \| 0.4969 \| 2.5757 \| 0.685 \| 0.6175 \| 0.2955 \| 0.1342 \| \| 0.7628 \| 44.0 \| 572 \| 0.6507 \| 0.685 \| 0.4944 \| 2.5057 \| 0.685 \| 0.6257 \| 0.3144 \| 0.1304 \| \| 0.7628 \| 45.0 \| 585 \| 0.6501 \| 0.675 \| 0.4937 \| 2.4903 \| 0.675 \| 0.6208 \| 0.3091 \| 0.1301 \| \| 0.7628 \| 46.0 \| 598 \| 0.6518 \| 0.685 \| 0.4949 \| 2.4732 \| 0.685 \| 0.6254 \| 0.3164 \| 0.1235 \| \| 0.7628 \| 47.0 \| 611 \| 0.6499 \| 0.685 \| 0.4936 \| 2.4924 \| 0.685 \| 0.6273 \| 0.3124 \| 0.1323 \| \| 0.7628 \| 48.0 \| 624 \| 0.6490 \| 0.7 \| 0.4925 \| 2.4999 \| 0.7 \| 0.6353 \| 0.3147 \| 0.1243 \| \| 0.7628 \| 49.0 \| 637 \| 0.6510 \| 0.685 \| 0.4933 \| 2.5758 \| 0.685 \| 0.6242 \| 0.3206 \| 0.1281 \| \| 0.7628 \| 50.0 \| 650 \| 0.6481 \| 0.69 \| 0.4919 \| 2.4969 \| 0.69 \| 0.6317 \| 0.3029 \| 0.1260 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/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 [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0899 - Accuracy: 0.395 - Brier Loss: 0.6867 - Nll: 4.7352 - F1 Micro: 0.395 - F1 Macro: 0.2347 - Ece: 0.2366 - Aurc: 0.3626 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.1202 \| 0.17 \| 0.8964 \| 8.4790 \| 0.17 \| 0.1089 \| 0.2136 \| 0.8244 \| \| No log \| 2.0 \| 26 \| 1.0772 \| 0.165 \| 0.8950 \| 8.2397 \| 0.165 \| 0.0929 \| 0.2120 \| 0.8534 \| \| No log \| 3.0 \| 39 \| 0.9427 \| 0.2 \| 0.8847 \| 7.1036 \| 0.2000 \| 0.0796 \| 0.2384 \| 0.7748 \| \| No log \| 4.0 \| 52 \| 0.7947 \| 0.21 \| 0.8720 \| 6.5481 \| 0.2100 \| 0.0649 \| 0.2432 \| 0.7270 \| \| No log \| 5.0 \| 65 \| 0.5378 \| 0.205 \| 0.8432 \| 6.3064 \| 0.205 \| 0.0544 \| 0.2367 \| 0.6763 \| \| No log \| 6.0 \| 78 \| 0.4557 \| 0.18 \| 0.8402 \| 6.3878 \| 0.18 \| 0.0308 \| 0.2384 \| 0.7467 \| \| No log \| 7.0 \| 91 \| 0.4326 \| 0.18 \| 0.8383 \| 6.3386 \| 0.18 \| 0.0308 \| 0.2385 \| 0.7234 \| \| No log \| 8.0 \| 104 \| 0.2832 \| 0.265 \| 0.8085 \| 6.3561 \| 0.265 \| 0.1012 \| 0.2570 \| 0.6272 \| \| No log \| 9.0 \| 117 \| 0.2672 \| 0.255 \| 0.8124 \| 6.2296 \| 0.255 \| 0.0981 \| 0.2569 \| 0.6567 \| \| No log \| 10.0 \| 130 \| 0.2452 \| 0.29 \| 0.7953 \| 6.3199 \| 0.29 \| 0.1153 \| 0.2717 \| 0.5884 \| \| No log \| 11.0 \| 143 \| 0.2155 \| 0.31 \| 0.7764 \| 6.3618 \| 0.31 \| 0.1231 \| 0.2728 \| 0.4803 \| \| No log \| 12.0 \| 156 \| 0.1315 \| 0.31 \| 0.7371 \| 6.2610 \| 0.31 \| 0.1231 \| 0.2343 \| 0.4419 \| \| No log \| 13.0 \| 169 \| 0.1803 \| 0.3 \| 0.7665 \| 6.1189 \| 0.3 \| 0.1187 \| 0.2587 \| 0.4579 \| \| No log \| 14.0 \| 182 \| 0.1426 \| 0.31 \| 0.7386 \| 6.1115 \| 0.31 \| 0.1236 \| 0.2502 \| 0.4341 \| \| No log \| 15.0 \| 195 \| 0.1431 \| 0.31 \| 0.7334 \| 5.9353 \| 0.31 \| 0.1274 \| 0.2624 \| 0.4233 \| \| No log \| 16.0 \| 208 \| 0.1540 \| 0.32 \| 0.7318 \| 5.7102 \| 0.32 \| 0.1432 \| 0.2493 \| 0.4322 \| \| No log \| 17.0 \| 221 \| 0.2603 \| 0.305 \| 0.7784 \| 5.6776 \| 0.305 \| 0.1361 \| 0.2751 \| 0.5118 \| \| No log \| 18.0 \| 234 \| 0.1000 \| 0.35 \| 0.7074 \| 5.4636 \| 0.35 \| 0.1574 \| 0.2420 \| 0.4027 \| \| No log \| 19.0 \| 247 \| 0.1014 \| 0.33 \| 0.7131 \| 5.5297 \| 0.33 \| 0.1413 \| 0.2439 \| 0.4245 \| \| No log \| 20.0 \| 260 \| 0.2862 \| 0.265 \| 0.8013 \| 5.5041 \| 0.265 \| 0.1126 \| 0.2762 \| 0.6324 \| \| No log \| 21.0 \| 273 \| 0.1224 \| 0.34 \| 0.7183 \| 5.2027 \| 0.34 \| 0.1544 \| 0.2673 \| 0.4222 \| \| No log \| 22.0 \| 286 \| 0.1406 \| 0.345 \| 0.7173 \| 5.1426 \| 0.345 \| 0.1612 \| 0.2710 \| 0.4019 \| \| No log \| 23.0 \| 299 \| 0.1509 \| 0.34 \| 0.7270 \| 5.0281 \| 0.34 \| 0.1565 \| 0.2641 \| 0.4178 \| \| No log \| 24.0 \| 312 \| 0.0994 \| 0.37 \| 0.6996 \| 5.1278 \| 0.37 \| 0.1771 \| 0.2390 \| 0.3930 \| \| No log \| 25.0 \| 325 \| 0.1965 \| 0.35 \| 0.7474 \| 5.0356 \| 0.35 \| 0.1707 \| 0.2774 \| 0.4503 \| \| No log \| 26.0 \| 338 \| 0.1104 \| 0.37 \| 0.7085 \| 5.0275 \| 0.37 \| 0.1984 \| 0.2663 \| 0.3927 \| \| No log \| 27.0 \| 351 \| 0.1674 \| 0.34 \| 0.7299 \| 4.9200 \| 0.34 \| 0.1739 \| 0.2787 \| 0.4257 \| \| No log \| 28.0 \| 364 \| 0.2424 \| 0.335 \| 0.7626 \| 5.0286 \| 0.335 \| 0.1693 \| 0.2905 \| 0.5297 \| \| No log \| 29.0 \| 377 \| 0.1261 \| 0.345 \| 0.7185 \| 5.0591 \| 0.345 \| 0.1730 \| 0.2892 \| 0.4142 \| \| No log \| 30.0 \| 390 \| 0.1574 \| 0.365 \| 0.7213 \| 4.8809 \| 0.3650 \| 0.1951 \| 0.2983 \| 0.4062 \| \| No log \| 31.0 \| 403 \| 0.1227 \| 0.365 \| 0.7098 \| 4.8152 \| 0.3650 \| 0.1996 \| 0.2802 \| 0.3992 \| \| No log \| 32.0 \| 416 \| 0.1114 \| 0.355 \| 0.7010 \| 4.8224 \| 0.3550 \| 0.1915 \| 0.2657 \| 0.3958 \| \| No log \| 33.0 \| 429 \| 0.1027 \| 0.39 \| 0.6934 \| 4.7755 \| 0.39 \| 0.2245 \| 0.2653 \| 0.3695 \| \| No log \| 34.0 \| 442 \| 0.0959 \| 0.385 \| 0.6875 \| 4.8715 \| 0.3850 \| 0.2299 \| 0.2591 \| 0.3699 \| \| No log \| 35.0 \| 455 \| 0.0905 \| 0.395 \| 0.6897 \| 4.8649 \| 0.395 \| 0.2367 \| 0.2519 \| 0.3627 \| \| No log \| 36.0 \| 468 \| 0.0879 \| 0.365 \| 0.6911 \| 4.8472 \| 0.3650 \| 0.2132 \| 0.2437 \| 0.3910 \| \| No log \| 37.0 \| 481 \| 0.0867 \| 0.39 \| 0.6881 \| 4.7379 \| 0.39 \| 0.2335 \| 0.2576 \| 0.3680 \| \| No log \| 38.0 \| 494 \| 0.0934 \| 0.4 \| 0.6916 \| 4.6797 \| 0.4000 \| 0.2490 \| 0.2578 \| 0.3628 \| \| 0.2032 \| 39.0 \| 507 \| 0.0928 \| 0.38 \| 0.6901 \| 4.6734 \| 0.38 \| 0.2268 \| 0.2432 \| 0.3783 \| \| 0.2032 \| 40.0 \| 520 \| 0.0995 \| 0.39 \| 0.6875 \| 4.8180 \| 0.39 \| 0.2323 \| 0.2647 \| 0.3730 \| \| 0.2032 \| 41.0 \| 533 \| 0.0944 \| 0.37 \| 0.6892 \| 4.8193 \| 0.37 \| 0.2174 \| 0.2536 \| 0.3862 \| \| 0.2032 \| 42.0 \| 546 \| 0.0904 \| 0.415 \| 0.6885 \| 4.5644 \| 0.415 \| 0.2556 \| 0.2729 \| 0.3573 \| \| 0.2032 \| 43.0 \| 559 \| 0.0951 \| 0.39 \| 0.6899 \| 4.6549 \| 0.39 \| 0.2417 \| 0.2525 \| 0.3692 \| \| 0.2032 \| 44.0 \| 572 \| 0.0884 \| 0.4 \| 0.6860 \| 4.6572 \| 0.4000 \| 0.2402 \| 0.2587 \| 0.3557 \| \| 0.2032 \| 45.0 \| 585 \| 0.0867 \| 0.38 \| 0.6874 \| 4.6558 \| 0.38 \| 0.2278 \| 0.2526 \| 0.3738 \| \| 0.2032 \| 46.0 \| 598 \| 0.0861 \| 0.405 \| 0.6844 \| 4.5777 \| 0.405 \| 0.2537 \| 0.2548 \| 0.3628 \| \| 0.2032 \| 47.0 \| 611 \| 0.0874 \| 0.385 \| 0.6853 \| 4.4946 \| 0.3850 \| 0.2380 \| 0.2570 \| 0.3743 \| \| 0.2032 \| 48.0 \| 624 \| 0.0880 \| 0.405 \| 0.6857 \| 4.5605 \| 0.405 \| 0.2500 \| 0.2489 \| 0.3555 \| \| 0.2032 \| 49.0 \| 637 \| 0.0884 \| 0.4 \| 0.6853 \| 4.6057 \| 0.4000 \| 0.2481 \| 0.2401 \| 0.3616 \| \| 0.2032 \| 50.0 \| 650 \| 0.0899 \| 0.395 \| 0.6867 \| 4.7352 \| 0.395 \| 0.2347 \| 0.2366 \| 0.3626 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_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. --> # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6500 - Accuracy: 0.69 - Brier Loss: 0.5003 - Nll: 2.5629 - F1 Micro: 0.69 - F1 Macro: 0.6350 - Ece: 0.3098 - Aurc: 0.1329 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.4712 \| 0.165 \| 0.8966 \| 8.4652 \| 0.165 \| 0.1101 \| 0.2129 \| 0.8342 \| \| No log \| 2.0 \| 26 \| 1.4590 \| 0.165 \| 0.8951 \| 8.1097 \| 0.165 \| 0.1059 \| 0.2034 \| 0.8021 \| \| No log \| 3.0 \| 39 \| 1.4178 \| 0.175 \| 0.8873 \| 6.8095 \| 0.175 \| 0.0813 \| 0.2150 \| 0.7994 \| \| No log \| 4.0 \| 52 \| 1.3342 \| 0.18 \| 0.8702 \| 6.4137 \| 0.18 \| 0.0475 \| 0.2314 \| 0.7558 \| \| No log \| 5.0 \| 65 \| 1.2828 \| 0.2 \| 0.8587 \| 6.1547 \| 0.2000 \| 0.0642 \| 0.2429 \| 0.7009 \| \| No log \| 6.0 \| 78 \| 1.2675 \| 0.205 \| 0.8548 \| 6.1395 \| 0.205 \| 0.0612 \| 0.2348 \| 0.7022 \| \| No log \| 7.0 \| 91 \| 1.0716 \| 0.31 \| 0.7962 \| 6.4589 \| 0.31 \| 0.1241 \| 0.2787 \| 0.4433 \| \| No log \| 8.0 \| 104 \| 1.1184 \| 0.29 \| 0.8126 \| 6.2585 \| 0.29 \| 0.1394 \| 0.2863 \| 0.5819 \| \| No log \| 9.0 \| 117 \| 1.1021 \| 0.31 \| 0.8075 \| 6.0370 \| 0.31 \| 0.1697 \| 0.2834 \| 0.5458 \| \| No log \| 10.0 \| 130 \| 1.0268 \| 0.33 \| 0.7815 \| 6.1370 \| 0.33 \| 0.1921 \| 0.2856 \| 0.5395 \| \| No log \| 11.0 \| 143 \| 1.0290 \| 0.355 \| 0.7759 \| 5.3640 \| 0.3550 \| 0.2143 \| 0.2795 \| 0.4697 \| \| No log \| 12.0 \| 156 \| 0.9169 \| 0.36 \| 0.7262 \| 5.2997 \| 0.36 \| 0.1995 \| 0.2761 \| 0.4070 \| \| No log \| 13.0 \| 169 \| 0.9903 \| 0.36 \| 0.7586 \| 4.9404 \| 0.36 \| 0.2200 \| 0.2832 \| 0.5343 \| \| No log \| 14.0 \| 182 \| 0.9128 \| 0.425 \| 0.7082 \| 4.5862 \| 0.425 \| 0.2706 \| 0.2834 \| 0.3542 \| \| No log \| 15.0 \| 195 \| 1.0046 \| 0.405 \| 0.7441 \| 3.9763 \| 0.405 \| 0.2759 \| 0.3142 \| 0.4602 \| \| No log \| 16.0 \| 208 \| 0.9277 \| 0.41 \| 0.7146 \| 4.3670 \| 0.41 \| 0.2763 \| 0.2695 \| 0.4409 \| \| No log \| 17.0 \| 221 \| 0.9726 \| 0.505 \| 0.7208 \| 3.5350 \| 0.505 \| 0.3736 \| 0.3332 \| 0.3469 \| \| No log \| 18.0 \| 234 \| 0.7717 \| 0.505 \| 0.6280 \| 3.4386 \| 0.505 \| 0.3412 \| 0.2564 \| 0.2567 \| \| No log \| 19.0 \| 247 \| 0.7723 \| 0.58 \| 0.6143 \| 3.6207 \| 0.58 \| 0.4125 \| 0.3178 \| 0.1847 \| \| No log \| 20.0 \| 260 \| 0.8182 \| 0.57 \| 0.6419 \| 3.1633 \| 0.57 \| 0.4855 \| 0.3517 \| 0.2530 \| \| No log \| 21.0 \| 273 \| 0.7333 \| 0.58 \| 0.5891 \| 3.3014 \| 0.58 \| 0.4512 \| 0.2718 \| 0.2137 \| \| No log \| 22.0 \| 286 \| 0.7374 \| 0.665 \| 0.5856 \| 3.0299 \| 0.665 \| 0.5432 \| 0.3459 \| 0.1657 \| \| No log \| 23.0 \| 299 \| 0.7083 \| 0.645 \| 0.5564 \| 3.0874 \| 0.645 \| 0.5180 \| 0.3112 \| 0.1608 \| \| No log \| 24.0 \| 312 \| 0.7480 \| 0.64 \| 0.5901 \| 3.0218 \| 0.64 \| 0.5410 \| 0.3701 \| 0.1976 \| \| No log \| 25.0 \| 325 \| 0.7547 \| 0.68 \| 0.5894 \| 2.9002 \| 0.68 \| 0.5801 \| 0.3817 \| 0.1559 \| \| No log \| 26.0 \| 338 \| 0.6998 \| 0.65 \| 0.5474 \| 2.9402 \| 0.65 \| 0.5468 \| 0.2875 \| 0.1707 \| \| No log \| 27.0 \| 351 \| 0.6967 \| 0.66 \| 0.5506 \| 2.8344 \| 0.66 \| 0.5578 \| 0.3105 \| 0.1707 \| \| No log \| 28.0 \| 364 \| 0.6733 \| 0.655 \| 0.5332 \| 2.6492 \| 0.655 \| 0.5719 \| 0.2935 \| 0.1554 \| \| No log \| 29.0 \| 377 \| 0.7162 \| 0.67 \| 0.5596 \| 2.7250 \| 0.67 \| 0.5721 \| 0.3388 \| 0.1423 \| \| No log \| 30.0 \| 390 \| 0.6826 \| 0.665 \| 0.5291 \| 2.7460 \| 0.665 \| 0.5797 \| 0.3353 \| 0.1469 \| \| No log \| 31.0 \| 403 \| 0.6761 \| 0.665 \| 0.5195 \| 2.7938 \| 0.665 \| 0.5647 \| 0.3096 \| 0.1485 \| \| No log \| 32.0 \| 416 \| 0.6745 \| 0.695 \| 0.5295 \| 2.6172 \| 0.695 \| 0.6160 \| 0.3171 \| 0.1636 \| \| No log \| 33.0 \| 429 \| 0.6785 \| 0.695 \| 0.5242 \| 2.5816 \| 0.695 \| 0.6115 \| 0.3475 \| 0.1349 \| \| No log \| 34.0 \| 442 \| 0.6688 \| 0.665 \| 0.5174 \| 2.6401 \| 0.665 \| 0.5833 \| 0.2988 \| 0.1427 \| \| No log \| 35.0 \| 455 \| 0.6767 \| 0.675 \| 0.5275 \| 2.6364 \| 0.675 \| 0.6027 \| 0.3285 \| 0.1483 \| \| No log \| 36.0 \| 468 \| 0.6605 \| 0.695 \| 0.5076 \| 2.6483 \| 0.695 \| 0.6252 \| 0.3127 \| 0.1372 \| \| No log \| 37.0 \| 481 \| 0.6538 \| 0.705 \| 0.5029 \| 2.6284 \| 0.705 \| 0.6340 \| 0.3173 \| 0.1220 \| \| No log \| 38.0 \| 494 \| 0.6610 \| 0.695 \| 0.5102 \| 2.5052 \| 0.695 \| 0.6375 \| 0.3128 \| 0.1298 \| \| 0.7532 \| 39.0 \| 507 \| 0.6618 \| 0.695 \| 0.5110 \| 2.5663 \| 0.695 \| 0.6268 \| 0.3297 \| 0.1367 \| \| 0.7532 \| 40.0 \| 520 \| 0.6749 \| 0.69 \| 0.5235 \| 2.5343 \| 0.69 \| 0.6341 \| 0.3256 \| 0.1332 \| \| 0.7532 \| 41.0 \| 533 \| 0.6574 \| 0.695 \| 0.5062 \| 2.4223 \| 0.695 \| 0.6338 \| 0.3292 \| 0.1469 \| \| 0.7532 \| 42.0 \| 546 \| 0.6530 \| 0.695 \| 0.5026 \| 2.6189 \| 0.695 \| 0.6390 \| 0.2950 \| 0.1391 \| \| 0.7532 \| 43.0 \| 559 \| 0.6509 \| 0.685 \| 0.5003 \| 2.5417 \| 0.685 \| 0.6299 \| 0.3150 \| 0.1368 \| \| 0.7532 \| 44.0 \| 572 \| 0.6520 \| 0.71 \| 0.5030 \| 2.4796 \| 0.7100 \| 0.6453 \| 0.3251 \| 0.1286 \| \| 0.7532 \| 45.0 \| 585 \| 0.6494 \| 0.69 \| 0.4994 \| 2.5431 \| 0.69 \| 0.6327 \| 0.3138 \| 0.1279 \| \| 0.7532 \| 46.0 \| 598 \| 0.6515 \| 0.71 \| 0.5007 \| 2.5295 \| 0.7100 \| 0.6541 \| 0.3307 \| 0.1208 \| \| 0.7532 \| 47.0 \| 611 \| 0.6477 \| 0.69 \| 0.4979 \| 2.5971 \| 0.69 \| 0.6323 \| 0.3263 \| 0.1281 \| \| 0.7532 \| 48.0 \| 624 \| 0.6495 \| 0.7 \| 0.5007 \| 2.6162 \| 0.7 \| 0.6395 \| 0.3412 \| 0.1272 \| \| 0.7532 \| 49.0 \| 637 \| 0.6478 \| 0.7 \| 0.4968 \| 2.4946 \| 0.7 \| 0.6386 \| 0.3191 \| 0.1309 \| \| 0.7532 \| 50.0 \| 650 \| 0.6500 \| 0.69 \| 0.5003 \| 2.5629 \| 0.69 \| 0.6350 \| 0.3098 \| 0.1329 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.7	<!-- 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_t1.5_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8009 - Accuracy: 0.695 - Brier Loss: 0.4518 - Nll: 2.3840 - F1 Micro: 0.695 - F1 Macro: 0.6406 - Ece: 0.2661 - Aurc: 0.1211 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.7971 \| 0.17 \| 0.8966 \| 8.4593 \| 0.17 \| 0.1148 \| 0.2202 \| 0.8308 \| \| No log \| 2.0 \| 26 \| 1.7887 \| 0.13 \| 0.8956 \| 8.3211 \| 0.13 \| 0.0772 \| 0.2024 \| 0.8359 \| \| No log \| 3.0 \| 39 \| 1.7450 \| 0.225 \| 0.8868 \| 6.4554 \| 0.225 \| 0.1165 \| 0.2502 \| 0.7572 \| \| No log \| 4.0 \| 52 \| 1.6811 \| 0.24 \| 0.8733 \| 5.9510 \| 0.24 \| 0.0953 \| 0.2651 \| 0.6944 \| \| No log \| 5.0 \| 65 \| 1.6411 \| 0.19 \| 0.8649 \| 6.0993 \| 0.19 \| 0.0493 \| 0.2422 \| 0.7783 \| \| No log \| 6.0 \| 78 \| 1.5475 \| 0.195 \| 0.8429 \| 6.2065 \| 0.195 \| 0.0630 \| 0.2472 \| 0.7110 \| \| No log \| 7.0 \| 91 \| 1.4688 \| 0.3 \| 0.8197 \| 6.0345 \| 0.3 \| 0.1481 \| 0.2936 \| 0.5379 \| \| No log \| 8.0 \| 104 \| 1.5036 \| 0.285 \| 0.8294 \| 5.6660 \| 0.285 \| 0.1428 \| 0.2869 \| 0.6535 \| \| No log \| 9.0 \| 117 \| 1.3901 \| 0.34 \| 0.7934 \| 5.9107 \| 0.34 \| 0.1853 \| 0.2894 \| 0.5277 \| \| No log \| 10.0 \| 130 \| 1.3484 \| 0.37 \| 0.7760 \| 5.6441 \| 0.37 \| 0.2175 \| 0.3177 \| 0.5266 \| \| No log \| 11.0 \| 143 \| 1.3375 \| 0.34 \| 0.7734 \| 5.0872 \| 0.34 \| 0.2083 \| 0.2902 \| 0.5557 \| \| No log \| 12.0 \| 156 \| 1.3639 \| 0.305 \| 0.7834 \| 4.5070 \| 0.305 \| 0.1885 \| 0.2674 \| 0.6177 \| \| No log \| 13.0 \| 169 \| 1.2321 \| 0.415 \| 0.7225 \| 4.3464 \| 0.415 \| 0.2751 \| 0.2943 \| 0.3825 \| \| No log \| 14.0 \| 182 \| 1.1453 \| 0.44 \| 0.6767 \| 4.4158 \| 0.44 \| 0.2864 \| 0.2617 \| 0.3413 \| \| No log \| 15.0 \| 195 \| 1.1830 \| 0.43 \| 0.6965 \| 3.8251 \| 0.4300 \| 0.2972 \| 0.2912 \| 0.4239 \| \| No log \| 16.0 \| 208 \| 1.0572 \| 0.535 \| 0.6230 \| 3.5943 \| 0.535 \| 0.3758 \| 0.2861 \| 0.2291 \| \| No log \| 17.0 \| 221 \| 1.0532 \| 0.585 \| 0.6151 \| 3.3834 \| 0.585 \| 0.4331 \| 0.3278 \| 0.1879 \| \| No log \| 18.0 \| 234 \| 1.0940 \| 0.565 \| 0.6374 \| 3.2290 \| 0.565 \| 0.4431 \| 0.3313 \| 0.2415 \| \| No log \| 19.0 \| 247 \| 0.9877 \| 0.585 \| 0.5886 \| 3.1068 \| 0.585 \| 0.4564 \| 0.2896 \| 0.2110 \| \| No log \| 20.0 \| 260 \| 1.0405 \| 0.61 \| 0.6056 \| 3.1786 \| 0.61 \| 0.5038 \| 0.3428 \| 0.1962 \| \| No log \| 21.0 \| 273 \| 0.9728 \| 0.635 \| 0.5634 \| 2.9133 \| 0.635 \| 0.5293 \| 0.3333 \| 0.1664 \| \| No log \| 22.0 \| 286 \| 0.9425 \| 0.635 \| 0.5527 \| 2.8909 \| 0.635 \| 0.5237 \| 0.3131 \| 0.1796 \| \| No log \| 23.0 \| 299 \| 0.9549 \| 0.65 \| 0.5605 \| 2.8074 \| 0.65 \| 0.5539 \| 0.3283 \| 0.1914 \| \| No log \| 24.0 \| 312 \| 1.0085 \| 0.67 \| 0.5733 \| 2.8377 \| 0.67 \| 0.5543 \| 0.3525 \| 0.1571 \| \| No log \| 25.0 \| 325 \| 0.9140 \| 0.655 \| 0.5257 \| 2.5878 \| 0.655 \| 0.5603 \| 0.3171 \| 0.1495 \| \| No log \| 26.0 \| 338 \| 0.8979 \| 0.65 \| 0.5249 \| 2.7723 \| 0.65 \| 0.5563 \| 0.2843 \| 0.1646 \| \| No log \| 27.0 \| 351 \| 0.8912 \| 0.675 \| 0.5082 \| 2.6562 \| 0.675 \| 0.5837 \| 0.2871 \| 0.1380 \| \| No log \| 28.0 \| 364 \| 0.8966 \| 0.66 \| 0.5242 \| 2.3150 \| 0.66 \| 0.5890 \| 0.3180 \| 0.1777 \| \| No log \| 29.0 \| 377 \| 0.8602 \| 0.67 \| 0.4959 \| 2.5813 \| 0.67 \| 0.5866 \| 0.3023 \| 0.1319 \| \| No log \| 30.0 \| 390 \| 0.8434 \| 0.69 \| 0.4779 \| 2.5451 \| 0.69 \| 0.6130 \| 0.3061 \| 0.1188 \| \| No log \| 31.0 \| 403 \| 0.8406 \| 0.715 \| 0.4782 \| 2.3339 \| 0.715 \| 0.6438 \| 0.3241 \| 0.1092 \| \| No log \| 32.0 \| 416 \| 0.8294 \| 0.71 \| 0.4726 \| 2.5394 \| 0.7100 \| 0.6308 \| 0.2922 \| 0.1218 \| \| No log \| 33.0 \| 429 \| 0.8329 \| 0.68 \| 0.4763 \| 2.4520 \| 0.68 \| 0.6166 \| 0.2592 \| 0.1396 \| \| No log \| 34.0 \| 442 \| 0.8937 \| 0.69 \| 0.5015 \| 2.5649 \| 0.69 \| 0.6357 \| 0.3293 \| 0.1279 \| \| No log \| 35.0 \| 455 \| 0.8358 \| 0.665 \| 0.4807 \| 2.4437 \| 0.665 \| 0.6178 \| 0.2380 \| 0.1473 \| \| No log \| 36.0 \| 468 \| 0.8283 \| 0.685 \| 0.4747 \| 2.5408 \| 0.685 \| 0.6304 \| 0.3126 \| 0.1361 \| \| No log \| 37.0 \| 481 \| 0.8235 \| 0.685 \| 0.4707 \| 2.4620 \| 0.685 \| 0.6300 \| 0.2757 \| 0.1343 \| \| No log \| 38.0 \| 494 \| 0.8289 \| 0.68 \| 0.4778 \| 2.5443 \| 0.68 \| 0.6305 \| 0.2935 \| 0.1469 \| \| 0.9462 \| 39.0 \| 507 \| 0.8373 \| 0.69 \| 0.4728 \| 2.5775 \| 0.69 \| 0.6281 \| 0.3028 \| 0.1149 \| \| 0.9462 \| 40.0 \| 520 \| 0.8062 \| 0.715 \| 0.4548 \| 2.3673 \| 0.715 \| 0.6587 \| 0.2776 \| 0.1133 \| \| 0.9462 \| 41.0 \| 533 \| 0.7990 \| 0.705 \| 0.4517 \| 2.3284 \| 0.705 \| 0.6463 \| 0.2716 \| 0.1185 \| \| 0.9462 \| 42.0 \| 546 \| 0.8210 \| 0.7 \| 0.4650 \| 2.5646 \| 0.7 \| 0.6432 \| 0.2690 \| 0.1199 \| \| 0.9462 \| 43.0 \| 559 \| 0.8102 \| 0.695 \| 0.4558 \| 2.5651 \| 0.695 \| 0.6442 \| 0.2656 \| 0.1184 \| \| 0.9462 \| 44.0 \| 572 \| 0.8061 \| 0.69 \| 0.4566 \| 2.5154 \| 0.69 \| 0.6356 \| 0.2816 \| 0.1267 \| \| 0.9462 \| 45.0 \| 585 \| 0.8018 \| 0.7 \| 0.4531 \| 2.4982 \| 0.7 \| 0.6419 \| 0.2696 \| 0.1192 \| \| 0.9462 \| 46.0 \| 598 \| 0.8040 \| 0.7 \| 0.4521 \| 2.5309 \| 0.7 \| 0.6448 \| 0.2797 \| 0.1166 \| \| 0.9462 \| 47.0 \| 611 \| 0.8062 \| 0.68 \| 0.4560 \| 2.5452 \| 0.68 \| 0.6370 \| 0.2744 \| 0.1217 \| \| 0.9462 \| 48.0 \| 624 \| 0.8011 \| 0.69 \| 0.4529 \| 2.4281 \| 0.69 \| 0.6402 \| 0.2594 \| 0.1224 \| \| 0.9462 \| 49.0 \| 637 \| 0.8017 \| 0.69 \| 0.4532 \| 2.4239 \| 0.69 \| 0.6400 \| 0.2613 \| 0.1261 \| \| 0.9462 \| 50.0 \| 650 \| 0.8009 \| 0.695 \| 0.4518 \| 2.3840 \| 0.695 \| 0.6406 \| 0.2661 \| 0.1211 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9	<!-- 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_t1.5_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8831 - Accuracy: 0.695 - Brier Loss: 0.4126 - Nll: 2.4628 - F1 Micro: 0.695 - F1 Macro: 0.6387 - Ece: 0.2426 - Aurc: 0.1068 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 2.1233 \| 0.16 \| 0.8967 \| 8.5697 \| 0.16 \| 0.1066 \| 0.2078 \| 0.8316 \| \| No log \| 2.0 \| 26 \| 2.1188 \| 0.14 \| 0.8961 \| 8.2960 \| 0.14 \| 0.0886 \| 0.1947 \| 0.8419 \| \| No log \| 3.0 \| 39 \| 2.0764 \| 0.195 \| 0.8873 \| 6.4713 \| 0.195 \| 0.1159 \| 0.2335 \| 0.7665 \| \| No log \| 4.0 \| 52 \| 2.0365 \| 0.21 \| 0.8787 \| 5.7752 \| 0.2100 \| 0.0930 \| 0.2376 \| 0.7548 \| \| No log \| 5.0 \| 65 \| 1.9888 \| 0.2 \| 0.8682 \| 5.8737 \| 0.2000 \| 0.0775 \| 0.2417 \| 0.7314 \| \| No log \| 6.0 \| 78 \| 1.8998 \| 0.215 \| 0.8465 \| 5.8553 \| 0.2150 \| 0.0970 \| 0.2586 \| 0.7063 \| \| No log \| 7.0 \| 91 \| 1.8351 \| 0.33 \| 0.8289 \| 5.7781 \| 0.33 \| 0.1904 \| 0.3089 \| 0.6103 \| \| No log \| 8.0 \| 104 \| 1.7342 \| 0.4 \| 0.7968 \| 5.5366 \| 0.4000 \| 0.2476 \| 0.3457 \| 0.4276 \| \| No log \| 9.0 \| 117 \| 1.6787 \| 0.36 \| 0.7757 \| 5.7414 \| 0.36 \| 0.2148 \| 0.3062 \| 0.4324 \| \| No log \| 10.0 \| 130 \| 1.6942 \| 0.4 \| 0.7870 \| 5.2615 \| 0.4000 \| 0.2831 \| 0.3168 \| 0.5227 \| \| No log \| 11.0 \| 143 \| 1.5992 \| 0.4 \| 0.7489 \| 4.7833 \| 0.4000 \| 0.2649 \| 0.3053 \| 0.4679 \| \| No log \| 12.0 \| 156 \| 1.6071 \| 0.425 \| 0.7532 \| 4.2803 \| 0.425 \| 0.2906 \| 0.3196 \| 0.4646 \| \| No log \| 13.0 \| 169 \| 1.4727 \| 0.48 \| 0.6925 \| 4.1911 \| 0.48 \| 0.3239 \| 0.2957 \| 0.3081 \| \| No log \| 14.0 \| 182 \| 1.4275 \| 0.515 \| 0.6705 \| 3.7980 \| 0.515 \| 0.3569 \| 0.3211 \| 0.2626 \| \| No log \| 15.0 \| 195 \| 1.3282 \| 0.56 \| 0.6200 \| 3.6359 \| 0.56 \| 0.4163 \| 0.2990 \| 0.2213 \| \| No log \| 16.0 \| 208 \| 1.3280 \| 0.565 \| 0.6263 \| 3.4960 \| 0.565 \| 0.4177 \| 0.3217 \| 0.2346 \| \| No log \| 17.0 \| 221 \| 1.3220 \| 0.595 \| 0.6196 \| 3.2202 \| 0.595 \| 0.4639 \| 0.3322 \| 0.1992 \| \| No log \| 18.0 \| 234 \| 1.2359 \| 0.595 \| 0.5840 \| 3.3332 \| 0.595 \| 0.4780 \| 0.3042 \| 0.2011 \| \| No log \| 19.0 \| 247 \| 1.1690 \| 0.625 \| 0.5531 \| 3.2423 \| 0.625 \| 0.5233 \| 0.2940 \| 0.1807 \| \| No log \| 20.0 \| 260 \| 1.1644 \| 0.64 \| 0.5532 \| 3.0542 \| 0.64 \| 0.5429 \| 0.3019 \| 0.1821 \| \| No log \| 21.0 \| 273 \| 1.1611 \| 0.62 \| 0.5516 \| 2.9412 \| 0.62 \| 0.5193 \| 0.2865 \| 0.2160 \| \| No log \| 22.0 \| 286 \| 1.3427 \| 0.585 \| 0.6361 \| 3.0936 \| 0.585 \| 0.5089 \| 0.3442 \| 0.2922 \| \| No log \| 23.0 \| 299 \| 1.1238 \| 0.62 \| 0.5440 \| 2.7924 \| 0.62 \| 0.5458 \| 0.2654 \| 0.2088 \| \| No log \| 24.0 \| 312 \| 1.2008 \| 0.685 \| 0.5615 \| 2.5918 \| 0.685 \| 0.5890 \| 0.3907 \| 0.1516 \| \| No log \| 25.0 \| 325 \| 1.0764 \| 0.695 \| 0.5000 \| 2.6354 \| 0.695 \| 0.6107 \| 0.3126 \| 0.1397 \| \| No log \| 26.0 \| 338 \| 1.0268 \| 0.675 \| 0.4822 \| 2.4798 \| 0.675 \| 0.5992 \| 0.2775 \| 0.1229 \| \| No log \| 27.0 \| 351 \| 1.0340 \| 0.67 \| 0.4893 \| 2.4316 \| 0.67 \| 0.5997 \| 0.2763 \| 0.1638 \| \| No log \| 28.0 \| 364 \| 1.0154 \| 0.665 \| 0.4769 \| 2.6487 \| 0.665 \| 0.6034 \| 0.2590 \| 0.1487 \| \| No log \| 29.0 \| 377 \| 1.0013 \| 0.64 \| 0.4814 \| 2.5899 \| 0.64 \| 0.5771 \| 0.2429 \| 0.1593 \| \| No log \| 30.0 \| 390 \| 1.0173 \| 0.685 \| 0.4714 \| 2.6922 \| 0.685 \| 0.6178 \| 0.2898 \| 0.1423 \| \| No log \| 31.0 \| 403 \| 0.9630 \| 0.695 \| 0.4509 \| 2.6349 \| 0.695 \| 0.6206 \| 0.2746 \| 0.1248 \| \| No log \| 32.0 \| 416 \| 0.9950 \| 0.68 \| 0.4648 \| 2.4144 \| 0.68 \| 0.6362 \| 0.3020 \| 0.1725 \| \| No log \| 33.0 \| 429 \| 0.9711 \| 0.72 \| 0.4502 \| 2.6651 \| 0.72 \| 0.6571 \| 0.2892 \| 0.1268 \| \| No log \| 34.0 \| 442 \| 0.9491 \| 0.705 \| 0.4425 \| 2.7169 \| 0.705 \| 0.6425 \| 0.2541 \| 0.1145 \| \| No log \| 35.0 \| 455 \| 0.9213 \| 0.685 \| 0.4309 \| 2.5736 \| 0.685 \| 0.6174 \| 0.2380 \| 0.1161 \| \| No log \| 36.0 \| 468 \| 0.9144 \| 0.695 \| 0.4297 \| 2.4141 \| 0.695 \| 0.6308 \| 0.2502 \| 0.1154 \| \| No log \| 37.0 \| 481 \| 0.9242 \| 0.715 \| 0.4264 \| 2.7191 \| 0.715 \| 0.6429 \| 0.2386 \| 0.1030 \| \| No log \| 38.0 \| 494 \| 0.9290 \| 0.695 \| 0.4346 \| 2.6515 \| 0.695 \| 0.6367 \| 0.2432 \| 0.1189 \| \| 1.0953 \| 39.0 \| 507 \| 0.9110 \| 0.69 \| 0.4262 \| 2.6615 \| 0.69 \| 0.6328 \| 0.2368 \| 0.1112 \| \| 1.0953 \| 40.0 \| 520 \| 0.9000 \| 0.695 \| 0.4186 \| 2.4590 \| 0.695 \| 0.6417 \| 0.2453 \| 0.1070 \| \| 1.0953 \| 41.0 \| 533 \| 0.8961 \| 0.69 \| 0.4189 \| 2.4170 \| 0.69 \| 0.6368 \| 0.2349 \| 0.1090 \| \| 1.0953 \| 42.0 \| 546 \| 0.9103 \| 0.675 \| 0.4286 \| 2.6129 \| 0.675 \| 0.6193 \| 0.2318 \| 0.1190 \| \| 1.0953 \| 43.0 \| 559 \| 0.8858 \| 0.715 \| 0.4131 \| 2.5243 \| 0.715 \| 0.6517 \| 0.2462 \| 0.1053 \| \| 1.0953 \| 44.0 \| 572 \| 0.8872 \| 0.705 \| 0.4135 \| 2.3272 \| 0.705 \| 0.6542 \| 0.2596 \| 0.1051 \| \| 1.0953 \| 45.0 \| 585 \| 0.8897 \| 0.715 \| 0.4136 \| 2.3788 \| 0.715 \| 0.6532 \| 0.2560 \| 0.1035 \| \| 1.0953 \| 46.0 \| 598 \| 0.8842 \| 0.7 \| 0.4117 \| 2.5325 \| 0.7 \| 0.6446 \| 0.2327 \| 0.1075 \| \| 1.0953 \| 47.0 \| 611 \| 0.8857 \| 0.675 \| 0.4141 \| 2.5451 \| 0.675 \| 0.6203 \| 0.2473 \| 0.1125 \| \| 1.0953 \| 48.0 \| 624 \| 0.8875 \| 0.69 \| 0.4164 \| 2.4696 \| 0.69 \| 0.6352 \| 0.2542 \| 0.1109 \| \| 1.0953 \| 49.0 \| 637 \| 0.8842 \| 0.69 \| 0.4153 \| 2.5338 \| 0.69 \| 0.6358 \| 0.2302 \| 0.1112 \| \| 1.0953 \| 50.0 \| 650 \| 0.8831 \| 0.695 \| 0.4126 \| 2.4628 \| 0.695 \| 0.6387 \| 0.2426 \| 0.1068 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0	<!-- 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_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7844 - Accuracy: 0.742 - Brier Loss: 0.4405 - Nll: 2.8680 - F1 Micro: 0.7420 - F1 Macro: 0.7411 - Ece: 0.1946 - Aurc: 0.1002 ## 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: 64 - eval_batch_size: 64 - 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 \| 250 \| 2.7345 \| 0.153 \| 0.9327 \| 8.3371 \| 0.153 \| 0.1246 \| 0.0866 \| 0.7933 \| \| 2.6983 \| 2.0 \| 500 \| 2.4500 \| 0.4213 \| 0.8816 \| 4.7062 \| 0.4213 \| 0.3924 \| 0.3073 \| 0.4444 \| \| 2.6983 \| 3.0 \| 750 \| 1.7959 \| 0.5012 \| 0.7003 \| 3.3576 \| 0.5012 \| 0.4758 \| 0.1869 \| 0.3051 \| \| 1.7341 \| 4.0 \| 1000 \| 1.3637 \| 0.5985 \| 0.5511 \| 2.8818 \| 0.5985 \| 0.5868 \| 0.1005 \| 0.1935 \| \| 1.7341 \| 5.0 \| 1250 \| 1.1978 \| 0.6498 \| 0.4862 \| 2.7546 \| 0.6498 \| 0.6471 \| 0.0826 \| 0.1500 \| \| 1.0818 \| 6.0 \| 1500 \| 1.0812 \| 0.6853 \| 0.4364 \| 2.6325 \| 0.6853 \| 0.6845 \| 0.0522 \| 0.1217 \| \| 1.0818 \| 7.0 \| 1750 \| 1.0276 \| 0.7013 \| 0.4149 \| 2.5542 \| 0.7013 \| 0.7003 \| 0.0397 \| 0.1108 \| \| 0.7498 \| 8.0 \| 2000 \| 0.9724 \| 0.7133 \| 0.3944 \| 2.4773 \| 0.7133 \| 0.7129 \| 0.0505 \| 0.1040 \| \| 0.7498 \| 9.0 \| 2250 \| 0.9777 \| 0.7248 \| 0.3924 \| 2.4916 \| 0.7248 \| 0.7242 \| 0.0628 \| 0.0992 \| \| 0.5034 \| 10.0 \| 2500 \| 1.0027 \| 0.724 \| 0.3976 \| 2.4974 \| 0.724 \| 0.7250 \| 0.0751 \| 0.1032 \| \| 0.5034 \| 11.0 \| 2750 \| 0.9979 \| 0.729 \| 0.3913 \| 2.5344 \| 0.729 \| 0.7295 \| 0.0805 \| 0.0988 \| \| 0.3237 \| 12.0 \| 3000 \| 1.0553 \| 0.7192 \| 0.4075 \| 2.6242 \| 0.7192 \| 0.7193 \| 0.0963 \| 0.1072 \| \| 0.3237 \| 13.0 \| 3250 \| 1.1162 \| 0.7175 \| 0.4139 \| 2.6543 \| 0.7175 \| 0.7185 \| 0.1295 \| 0.1093 \| \| 0.2023 \| 14.0 \| 3500 \| 1.1259 \| 0.725 \| 0.4140 \| 2.6758 \| 0.7250 \| 0.7246 \| 0.1237 \| 0.1055 \| \| 0.2023 \| 15.0 \| 3750 \| 1.2728 \| 0.7115 \| 0.4381 \| 2.8308 \| 0.7115 \| 0.7147 \| 0.1464 \| 0.1168 \| \| 0.1264 \| 16.0 \| 4000 \| 1.2664 \| 0.7222 \| 0.4296 \| 2.8434 \| 0.7223 \| 0.7236 \| 0.1523 \| 0.1107 \| \| 0.1264 \| 17.0 \| 4250 \| 1.2620 \| 0.724 \| 0.4252 \| 2.7990 \| 0.724 \| 0.7252 \| 0.1563 \| 0.1066 \| \| 0.0802 \| 18.0 \| 4500 \| 1.3362 \| 0.727 \| 0.4293 \| 2.8642 \| 0.7270 \| 0.7267 \| 0.1653 \| 0.1090 \| \| 0.0802 \| 19.0 \| 4750 \| 1.3608 \| 0.7302 \| 0.4288 \| 2.7893 \| 0.7302 \| 0.7318 \| 0.1637 \| 0.1059 \| \| 0.0553 \| 20.0 \| 5000 \| 1.3757 \| 0.7308 \| 0.4303 \| 2.8861 \| 0.7308 \| 0.7300 \| 0.1670 \| 0.1073 \| \| 0.0553 \| 21.0 \| 5250 \| 1.4947 \| 0.7295 \| 0.4420 \| 2.8306 \| 0.7295 \| 0.7300 \| 0.1770 \| 0.1128 \| \| 0.0329 \| 22.0 \| 5500 \| 1.5338 \| 0.7265 \| 0.4416 \| 2.8729 \| 0.7265 \| 0.7273 \| 0.1808 \| 0.1097 \| \| 0.0329 \| 23.0 \| 5750 \| 1.5127 \| 0.7355 \| 0.4362 \| 2.8574 \| 0.7355 \| 0.7366 \| 0.1774 \| 0.1045 \| \| 0.0258 \| 24.0 \| 6000 \| 1.5189 \| 0.7352 \| 0.4360 \| 2.8435 \| 0.7353 \| 0.7344 \| 0.1784 \| 0.1030 \| \| 0.0258 \| 25.0 \| 6250 \| 1.5802 \| 0.7362 \| 0.4404 \| 2.8399 \| 0.7362 \| 0.7362 \| 0.1847 \| 0.1013 \| \| 0.0193 \| 26.0 \| 6500 \| 1.5869 \| 0.737 \| 0.4378 \| 2.8237 \| 0.737 \| 0.7362 \| 0.1846 \| 0.1022 \| \| 0.0193 \| 27.0 \| 6750 \| 1.6160 \| 0.7365 \| 0.4373 \| 2.7928 \| 0.7365 \| 0.7360 \| 0.1864 \| 0.1049 \| \| 0.014 \| 28.0 \| 7000 \| 1.6775 \| 0.7372 \| 0.4426 \| 2.9236 \| 0.7372 \| 0.7373 \| 0.1909 \| 0.1039 \| \| 0.014 \| 29.0 \| 7250 \| 1.6391 \| 0.736 \| 0.4370 \| 2.8717 \| 0.736 \| 0.7358 \| 0.1905 \| 0.0999 \| \| 0.0132 \| 30.0 \| 7500 \| 1.6804 \| 0.7355 \| 0.4434 \| 2.8397 \| 0.7355 \| 0.7360 \| 0.1903 \| 0.1067 \| \| 0.0132 \| 31.0 \| 7750 \| 1.6809 \| 0.738 \| 0.4386 \| 2.8853 \| 0.738 \| 0.7387 \| 0.1920 \| 0.1015 \| \| 0.0121 \| 32.0 \| 8000 \| 1.6953 \| 0.734 \| 0.4443 \| 2.8451 \| 0.734 \| 0.7342 \| 0.1961 \| 0.1013 \| \| 0.0121 \| 33.0 \| 8250 \| 1.7184 \| 0.7425 \| 0.4344 \| 2.8180 \| 0.7425 \| 0.7423 \| 0.1910 \| 0.1014 \| \| 0.0098 \| 34.0 \| 8500 \| 1.7151 \| 0.735 \| 0.4445 \| 2.8532 \| 0.735 \| 0.7337 \| 0.1952 \| 0.1000 \| \| 0.0098 \| 35.0 \| 8750 \| 1.7781 \| 0.7338 \| 0.4484 \| 2.8133 \| 0.7338 \| 0.7351 \| 0.1999 \| 0.1052 \| \| 0.0086 \| 36.0 \| 9000 \| 1.7540 \| 0.7372 \| 0.4443 \| 2.8388 \| 0.7372 \| 0.7388 \| 0.1954 \| 0.1039 \| \| 0.0086 \| 37.0 \| 9250 \| 1.7744 \| 0.738 \| 0.4474 \| 2.8600 \| 0.738 \| 0.7390 \| 0.1953 \| 0.1057 \| \| 0.0079 \| 38.0 \| 9500 \| 1.7446 \| 0.7368 \| 0.4417 \| 2.8485 \| 0.7367 \| 0.7374 \| 0.1972 \| 0.1016 \| \| 0.0079 \| 39.0 \| 9750 \| 1.7700 \| 0.739 \| 0.4398 \| 2.8826 \| 0.739 \| 0.7395 \| 0.1970 \| 0.1023 \| \| 0.0076 \| 40.0 \| 10000 \| 1.7896 \| 0.7368 \| 0.4442 \| 2.8449 \| 0.7367 \| 0.7376 \| 0.1988 \| 0.1033 \| \| 0.0076 \| 41.0 \| 10250 \| 1.7435 \| 0.7402 \| 0.4387 \| 2.8390 \| 0.7402 \| 0.7405 \| 0.1926 \| 0.1031 \| \| 0.0074 \| 42.0 \| 10500 \| 1.7837 \| 0.7338 \| 0.4470 \| 2.8191 \| 0.7338 \| 0.7339 \| 0.2018 \| 0.1035 \| \| 0.0074 \| 43.0 \| 10750 \| 1.8015 \| 0.7392 \| 0.4427 \| 2.8093 \| 0.7392 \| 0.7401 \| 0.1981 \| 0.1017 \| \| 0.0061 \| 44.0 \| 11000 \| 1.8155 \| 0.739 \| 0.4449 \| 2.8333 \| 0.739 \| 0.7406 \| 0.1983 \| 0.1022 \| \| 0.0061 \| 45.0 \| 11250 \| 1.7958 \| 0.7392 \| 0.4426 \| 2.8161 \| 0.7392 \| 0.7385 \| 0.1963 \| 0.1039 \| \| 0.0059 \| 46.0 \| 11500 \| 1.8089 \| 0.7422 \| 0.4411 \| 2.8174 \| 0.7422 \| 0.7422 \| 0.1955 \| 0.1011 \| \| 0.0059 \| 47.0 \| 11750 \| 1.8125 \| 0.743 \| 0.4386 \| 2.8184 \| 0.743 \| 0.7435 \| 0.1939 \| 0.1012 \| \| 0.0053 \| 48.0 \| 12000 \| 1.8004 \| 0.7372 \| 0.4432 \| 2.8413 \| 0.7372 \| 0.7371 \| 0.1995 \| 0.1023 \| \| 0.0053 \| 49.0 \| 12250 \| 1.8075 \| 0.7405 \| 0.4392 \| 2.8569 \| 0.7405 \| 0.7397 \| 0.1962 \| 0.1015 \| \| 0.0055 \| 50.0 \| 12500 \| 1.7844 \| 0.742 \| 0.4405 \| 2.8680 \| 0.7420 \| 0.7411 \| 0.1946 \| 0.1002 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7	<!-- 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_t2.5_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8012 - Accuracy: 0.7 - Brier Loss: 0.4467 - Nll: 2.5682 - F1 Micro: 0.7 - F1 Macro: 0.6313 - Ece: 0.2684 - Aurc: 0.1170 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.8024 \| 0.16 \| 0.8966 \| 8.5001 \| 0.16 \| 0.1073 \| 0.2079 \| 0.8334 \| \| No log \| 2.0 \| 26 \| 1.7941 \| 0.145 \| 0.8957 \| 8.3207 \| 0.145 \| 0.0843 \| 0.2022 \| 0.8435 \| \| No log \| 3.0 \| 39 \| 1.7486 \| 0.2 \| 0.8868 \| 6.2015 \| 0.2000 \| 0.1007 \| 0.2209 \| 0.7900 \| \| No log \| 4.0 \| 52 \| 1.6854 \| 0.205 \| 0.8738 \| 6.0142 \| 0.205 \| 0.0707 \| 0.2453 \| 0.7584 \| \| No log \| 5.0 \| 65 \| 1.6162 \| 0.2 \| 0.8594 \| 6.2364 \| 0.2000 \| 0.0552 \| 0.2466 \| 0.7717 \| \| No log \| 6.0 \| 78 \| 1.5412 \| 0.235 \| 0.8416 \| 6.0423 \| 0.235 \| 0.0902 \| 0.2589 \| 0.7006 \| \| No log \| 7.0 \| 91 \| 1.5011 \| 0.295 \| 0.8304 \| 6.1420 \| 0.295 \| 0.1272 \| 0.2803 \| 0.6124 \| \| No log \| 8.0 \| 104 \| 1.4415 \| 0.3 \| 0.8114 \| 6.0440 \| 0.3 \| 0.1296 \| 0.2870 \| 0.5641 \| \| No log \| 9.0 \| 117 \| 1.3257 \| 0.38 \| 0.7625 \| 5.6923 \| 0.38 \| 0.2198 \| 0.3136 \| 0.3675 \| \| No log \| 10.0 \| 130 \| 1.3748 \| 0.33 \| 0.7905 \| 5.5276 \| 0.33 \| 0.1870 \| 0.2947 \| 0.5985 \| \| No log \| 11.0 \| 143 \| 1.3294 \| 0.39 \| 0.7683 \| 4.9632 \| 0.39 \| 0.2573 \| 0.2940 \| 0.4639 \| \| No log \| 12.0 \| 156 \| 1.2444 \| 0.385 \| 0.7297 \| 4.8431 \| 0.3850 \| 0.2330 \| 0.2849 \| 0.4173 \| \| No log \| 13.0 \| 169 \| 1.2212 \| 0.45 \| 0.7153 \| 4.5819 \| 0.45 \| 0.3051 \| 0.3143 \| 0.3379 \| \| No log \| 14.0 \| 182 \| 1.1835 \| 0.495 \| 0.6888 \| 3.6108 \| 0.495 \| 0.3412 \| 0.3316 \| 0.2873 \| \| No log \| 15.0 \| 195 \| 1.1203 \| 0.47 \| 0.6559 \| 3.6500 \| 0.47 \| 0.3348 \| 0.2935 \| 0.3061 \| \| No log \| 16.0 \| 208 \| 1.1520 \| 0.495 \| 0.6707 \| 3.8106 \| 0.495 \| 0.3632 \| 0.2938 \| 0.3604 \| \| No log \| 17.0 \| 221 \| 1.0261 \| 0.565 \| 0.6021 \| 3.3382 \| 0.565 \| 0.4214 \| 0.2840 \| 0.2047 \| \| No log \| 18.0 \| 234 \| 1.0080 \| 0.61 \| 0.5914 \| 3.2936 \| 0.61 \| 0.4748 \| 0.3240 \| 0.1806 \| \| No log \| 19.0 \| 247 \| 1.0696 \| 0.58 \| 0.6253 \| 3.2354 \| 0.58 \| 0.4686 \| 0.3152 \| 0.2626 \| \| No log \| 20.0 \| 260 \| 0.9733 \| 0.615 \| 0.5722 \| 3.1019 \| 0.615 \| 0.4968 \| 0.3259 \| 0.2066 \| \| No log \| 21.0 \| 273 \| 0.9266 \| 0.625 \| 0.5423 \| 3.0239 \| 0.625 \| 0.5202 \| 0.2834 \| 0.1782 \| \| No log \| 22.0 \| 286 \| 0.9364 \| 0.66 \| 0.5461 \| 2.9031 \| 0.66 \| 0.5461 \| 0.3128 \| 0.1601 \| \| No log \| 23.0 \| 299 \| 0.9181 \| 0.675 \| 0.5307 \| 2.8416 \| 0.675 \| 0.5584 \| 0.3106 \| 0.1462 \| \| No log \| 24.0 \| 312 \| 0.9739 \| 0.665 \| 0.5539 \| 2.8798 \| 0.665 \| 0.5634 \| 0.3325 \| 0.1610 \| \| No log \| 25.0 \| 325 \| 0.8851 \| 0.69 \| 0.5099 \| 2.7336 \| 0.69 \| 0.6013 \| 0.3064 \| 0.1437 \| \| No log \| 26.0 \| 338 \| 0.8755 \| 0.71 \| 0.4979 \| 2.7400 \| 0.7100 \| 0.6032 \| 0.3162 \| 0.1211 \| \| No log \| 27.0 \| 351 \| 0.8653 \| 0.675 \| 0.4964 \| 2.8339 \| 0.675 \| 0.5705 \| 0.2977 \| 0.1386 \| \| No log \| 28.0 \| 364 \| 0.8838 \| 0.675 \| 0.5055 \| 2.7456 \| 0.675 \| 0.5816 \| 0.2969 \| 0.1524 \| \| No log \| 29.0 \| 377 \| 0.8805 \| 0.68 \| 0.5025 \| 2.6942 \| 0.68 \| 0.5855 \| 0.3099 \| 0.1380 \| \| No log \| 30.0 \| 390 \| 0.8585 \| 0.665 \| 0.4891 \| 2.7511 \| 0.665 \| 0.5737 \| 0.2627 \| 0.1370 \| \| No log \| 31.0 \| 403 \| 0.8410 \| 0.675 \| 0.4736 \| 2.6431 \| 0.675 \| 0.5985 \| 0.2670 \| 0.1335 \| \| No log \| 32.0 \| 416 \| 0.8378 \| 0.71 \| 0.4724 \| 2.7320 \| 0.7100 \| 0.6236 \| 0.2885 \| 0.1153 \| \| No log \| 33.0 \| 429 \| 0.8421 \| 0.705 \| 0.4718 \| 2.6331 \| 0.705 \| 0.6326 \| 0.2644 \| 0.1147 \| \| No log \| 34.0 \| 442 \| 0.8350 \| 0.685 \| 0.4697 \| 2.8035 \| 0.685 \| 0.6062 \| 0.2831 \| 0.1291 \| \| No log \| 35.0 \| 455 \| 0.8377 \| 0.7 \| 0.4708 \| 2.4611 \| 0.7 \| 0.6376 \| 0.3173 \| 0.1195 \| \| No log \| 36.0 \| 468 \| 0.8126 \| 0.69 \| 0.4562 \| 2.3909 \| 0.69 \| 0.6154 \| 0.2433 \| 0.1177 \| \| No log \| 37.0 \| 481 \| 0.8299 \| 0.685 \| 0.4673 \| 2.5695 \| 0.685 \| 0.6080 \| 0.2802 \| 0.1261 \| \| No log \| 38.0 \| 494 \| 0.8197 \| 0.685 \| 0.4597 \| 2.6388 \| 0.685 \| 0.6187 \| 0.2690 \| 0.1229 \| \| 0.9314 \| 39.0 \| 507 \| 0.8137 \| 0.695 \| 0.4547 \| 2.7263 \| 0.695 \| 0.6332 \| 0.2581 \| 0.1207 \| \| 0.9314 \| 40.0 \| 520 \| 0.8168 \| 0.69 \| 0.4583 \| 2.6230 \| 0.69 \| 0.6267 \| 0.2696 \| 0.1161 \| \| 0.9314 \| 41.0 \| 533 \| 0.8090 \| 0.7 \| 0.4529 \| 2.6449 \| 0.7 \| 0.6236 \| 0.2445 \| 0.1187 \| \| 0.9314 \| 42.0 \| 546 \| 0.8168 \| 0.68 \| 0.4586 \| 2.5516 \| 0.68 \| 0.6162 \| 0.2722 \| 0.1275 \| \| 0.9314 \| 43.0 \| 559 \| 0.8100 \| 0.7 \| 0.4523 \| 2.5565 \| 0.7 \| 0.6347 \| 0.2869 \| 0.1192 \| \| 0.9314 \| 44.0 \| 572 \| 0.8078 \| 0.7 \| 0.4514 \| 2.5734 \| 0.7 \| 0.6344 \| 0.2583 \| 0.1172 \| \| 0.9314 \| 45.0 \| 585 \| 0.8022 \| 0.715 \| 0.4472 \| 2.4971 \| 0.715 \| 0.6534 \| 0.2890 \| 0.1165 \| \| 0.9314 \| 46.0 \| 598 \| 0.8049 \| 0.695 \| 0.4484 \| 2.4891 \| 0.695 \| 0.6423 \| 0.2722 \| 0.1189 \| \| 0.9314 \| 47.0 \| 611 \| 0.8025 \| 0.705 \| 0.4481 \| 2.4929 \| 0.705 \| 0.6393 \| 0.2650 \| 0.1124 \| \| 0.9314 \| 48.0 \| 624 \| 0.7973 \| 0.7 \| 0.4439 \| 2.5000 \| 0.7 \| 0.6292 \| 0.2718 \| 0.1142 \| \| 0.9314 \| 49.0 \| 637 \| 0.8011 \| 0.7 \| 0.4464 \| 2.5713 \| 0.7 \| 0.6303 \| 0.2400 \| 0.1183 \| \| 0.9314 \| 50.0 \| 650 \| 0.8012 \| 0.7 \| 0.4467 \| 2.5682 \| 0.7 \| 0.6313 \| 0.2684 \| 0.1170 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9	<!-- 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_t2.5_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8672 - Accuracy: 0.71 - Brier Loss: 0.4047 - Nll: 2.1924 - F1 Micro: 0.7100 - F1 Macro: 0.6463 - Ece: 0.2420 - Aurc: 0.1050 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 2.1239 \| 0.16 \| 0.8967 \| 8.4233 \| 0.16 \| 0.1062 \| 0.2101 \| 0.8304 \| \| No log \| 2.0 \| 26 \| 2.1201 \| 0.14 \| 0.8961 \| 8.2220 \| 0.14 \| 0.0876 \| 0.1970 \| 0.8491 \| \| No log \| 3.0 \| 39 \| 2.0724 \| 0.215 \| 0.8865 \| 6.2039 \| 0.2150 \| 0.1169 \| 0.2432 \| 0.7837 \| \| No log \| 4.0 \| 52 \| 2.0291 \| 0.185 \| 0.8773 \| 5.6169 \| 0.185 \| 0.0792 \| 0.2329 \| 0.7651 \| \| No log \| 5.0 \| 65 \| 1.9592 \| 0.215 \| 0.8614 \| 6.0237 \| 0.2150 \| 0.0835 \| 0.2493 \| 0.7373 \| \| No log \| 6.0 \| 78 \| 1.9039 \| 0.205 \| 0.8483 \| 5.9575 \| 0.205 \| 0.0619 \| 0.2493 \| 0.7526 \| \| No log \| 7.0 \| 91 \| 1.8651 \| 0.26 \| 0.8381 \| 5.6215 \| 0.26 \| 0.1490 \| 0.2663 \| 0.6747 \| \| No log \| 8.0 \| 104 \| 1.8342 \| 0.225 \| 0.8311 \| 5.7631 \| 0.225 \| 0.1071 \| 0.2425 \| 0.6919 \| \| No log \| 9.0 \| 117 \| 1.8057 \| 0.31 \| 0.8218 \| 5.2969 \| 0.31 \| 0.2118 \| 0.2795 \| 0.6489 \| \| No log \| 10.0 \| 130 \| 1.5737 \| 0.46 \| 0.7277 \| 5.1748 \| 0.46 \| 0.2853 \| 0.3279 \| 0.2977 \| \| No log \| 11.0 \| 143 \| 1.5629 \| 0.415 \| 0.7331 \| 4.8259 \| 0.415 \| 0.2846 \| 0.2924 \| 0.3880 \| \| No log \| 12.0 \| 156 \| 1.5283 \| 0.45 \| 0.7135 \| 4.0012 \| 0.45 \| 0.3122 \| 0.3298 \| 0.3197 \| \| No log \| 13.0 \| 169 \| 1.4200 \| 0.51 \| 0.6674 \| 3.9849 \| 0.51 \| 0.3400 \| 0.3259 \| 0.2549 \| \| No log \| 14.0 \| 182 \| 1.4334 \| 0.535 \| 0.6710 \| 3.7006 \| 0.535 \| 0.3840 \| 0.3291 \| 0.2584 \| \| No log \| 15.0 \| 195 \| 1.4306 \| 0.45 \| 0.6854 \| 3.8260 \| 0.45 \| 0.3120 \| 0.3055 \| 0.4297 \| \| No log \| 16.0 \| 208 \| 1.3175 \| 0.585 \| 0.6174 \| 3.3484 \| 0.585 \| 0.4401 \| 0.3406 \| 0.1916 \| \| No log \| 17.0 \| 221 \| 1.2680 \| 0.57 \| 0.5998 \| 3.1408 \| 0.57 \| 0.4356 \| 0.2903 \| 0.2136 \| \| No log \| 18.0 \| 234 \| 1.2605 \| 0.58 \| 0.6020 \| 3.2085 \| 0.58 \| 0.4711 \| 0.2915 \| 0.2355 \| \| No log \| 19.0 \| 247 \| 1.2292 \| 0.61 \| 0.5791 \| 3.0633 \| 0.61 \| 0.5021 \| 0.2929 \| 0.2082 \| \| No log \| 20.0 \| 260 \| 1.3872 \| 0.54 \| 0.6604 \| 3.2778 \| 0.54 \| 0.4604 \| 0.3284 \| 0.3506 \| \| No log \| 21.0 \| 273 \| 1.1646 \| 0.625 \| 0.5520 \| 2.8539 \| 0.625 \| 0.5193 \| 0.2828 \| 0.1885 \| \| No log \| 22.0 \| 286 \| 1.1565 \| 0.655 \| 0.5438 \| 2.6915 \| 0.655 \| 0.5437 \| 0.3430 \| 0.1549 \| \| No log \| 23.0 \| 299 \| 1.1041 \| 0.625 \| 0.5298 \| 2.9930 \| 0.625 \| 0.5241 \| 0.2423 \| 0.1906 \| \| No log \| 24.0 \| 312 \| 1.0448 \| 0.685 \| 0.4895 \| 2.8196 \| 0.685 \| 0.5846 \| 0.2701 \| 0.1411 \| \| No log \| 25.0 \| 325 \| 1.0623 \| 0.695 \| 0.4904 \| 2.6903 \| 0.695 \| 0.6086 \| 0.2762 \| 0.1435 \| \| No log \| 26.0 \| 338 \| 0.9872 \| 0.695 \| 0.4607 \| 2.6336 \| 0.695 \| 0.5953 \| 0.2728 \| 0.1180 \| \| No log \| 27.0 \| 351 \| 0.9789 \| 0.705 \| 0.4580 \| 2.6326 \| 0.705 \| 0.6127 \| 0.2579 \| 0.1171 \| \| No log \| 28.0 \| 364 \| 1.0033 \| 0.685 \| 0.4707 \| 2.5747 \| 0.685 \| 0.5906 \| 0.2747 \| 0.1291 \| \| No log \| 29.0 \| 377 \| 1.0152 \| 0.7 \| 0.4789 \| 2.4333 \| 0.7 \| 0.6260 \| 0.2951 \| 0.1739 \| \| No log \| 30.0 \| 390 \| 1.0107 \| 0.715 \| 0.4684 \| 2.5194 \| 0.715 \| 0.6401 \| 0.3197 \| 0.1389 \| \| No log \| 31.0 \| 403 \| 0.9511 \| 0.69 \| 0.4445 \| 2.5648 \| 0.69 \| 0.6131 \| 0.2648 \| 0.1298 \| \| No log \| 32.0 \| 416 \| 0.9586 \| 0.735 \| 0.4448 \| 2.3342 \| 0.735 \| 0.6578 \| 0.2941 \| 0.1275 \| \| No log \| 33.0 \| 429 \| 1.0010 \| 0.73 \| 0.4625 \| 2.4748 \| 0.7300 \| 0.6613 \| 0.3307 \| 0.1202 \| \| No log \| 34.0 \| 442 \| 0.9481 \| 0.71 \| 0.4361 \| 2.4986 \| 0.7100 \| 0.6456 \| 0.2856 \| 0.1228 \| \| No log \| 35.0 \| 455 \| 0.9190 \| 0.69 \| 0.4323 \| 2.6586 \| 0.69 \| 0.6265 \| 0.2538 \| 0.1250 \| \| No log \| 36.0 \| 468 \| 0.9226 \| 0.715 \| 0.4350 \| 2.2652 \| 0.715 \| 0.6507 \| 0.2868 \| 0.1328 \| \| No log \| 37.0 \| 481 \| 0.9017 \| 0.725 \| 0.4182 \| 2.5141 \| 0.7250 \| 0.6590 \| 0.2547 \| 0.1013 \| \| No log \| 38.0 \| 494 \| 0.9092 \| 0.72 \| 0.4218 \| 2.5171 \| 0.72 \| 0.6495 \| 0.2677 \| 0.1055 \| \| 1.0958 \| 39.0 \| 507 \| 0.9093 \| 0.71 \| 0.4221 \| 2.6479 \| 0.7100 \| 0.6456 \| 0.2567 \| 0.1185 \| \| 1.0958 \| 40.0 \| 520 \| 0.8926 \| 0.71 \| 0.4204 \| 2.3785 \| 0.7100 \| 0.6522 \| 0.2396 \| 0.1153 \| \| 1.0958 \| 41.0 \| 533 \| 0.8928 \| 0.715 \| 0.4157 \| 2.5719 \| 0.715 \| 0.6487 \| 0.2708 \| 0.1067 \| \| 1.0958 \| 42.0 \| 546 \| 0.8967 \| 0.715 \| 0.4247 \| 2.6422 \| 0.715 \| 0.6495 \| 0.2525 \| 0.1174 \| \| 1.0958 \| 43.0 \| 559 \| 0.8773 \| 0.695 \| 0.4116 \| 2.5548 \| 0.695 \| 0.6400 \| 0.2491 \| 0.1142 \| \| 1.0958 \| 44.0 \| 572 \| 0.8660 \| 0.71 \| 0.4036 \| 2.2950 \| 0.7100 \| 0.6535 \| 0.2401 \| 0.1009 \| \| 1.0958 \| 45.0 \| 585 \| 0.8718 \| 0.72 \| 0.4057 \| 2.4922 \| 0.72 \| 0.6551 \| 0.2624 \| 0.0998 \| \| 1.0958 \| 46.0 \| 598 \| 0.8737 \| 0.7 \| 0.4070 \| 2.4455 \| 0.7 \| 0.6416 \| 0.2360 \| 0.1052 \| \| 1.0958 \| 47.0 \| 611 \| 0.8707 \| 0.715 \| 0.4094 \| 2.3519 \| 0.715 \| 0.6494 \| 0.2514 \| 0.1086 \| \| 1.0958 \| 48.0 \| 624 \| 0.8640 \| 0.705 \| 0.4039 \| 2.3765 \| 0.705 \| 0.6430 \| 0.2538 \| 0.1041 \| \| 1.0958 \| 49.0 \| 637 \| 0.8702 \| 0.7 \| 0.4066 \| 2.5524 \| 0.7 \| 0.6423 \| 0.2160 \| 0.1080 \| \| 1.0958 \| 50.0 \| 650 \| 0.8672 \| 0.71 \| 0.4047 \| 2.1924 \| 0.7100 \| 0.6463 \| 0.2420 \| 0.1050 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_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_t5.0_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6454 - Accuracy: 0.685 - Brier Loss: 0.4931 - Nll: 2.5040 - F1 Micro: 0.685 - F1 Macro: 0.6171 - Ece: 0.2996 - Aurc: 0.1499 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.4475 \| 0.17 \| 0.8966 \| 8.4781 \| 0.17 \| 0.1126 \| 0.2169 \| 0.8299 \| \| No log \| 2.0 \| 26 \| 1.4360 \| 0.165 \| 0.8955 \| 8.4118 \| 0.165 \| 0.1097 \| 0.2115 \| 0.8359 \| \| No log \| 3.0 \| 39 \| 1.3776 \| 0.16 \| 0.8842 \| 6.1685 \| 0.16 \| 0.0633 \| 0.2066 \| 0.7780 \| \| No log \| 4.0 \| 52 \| 1.3085 \| 0.2 \| 0.8701 \| 6.0521 \| 0.2000 \| 0.0728 \| 0.2332 \| 0.7424 \| \| No log \| 5.0 \| 65 \| 1.2551 \| 0.18 \| 0.8597 \| 6.1887 \| 0.18 \| 0.0491 \| 0.2265 \| 0.7890 \| \| No log \| 6.0 \| 78 \| 1.2118 \| 0.2 \| 0.8489 \| 6.1706 \| 0.2000 \| 0.0631 \| 0.2324 \| 0.7179 \| \| No log \| 7.0 \| 91 \| 1.1759 \| 0.19 \| 0.8418 \| 6.1310 \| 0.19 \| 0.0428 \| 0.2384 \| 0.7431 \| \| No log \| 8.0 \| 104 \| 1.1577 \| 0.195 \| 0.8339 \| 5.7305 \| 0.195 \| 0.0535 \| 0.2399 \| 0.7126 \| \| No log \| 9.0 \| 117 \| 0.9905 \| 0.34 \| 0.7692 \| 6.1092 \| 0.34 \| 0.1567 \| 0.2772 \| 0.4320 \| \| No log \| 10.0 \| 130 \| 0.9603 \| 0.355 \| 0.7541 \| 5.7998 \| 0.3550 \| 0.1969 \| 0.3021 \| 0.4111 \| \| No log \| 11.0 \| 143 \| 1.0839 \| 0.255 \| 0.8087 \| 5.1464 \| 0.255 \| 0.1242 \| 0.2389 \| 0.6769 \| \| No log \| 12.0 \| 156 \| 0.9374 \| 0.39 \| 0.7410 \| 4.8415 \| 0.39 \| 0.2220 \| 0.3037 \| 0.4194 \| \| No log \| 13.0 \| 169 \| 0.9974 \| 0.33 \| 0.7720 \| 4.9023 \| 0.33 \| 0.1732 \| 0.2863 \| 0.6049 \| \| No log \| 14.0 \| 182 \| 0.9393 \| 0.435 \| 0.7251 \| 4.3102 \| 0.435 \| 0.2455 \| 0.3276 \| 0.3645 \| \| No log \| 15.0 \| 195 \| 0.9554 \| 0.39 \| 0.7416 \| 4.0361 \| 0.39 \| 0.2535 \| 0.2721 \| 0.5075 \| \| No log \| 16.0 \| 208 \| 0.8012 \| 0.465 \| 0.6445 \| 4.0129 \| 0.465 \| 0.2935 \| 0.2551 \| 0.2839 \| \| No log \| 17.0 \| 221 \| 0.8033 \| 0.53 \| 0.6418 \| 3.4959 \| 0.53 \| 0.3816 \| 0.3242 \| 0.2458 \| \| No log \| 18.0 \| 234 \| 0.7740 \| 0.57 \| 0.6204 \| 3.4062 \| 0.57 \| 0.4297 \| 0.3139 \| 0.2245 \| \| No log \| 19.0 \| 247 \| 0.7736 \| 0.6 \| 0.6124 \| 3.3460 \| 0.6 \| 0.4408 \| 0.3017 \| 0.1919 \| \| No log \| 20.0 \| 260 \| 0.9105 \| 0.555 \| 0.6919 \| 3.2115 \| 0.555 \| 0.4524 \| 0.3604 \| 0.3099 \| \| No log \| 21.0 \| 273 \| 0.7416 \| 0.61 \| 0.5948 \| 3.1349 \| 0.61 \| 0.5093 \| 0.3176 \| 0.2233 \| \| No log \| 22.0 \| 286 \| 0.7318 \| 0.655 \| 0.5815 \| 3.1259 \| 0.655 \| 0.5433 \| 0.3478 \| 0.1672 \| \| No log \| 23.0 \| 299 \| 0.7799 \| 0.59 \| 0.6079 \| 3.0590 \| 0.59 \| 0.4963 \| 0.3340 \| 0.2455 \| \| No log \| 24.0 \| 312 \| 0.7886 \| 0.665 \| 0.6038 \| 2.9965 \| 0.665 \| 0.5575 \| 0.3773 \| 0.1623 \| \| No log \| 25.0 \| 325 \| 0.7083 \| 0.66 \| 0.5602 \| 3.0752 \| 0.66 \| 0.5582 \| 0.3283 \| 0.1772 \| \| No log \| 26.0 \| 338 \| 0.6882 \| 0.63 \| 0.5507 \| 2.9022 \| 0.63 \| 0.5404 \| 0.2963 \| 0.1851 \| \| No log \| 27.0 \| 351 \| 0.6774 \| 0.66 \| 0.5348 \| 2.7876 \| 0.66 \| 0.5674 \| 0.3095 \| 0.1662 \| \| No log \| 28.0 \| 364 \| 0.8111 \| 0.675 \| 0.6067 \| 2.7578 \| 0.675 \| 0.5800 \| 0.3905 \| 0.1923 \| \| No log \| 29.0 \| 377 \| 0.6803 \| 0.645 \| 0.5338 \| 2.8666 \| 0.645 \| 0.5486 \| 0.3054 \| 0.1646 \| \| No log \| 30.0 \| 390 \| 0.6835 \| 0.685 \| 0.5336 \| 2.5944 \| 0.685 \| 0.5840 \| 0.3119 \| 0.1595 \| \| No log \| 31.0 \| 403 \| 0.6810 \| 0.655 \| 0.5309 \| 2.7112 \| 0.655 \| 0.5625 \| 0.2879 \| 0.1786 \| \| No log \| 32.0 \| 416 \| 0.6848 \| 0.685 \| 0.5194 \| 2.6456 \| 0.685 \| 0.5893 \| 0.3314 \| 0.1350 \| \| No log \| 33.0 \| 429 \| 0.6631 \| 0.695 \| 0.5063 \| 2.6286 \| 0.695 \| 0.5980 \| 0.3198 \| 0.1314 \| \| No log \| 34.0 \| 442 \| 0.6639 \| 0.69 \| 0.5126 \| 2.3890 \| 0.69 \| 0.5834 \| 0.2990 \| 0.1376 \| \| No log \| 35.0 \| 455 \| 0.6736 \| 0.675 \| 0.5172 \| 2.3291 \| 0.675 \| 0.6014 \| 0.3148 \| 0.1646 \| \| No log \| 36.0 \| 468 \| 0.6648 \| 0.68 \| 0.5137 \| 2.4549 \| 0.68 \| 0.6156 \| 0.3316 \| 0.1492 \| \| No log \| 37.0 \| 481 \| 0.6543 \| 0.7 \| 0.5006 \| 2.4275 \| 0.7 \| 0.6130 \| 0.3041 \| 0.1342 \| \| No log \| 38.0 \| 494 \| 0.6514 \| 0.675 \| 0.5001 \| 2.4064 \| 0.675 \| 0.5984 \| 0.2963 \| 0.1491 \| \| 0.7462 \| 39.0 \| 507 \| 0.6498 \| 0.71 \| 0.4988 \| 2.5772 \| 0.7100 \| 0.6405 \| 0.2980 \| 0.1335 \| \| 0.7462 \| 40.0 \| 520 \| 0.6496 \| 0.705 \| 0.4964 \| 2.5649 \| 0.705 \| 0.6386 \| 0.3060 \| 0.1380 \| \| 0.7462 \| 41.0 \| 533 \| 0.6562 \| 0.68 \| 0.5027 \| 2.5816 \| 0.68 \| 0.6026 \| 0.3100 \| 0.1467 \| \| 0.7462 \| 42.0 \| 546 \| 0.6632 \| 0.68 \| 0.5089 \| 2.4570 \| 0.68 \| 0.6112 \| 0.2989 \| 0.1500 \| \| 0.7462 \| 43.0 \| 559 \| 0.6437 \| 0.7 \| 0.4885 \| 2.3648 \| 0.7 \| 0.6331 \| 0.2741 \| 0.1427 \| \| 0.7462 \| 44.0 \| 572 \| 0.6435 \| 0.705 \| 0.4894 \| 2.4253 \| 0.705 \| 0.6370 \| 0.3043 \| 0.1390 \| \| 0.7462 \| 45.0 \| 585 \| 0.6457 \| 0.695 \| 0.4929 \| 2.3611 \| 0.695 \| 0.6314 \| 0.3021 \| 0.1449 \| \| 0.7462 \| 46.0 \| 598 \| 0.6437 \| 0.695 \| 0.4912 \| 2.3639 \| 0.695 \| 0.6370 \| 0.2984 \| 0.1436 \| \| 0.7462 \| 47.0 \| 611 \| 0.6466 \| 0.685 \| 0.4933 \| 2.4859 \| 0.685 \| 0.6306 \| 0.2936 \| 0.1474 \| \| 0.7462 \| 48.0 \| 624 \| 0.6470 \| 0.67 \| 0.4950 \| 2.3782 \| 0.67 \| 0.6070 \| 0.3139 \| 0.1547 \| \| 0.7462 \| 49.0 \| 637 \| 0.6477 \| 0.675 \| 0.4945 \| 2.4509 \| 0.675 \| 0.6092 \| 0.2852 \| 0.1527 \| \| 0.7462 \| 50.0 \| 650 \| 0.6454 \| 0.685 \| 0.4931 \| 2.5040 \| 0.685 \| 0.6171 \| 0.2996 \| 0.1499 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7	<!-- 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_t5.0_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7861 - Accuracy: 0.705 - Brier Loss: 0.4410 - Nll: 2.6519 - F1 Micro: 0.705 - F1 Macro: 0.6403 - Ece: 0.2724 - Aurc: 0.1188 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 1.7831 \| 0.165 \| 0.8966 \| 8.4414 \| 0.165 \| 0.1121 \| 0.2151 \| 0.8335 \| \| No log \| 2.0 \| 26 \| 1.7753 \| 0.145 \| 0.8958 \| 8.5715 \| 0.145 \| 0.0954 \| 0.1998 \| 0.8332 \| \| No log \| 3.0 \| 39 \| 1.7334 \| 0.175 \| 0.8877 \| 6.4682 \| 0.175 \| 0.0756 \| 0.2069 \| 0.7896 \| \| No log \| 4.0 \| 52 \| 1.6604 \| 0.185 \| 0.8723 \| 6.0351 \| 0.185 \| 0.0505 \| 0.2328 \| 0.7549 \| \| No log \| 5.0 \| 65 \| 1.5874 \| 0.18 \| 0.8560 \| 6.0732 \| 0.18 \| 0.0431 \| 0.2285 \| 0.7506 \| \| No log \| 6.0 \| 78 \| 1.5223 \| 0.185 \| 0.8415 \| 6.1638 \| 0.185 \| 0.0479 \| 0.2419 \| 0.7530 \| \| No log \| 7.0 \| 91 \| 1.4642 \| 0.35 \| 0.8239 \| 6.0328 \| 0.35 \| 0.1696 \| 0.3081 \| 0.5219 \| \| No log \| 8.0 \| 104 \| 1.3599 \| 0.35 \| 0.7825 \| 6.2102 \| 0.35 \| 0.1908 \| 0.2977 \| 0.4172 \| \| No log \| 9.0 \| 117 \| 1.3083 \| 0.385 \| 0.7566 \| 5.7128 \| 0.3850 \| 0.2203 \| 0.3012 \| 0.3842 \| \| No log \| 10.0 \| 130 \| 1.3151 \| 0.365 \| 0.7670 \| 5.1073 \| 0.3650 \| 0.2150 \| 0.2923 \| 0.4891 \| \| No log \| 11.0 \| 143 \| 1.3736 \| 0.295 \| 0.7950 \| 5.3584 \| 0.295 \| 0.1747 \| 0.2716 \| 0.6360 \| \| No log \| 12.0 \| 156 \| 1.2655 \| 0.425 \| 0.7380 \| 4.0312 \| 0.425 \| 0.2789 \| 0.3273 \| 0.3366 \| \| No log \| 13.0 \| 169 \| 1.1696 \| 0.475 \| 0.6901 \| 3.9627 \| 0.4750 \| 0.3083 \| 0.3011 \| 0.2825 \| \| No log \| 14.0 \| 182 \| 1.2992 \| 0.355 \| 0.7473 \| 3.9098 \| 0.3550 \| 0.2292 \| 0.2675 \| 0.4929 \| \| No log \| 15.0 \| 195 \| 1.1698 \| 0.51 \| 0.6881 \| 3.7143 \| 0.51 \| 0.3691 \| 0.3333 \| 0.3278 \| \| No log \| 16.0 \| 208 \| 1.0624 \| 0.515 \| 0.6274 \| 3.8387 \| 0.515 \| 0.3631 \| 0.2821 \| 0.2583 \| \| No log \| 17.0 \| 221 \| 1.0970 \| 0.565 \| 0.6421 \| 3.3302 \| 0.565 \| 0.4493 \| 0.3362 \| 0.2373 \| \| No log \| 18.0 \| 234 \| 1.0029 \| 0.625 \| 0.5883 \| 3.3820 \| 0.625 \| 0.4675 \| 0.3005 \| 0.1660 \| \| No log \| 19.0 \| 247 \| 1.0384 \| 0.605 \| 0.6093 \| 3.3183 \| 0.605 \| 0.4863 \| 0.3252 \| 0.2145 \| \| No log \| 20.0 \| 260 \| 1.0686 \| 0.62 \| 0.6234 \| 3.0246 \| 0.62 \| 0.5155 \| 0.3625 \| 0.2334 \| \| No log \| 21.0 \| 273 \| 0.9641 \| 0.62 \| 0.5685 \| 2.9225 \| 0.62 \| 0.5259 \| 0.3103 \| 0.2063 \| \| No log \| 22.0 \| 286 \| 1.0054 \| 0.665 \| 0.5849 \| 3.0792 \| 0.665 \| 0.5614 \| 0.3636 \| 0.1863 \| \| No log \| 23.0 \| 299 \| 0.9959 \| 0.675 \| 0.5734 \| 2.9829 \| 0.675 \| 0.5577 \| 0.3619 \| 0.1806 \| \| No log \| 24.0 \| 312 \| 0.9044 \| 0.675 \| 0.5267 \| 2.8952 \| 0.675 \| 0.5712 \| 0.2989 \| 0.1475 \| \| No log \| 25.0 \| 325 \| 0.9803 \| 0.655 \| 0.5627 \| 2.7501 \| 0.655 \| 0.5418 \| 0.3415 \| 0.1919 \| \| No log \| 26.0 \| 338 \| 0.8814 \| 0.65 \| 0.5176 \| 2.8421 \| 0.65 \| 0.5619 \| 0.2665 \| 0.1694 \| \| No log \| 27.0 \| 351 \| 0.8555 \| 0.69 \| 0.4928 \| 2.7870 \| 0.69 \| 0.5831 \| 0.3091 \| 0.1279 \| \| No log \| 28.0 \| 364 \| 0.8290 \| 0.69 \| 0.4777 \| 2.6377 \| 0.69 \| 0.5976 \| 0.2551 \| 0.1290 \| \| No log \| 29.0 \| 377 \| 0.8593 \| 0.685 \| 0.4949 \| 2.5880 \| 0.685 \| 0.5776 \| 0.3083 \| 0.1279 \| \| No log \| 30.0 \| 390 \| 0.8226 \| 0.685 \| 0.4678 \| 2.8938 \| 0.685 \| 0.5884 \| 0.2820 \| 0.1249 \| \| No log \| 31.0 \| 403 \| 0.8578 \| 0.69 \| 0.4857 \| 2.6150 \| 0.69 \| 0.6024 \| 0.3109 \| 0.1344 \| \| No log \| 32.0 \| 416 \| 0.8330 \| 0.685 \| 0.4753 \| 2.5999 \| 0.685 \| 0.6047 \| 0.2688 \| 0.1407 \| \| No log \| 33.0 \| 429 \| 0.8268 \| 0.7 \| 0.4683 \| 2.6138 \| 0.7 \| 0.6193 \| 0.2913 \| 0.1315 \| \| No log \| 34.0 \| 442 \| 0.8535 \| 0.715 \| 0.4749 \| 2.5059 \| 0.715 \| 0.6450 \| 0.2931 \| 0.1190 \| \| No log \| 35.0 \| 455 \| 0.8334 \| 0.665 \| 0.4752 \| 2.3839 \| 0.665 \| 0.5950 \| 0.2762 \| 0.1397 \| \| No log \| 36.0 \| 468 \| 0.8025 \| 0.71 \| 0.4553 \| 2.4803 \| 0.7100 \| 0.6302 \| 0.2889 \| 0.1178 \| \| No log \| 37.0 \| 481 \| 0.8142 \| 0.715 \| 0.4563 \| 2.6785 \| 0.715 \| 0.6426 \| 0.2989 \| 0.1048 \| \| No log \| 38.0 \| 494 \| 0.8124 \| 0.7 \| 0.4538 \| 2.5320 \| 0.7 \| 0.6332 \| 0.2594 \| 0.1132 \| \| 0.9303 \| 39.0 \| 507 \| 0.7888 \| 0.69 \| 0.4452 \| 2.6427 \| 0.69 \| 0.6269 \| 0.2583 \| 0.1224 \| \| 0.9303 \| 40.0 \| 520 \| 0.7907 \| 0.705 \| 0.4458 \| 2.6942 \| 0.705 \| 0.6367 \| 0.2688 \| 0.1155 \| \| 0.9303 \| 41.0 \| 533 \| 0.7918 \| 0.71 \| 0.4442 \| 2.4378 \| 0.7100 \| 0.6558 \| 0.2816 \| 0.1132 \| \| 0.9303 \| 42.0 \| 546 \| 0.8005 \| 0.725 \| 0.4479 \| 2.6088 \| 0.7250 \| 0.6576 \| 0.2914 \| 0.1049 \| \| 0.9303 \| 43.0 \| 559 \| 0.7879 \| 0.72 \| 0.4421 \| 2.7052 \| 0.72 \| 0.6592 \| 0.2741 \| 0.1122 \| \| 0.9303 \| 44.0 \| 572 \| 0.7910 \| 0.71 \| 0.4461 \| 2.6463 \| 0.7100 \| 0.6463 \| 0.3119 \| 0.1188 \| \| 0.9303 \| 45.0 \| 585 \| 0.7922 \| 0.705 \| 0.4450 \| 2.6453 \| 0.705 \| 0.6481 \| 0.2753 \| 0.1211 \| \| 0.9303 \| 46.0 \| 598 \| 0.7915 \| 0.715 \| 0.4429 \| 2.6970 \| 0.715 \| 0.6526 \| 0.2741 \| 0.1107 \| \| 0.9303 \| 47.0 \| 611 \| 0.7809 \| 0.705 \| 0.4370 \| 2.6841 \| 0.705 \| 0.6453 \| 0.2734 \| 0.1158 \| \| 0.9303 \| 48.0 \| 624 \| 0.7771 \| 0.705 \| 0.4350 \| 2.6168 \| 0.705 \| 0.6423 \| 0.2652 \| 0.1139 \| \| 0.9303 \| 49.0 \| 637 \| 0.7826 \| 0.705 \| 0.4377 \| 2.5091 \| 0.705 \| 0.6423 \| 0.2758 \| 0.1202 \| \| 0.9303 \| 50.0 \| 650 \| 0.7861 \| 0.705 \| 0.4410 \| 2.6519 \| 0.705 \| 0.6403 \| 0.2724 \| 0.1188 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
dima806/man_woman_face_image_detection	Returns with about 98.7% accuracy whether the face belongs to man or woman based on face image. See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/t9MgehgAAZEJAXOebhfjO.png) ``` Classification report: precision recall f1-score support man 0.9885 0.9857 0.9871 51062 woman 0.9857 0.9885 0.9871 51062 accuracy 0.9871 102124 macro avg 0.9871 0.9871 0.9871 102124 weighted avg 0.9871 0.9871 0.9871 102124 ```	[ "man", "woman" ]
bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9	<!-- 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_t5.0_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8809 - Accuracy: 0.7 - Brier Loss: 0.4126 - Nll: 2.4279 - F1 Micro: 0.7 - F1 Macro: 0.6279 - Ece: 0.2569 - Aurc: 0.1111 ## 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: 64 - eval_batch_size: 64 - 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 \| 13 \| 2.1185 \| 0.165 \| 0.8967 \| 8.5399 \| 0.165 \| 0.1130 \| 0.2151 \| 0.8331 \| \| No log \| 2.0 \| 26 \| 2.1127 \| 0.13 \| 0.8958 \| 8.1152 \| 0.13 \| 0.0842 \| 0.1816 \| 0.8392 \| \| No log \| 3.0 \| 39 \| 2.0781 \| 0.165 \| 0.8888 \| 6.8828 \| 0.165 \| 0.0878 \| 0.2150 \| 0.8082 \| \| No log \| 4.0 \| 52 \| 2.0197 \| 0.22 \| 0.8762 \| 5.7578 \| 0.22 \| 0.1155 \| 0.2521 \| 0.7521 \| \| No log \| 5.0 \| 65 \| 1.9499 \| 0.205 \| 0.8601 \| 6.0641 \| 0.205 \| 0.0951 \| 0.2567 \| 0.7355 \| \| No log \| 6.0 \| 78 \| 1.9019 \| 0.25 \| 0.8483 \| 5.8930 \| 0.25 \| 0.1178 \| 0.2728 \| 0.6862 \| \| No log \| 7.0 \| 91 \| 1.8252 \| 0.28 \| 0.8301 \| 5.8062 \| 0.28 \| 0.1660 \| 0.2890 \| 0.6982 \| \| No log \| 8.0 \| 104 \| 1.8194 \| 0.28 \| 0.8275 \| 5.2642 \| 0.28 \| 0.1625 \| 0.2874 \| 0.6935 \| \| No log \| 9.0 \| 117 \| 1.7671 \| 0.355 \| 0.8109 \| 5.1326 \| 0.3550 \| 0.2211 \| 0.3018 \| 0.5678 \| \| No log \| 10.0 \| 130 \| 1.6582 \| 0.355 \| 0.7774 \| 5.2226 \| 0.3550 \| 0.2200 \| 0.2991 \| 0.5305 \| \| No log \| 11.0 \| 143 \| 1.5849 \| 0.395 \| 0.7422 \| 5.0239 \| 0.395 \| 0.2436 \| 0.2979 \| 0.3974 \| \| No log \| 12.0 \| 156 \| 1.4908 \| 0.46 \| 0.7001 \| 4.2790 \| 0.46 \| 0.3169 \| 0.3091 \| 0.3003 \| \| No log \| 13.0 \| 169 \| 1.6016 \| 0.395 \| 0.7496 \| 4.2149 \| 0.395 \| 0.2793 \| 0.2929 \| 0.4640 \| \| No log \| 14.0 \| 182 \| 1.4714 \| 0.475 \| 0.6971 \| 4.0742 \| 0.4750 \| 0.3299 \| 0.3177 \| 0.3613 \| \| No log \| 15.0 \| 195 \| 1.5007 \| 0.46 \| 0.7119 \| 3.8252 \| 0.46 \| 0.3145 \| 0.3111 \| 0.3954 \| \| No log \| 16.0 \| 208 \| 1.4352 \| 0.515 \| 0.6776 \| 3.4028 \| 0.515 \| 0.3948 \| 0.3376 \| 0.2993 \| \| No log \| 17.0 \| 221 \| 1.2890 \| 0.575 \| 0.6104 \| 3.4453 \| 0.575 \| 0.4478 \| 0.2940 \| 0.2119 \| \| No log \| 18.0 \| 234 \| 1.2190 \| 0.595 \| 0.5719 \| 3.2413 \| 0.595 \| 0.4662 \| 0.2608 \| 0.1981 \| \| No log \| 19.0 \| 247 \| 1.2287 \| 0.59 \| 0.5764 \| 3.2303 \| 0.59 \| 0.4857 \| 0.2811 \| 0.2020 \| \| No log \| 20.0 \| 260 \| 1.1726 \| 0.64 \| 0.5494 \| 2.9544 \| 0.64 \| 0.5307 \| 0.2993 \| 0.1708 \| \| No log \| 21.0 \| 273 \| 1.1305 \| 0.61 \| 0.5384 \| 2.9557 \| 0.61 \| 0.5170 \| 0.2771 \| 0.1949 \| \| No log \| 22.0 \| 286 \| 1.1256 \| 0.645 \| 0.5295 \| 2.7934 \| 0.645 \| 0.5381 \| 0.3181 \| 0.1629 \| \| No log \| 23.0 \| 299 \| 1.1209 \| 0.645 \| 0.5217 \| 2.8697 \| 0.645 \| 0.5432 \| 0.3055 \| 0.1687 \| \| No log \| 24.0 \| 312 \| 1.2513 \| 0.685 \| 0.5917 \| 2.7262 \| 0.685 \| 0.5639 \| 0.3779 \| 0.1833 \| \| No log \| 25.0 \| 325 \| 1.0321 \| 0.695 \| 0.4819 \| 2.7202 \| 0.695 \| 0.5896 \| 0.2810 \| 0.1280 \| \| No log \| 26.0 \| 338 \| 1.0405 \| 0.645 \| 0.4957 \| 2.6116 \| 0.645 \| 0.5661 \| 0.2515 \| 0.1700 \| \| No log \| 27.0 \| 351 \| 1.0580 \| 0.695 \| 0.4933 \| 2.7436 \| 0.695 \| 0.5996 \| 0.2967 \| 0.1339 \| \| No log \| 28.0 \| 364 \| 0.9740 \| 0.65 \| 0.4575 \| 2.5682 \| 0.65 \| 0.5731 \| 0.2513 \| 0.1384 \| \| No log \| 29.0 \| 377 \| 0.9934 \| 0.695 \| 0.4651 \| 2.5753 \| 0.695 \| 0.6108 \| 0.2775 \| 0.1171 \| \| No log \| 30.0 \| 390 \| 0.9900 \| 0.645 \| 0.4695 \| 2.6280 \| 0.645 \| 0.5668 \| 0.2459 \| 0.1558 \| \| No log \| 31.0 \| 403 \| 0.9671 \| 0.695 \| 0.4504 \| 2.8174 \| 0.695 \| 0.6094 \| 0.2505 \| 0.1188 \| \| No log \| 32.0 \| 416 \| 0.9327 \| 0.715 \| 0.4324 \| 2.5285 \| 0.715 \| 0.6415 \| 0.2565 \| 0.1086 \| \| No log \| 33.0 \| 429 \| 0.9628 \| 0.71 \| 0.4464 \| 2.5876 \| 0.7100 \| 0.6435 \| 0.2709 \| 0.1152 \| \| No log \| 34.0 \| 442 \| 0.9316 \| 0.715 \| 0.4353 \| 2.7111 \| 0.715 \| 0.6334 \| 0.2361 \| 0.1078 \| \| No log \| 35.0 \| 455 \| 0.9275 \| 0.7 \| 0.4364 \| 2.5226 \| 0.7 \| 0.6251 \| 0.2586 \| 0.1207 \| \| No log \| 36.0 \| 468 \| 0.9301 \| 0.7 \| 0.4346 \| 2.6464 \| 0.7 \| 0.6232 \| 0.2482 \| 0.1142 \| \| No log \| 37.0 \| 481 \| 0.9013 \| 0.695 \| 0.4194 \| 2.5575 \| 0.695 \| 0.6197 \| 0.2554 \| 0.1098 \| \| No log \| 38.0 \| 494 \| 0.9008 \| 0.695 \| 0.4196 \| 2.6270 \| 0.695 \| 0.6156 \| 0.2246 \| 0.1063 \| \| 1.0903 \| 39.0 \| 507 \| 0.9185 \| 0.71 \| 0.4311 \| 2.6290 \| 0.7100 \| 0.6362 \| 0.2626 \| 0.1165 \| \| 1.0903 \| 40.0 \| 520 \| 0.9053 \| 0.685 \| 0.4254 \| 2.5057 \| 0.685 \| 0.6239 \| 0.2210 \| 0.1171 \| \| 1.0903 \| 41.0 \| 533 \| 0.8955 \| 0.7 \| 0.4189 \| 2.4823 \| 0.7 \| 0.6291 \| 0.1995 \| 0.1103 \| \| 1.0903 \| 42.0 \| 546 \| 0.9012 \| 0.69 \| 0.4223 \| 2.5377 \| 0.69 \| 0.6195 \| 0.2486 \| 0.1119 \| \| 1.0903 \| 43.0 \| 559 \| 0.8894 \| 0.71 \| 0.4138 \| 2.6167 \| 0.7100 \| 0.6382 \| 0.2459 \| 0.1022 \| \| 1.0903 \| 44.0 \| 572 \| 0.8846 \| 0.695 \| 0.4132 \| 2.5130 \| 0.695 \| 0.6265 \| 0.2198 \| 0.1093 \| \| 1.0903 \| 45.0 \| 585 \| 0.8946 \| 0.69 \| 0.4190 \| 2.6357 \| 0.69 \| 0.6230 \| 0.2375 \| 0.1145 \| \| 1.0903 \| 46.0 \| 598 \| 0.8931 \| 0.705 \| 0.4168 \| 2.6306 \| 0.705 \| 0.6342 \| 0.2555 \| 0.1102 \| \| 1.0903 \| 47.0 \| 611 \| 0.8842 \| 0.71 \| 0.4160 \| 2.3021 \| 0.7100 \| 0.6347 \| 0.2096 \| 0.1120 \| \| 1.0903 \| 48.0 \| 624 \| 0.8805 \| 0.695 \| 0.4140 \| 2.3447 \| 0.695 \| 0.6237 \| 0.2181 \| 0.1128 \| \| 1.0903 \| 49.0 \| 637 \| 0.8816 \| 0.7 \| 0.4142 \| 2.4358 \| 0.7 \| 0.6295 \| 0.2550 \| 0.1112 \| \| 1.0903 \| 50.0 \| 650 \| 0.8809 \| 0.7 \| 0.4126 \| 2.4279 \| 0.7 \| 0.6279 \| 0.2569 \| 0.1111 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
yfh/food	<!-- 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. --> # food This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6313 - eval_accuracy: 0.856 - eval_runtime: 739.9774 - eval_samples_per_second: 1.351 - eval_steps_per_second: 0.085 - epoch: 0.15 - step: 38 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "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" ]
zkdeng/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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2188 - Accuracy: 0.92 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6077 \| 0.96 \| 12 \| 0.3408 \| 0.895 \| \| 0.3469 \| 2.0 \| 25 \| 0.2188 \| 0.92 \| \| 0.2627 \| 2.88 \| 36 \| 0.2183 \| 0.915 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]
zkdeng/swin-tiny-patch4-window7-224-finetuned-black_widow	<!-- 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-black_widow This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1422 - Accuracy: 0.945 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1728 \| 0.96 \| 12 \| 0.1506 \| 0.935 \| \| 0.1408 \| 2.0 \| 25 \| 0.1422 \| 0.945 \| \| 0.1669 \| 2.96 \| 37 \| 0.1289 \| 0.945 \| \| 0.1618 \| 4.0 \| 50 \| 0.1126 \| 0.945 \| \| 0.1383 \| 4.8 \| 60 \| 0.1200 \| 0.94 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]
aichoux/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: 0.3209 - Accuracy: 0.8902 ## 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 8 \| 2.7448 \| 0.0314 \| \| 2.7716 \| 2.0 \| 16 \| 2.5834 \| 0.1765 \| \| 2.5974 \| 3.0 \| 24 \| 2.3608 \| 0.3020 \| \| 2.3426 \| 4.0 \| 32 \| 2.1157 \| 0.3333 \| \| 1.9747 \| 5.0 \| 40 \| 1.7539 \| 0.4627 \| \| 1.9747 \| 6.0 \| 48 \| 1.3641 \| 0.6078 \| \| 1.5182 \| 7.0 \| 56 \| 1.0755 \| 0.6471 \| \| 1.198 \| 8.0 \| 64 \| 0.8743 \| 0.7216 \| \| 1.0206 \| 9.0 \| 72 \| 0.7666 \| 0.7294 \| \| 0.8731 \| 10.0 \| 80 \| 0.7035 \| 0.7490 \| \| 0.8731 \| 11.0 \| 88 \| 0.6122 \| 0.7608 \| \| 0.7938 \| 12.0 \| 96 \| 0.6508 \| 0.7490 \| \| 0.7286 \| 13.0 \| 104 \| 0.5081 \| 0.7961 \| \| 0.659 \| 14.0 \| 112 \| 0.5536 \| 0.7961 \| \| 0.6232 \| 15.0 \| 120 \| 0.5079 \| 0.8 \| \| 0.6232 \| 16.0 \| 128 \| 0.4483 \| 0.8314 \| \| 0.6028 \| 17.0 \| 136 \| 0.4096 \| 0.8157 \| \| 0.5333 \| 18.0 \| 144 \| 0.3710 \| 0.8510 \| \| 0.5053 \| 19.0 \| 152 \| 0.4810 \| 0.8039 \| \| 0.4717 \| 20.0 \| 160 \| 0.4121 \| 0.8235 \| \| 0.4717 \| 21.0 \| 168 \| 0.4021 \| 0.8392 \| \| 0.4728 \| 22.0 \| 176 \| 0.3780 \| 0.8588 \| \| 0.4347 \| 23.0 \| 184 \| 0.3374 \| 0.8745 \| \| 0.4545 \| 24.0 \| 192 \| 0.4056 \| 0.8431 \| \| 0.3954 \| 25.0 \| 200 \| 0.4088 \| 0.8745 \| \| 0.3954 \| 26.0 \| 208 \| 0.4169 \| 0.8392 \| \| 0.4145 \| 27.0 \| 216 \| 0.3262 \| 0.8706 \| \| 0.3895 \| 28.0 \| 224 \| 0.4235 \| 0.8706 \| \| 0.4185 \| 29.0 \| 232 \| 0.3482 \| 0.8706 \| \| 0.3686 \| 30.0 \| 240 \| 0.3088 \| 0.8824 \| \| 0.3686 \| 31.0 \| 248 \| 0.3230 \| 0.8902 \| \| 0.3617 \| 32.0 \| 256 \| 0.3473 \| 0.8824 \| \| 0.3136 \| 33.0 \| 264 \| 0.3793 \| 0.8627 \| \| 0.3482 \| 34.0 \| 272 \| 0.3477 \| 0.8588 \| \| 0.3519 \| 35.0 \| 280 \| 0.3692 \| 0.8667 \| \| 0.3519 \| 36.0 \| 288 \| 0.3611 \| 0.8627 \| \| 0.3311 \| 37.0 \| 296 \| 0.3233 \| 0.8745 \| \| 0.3222 \| 38.0 \| 304 \| 0.3416 \| 0.8627 \| \| 0.3013 \| 39.0 \| 312 \| 0.3198 \| 0.8824 \| \| 0.2871 \| 40.0 \| 320 \| 0.3308 \| 0.8667 \| \| 0.2871 \| 41.0 \| 328 \| 0.3246 \| 0.8667 \| \| 0.3154 \| 42.0 \| 336 \| 0.3943 \| 0.8667 \| \| 0.2735 \| 43.0 \| 344 \| 0.3186 \| 0.8784 \| \| 0.2911 \| 44.0 \| 352 \| 0.3132 \| 0.8824 \| \| 0.266 \| 45.0 \| 360 \| 0.3204 \| 0.8980 \| \| 0.266 \| 46.0 \| 368 \| 0.3097 \| 0.8784 \| \| 0.2686 \| 47.0 \| 376 \| 0.3075 \| 0.8902 \| \| 0.2818 \| 48.0 \| 384 \| 0.3192 \| 0.8902 \| \| 0.2492 \| 49.0 \| 392 \| 0.3434 \| 0.8745 \| \| 0.276 \| 50.0 \| 400 \| 0.3237 \| 0.8824 \| \| 0.276 \| 51.0 \| 408 \| 0.3450 \| 0.8745 \| \| 0.245 \| 52.0 \| 416 \| 0.3284 \| 0.8706 \| \| 0.2292 \| 53.0 \| 424 \| 0.3263 \| 0.8902 \| \| 0.2252 \| 54.0 \| 432 \| 0.3216 \| 0.8745 \| \| 0.2483 \| 55.0 \| 440 \| 0.3359 \| 0.8863 \| \| 0.2483 \| 56.0 \| 448 \| 0.3314 \| 0.8902 \| \| 0.2549 \| 57.0 \| 456 \| 0.3932 \| 0.8745 \| \| 0.2247 \| 58.0 \| 464 \| 0.3189 \| 0.8745 \| \| 0.2344 \| 59.0 \| 472 \| 0.3251 \| 0.8745 \| \| 0.2315 \| 60.0 \| 480 \| 0.3289 \| 0.8824 \| \| 0.2315 \| 61.0 \| 488 \| 0.3058 \| 0.8745 \| \| 0.2109 \| 62.0 \| 496 \| 0.2999 \| 0.8863 \| \| 0.2325 \| 63.0 \| 504 \| 0.3078 \| 0.8980 \| \| 0.2126 \| 64.0 \| 512 \| 0.3531 \| 0.8784 \| \| 0.1975 \| 65.0 \| 520 \| 0.3394 \| 0.8902 \| \| 0.1975 \| 66.0 \| 528 \| 0.3113 \| 0.8902 \| \| 0.1998 \| 67.0 \| 536 \| 0.3365 \| 0.8941 \| \| 0.2208 \| 68.0 \| 544 \| 0.2854 \| 0.9020 \| \| 0.2126 \| 69.0 \| 552 \| 0.3170 \| 0.8941 \| \| 0.2352 \| 70.0 \| 560 \| 0.3155 \| 0.8824 \| \| 0.2352 \| 71.0 \| 568 \| 0.3327 \| 0.8824 \| \| 0.1724 \| 72.0 \| 576 \| 0.3503 \| 0.8902 \| \| 0.2038 \| 73.0 \| 584 \| 0.3309 \| 0.8824 \| \| 0.1919 \| 74.0 \| 592 \| 0.3299 \| 0.8902 \| \| 0.2199 \| 75.0 \| 600 \| 0.3347 \| 0.8863 \| \| 0.2199 \| 76.0 \| 608 \| 0.3471 \| 0.8824 \| \| 0.2075 \| 77.0 \| 616 \| 0.3437 \| 0.8863 \| \| 0.2206 \| 78.0 \| 624 \| 0.3161 \| 0.8824 \| \| 0.1655 \| 79.0 \| 632 \| 0.3227 \| 0.8784 \| \| 0.1765 \| 80.0 \| 640 \| 0.3302 \| 0.8784 \| \| 0.1765 \| 81.0 \| 648 \| 0.3153 \| 0.8745 \| \| 0.1832 \| 82.0 \| 656 \| 0.3010 \| 0.8745 \| \| 0.185 \| 83.0 \| 664 \| 0.3266 \| 0.8941 \| \| 0.1627 \| 84.0 \| 672 \| 0.3192 \| 0.8941 \| \| 0.176 \| 85.0 \| 680 \| 0.3125 \| 0.8863 \| \| 0.176 \| 86.0 \| 688 \| 0.3241 \| 0.8745 \| \| 0.1723 \| 87.0 \| 696 \| 0.3124 \| 0.8784 \| \| 0.1477 \| 88.0 \| 704 \| 0.3109 \| 0.8745 \| \| 0.1703 \| 89.0 \| 712 \| 0.3196 \| 0.8824 \| \| 0.1919 \| 90.0 \| 720 \| 0.3186 \| 0.8980 \| \| 0.1919 \| 91.0 \| 728 \| 0.3178 \| 0.8902 \| \| 0.1465 \| 92.0 \| 736 \| 0.3241 \| 0.8824 \| \| 0.155 \| 93.0 \| 744 \| 0.3281 \| 0.8784 \| \| 0.1829 \| 94.0 \| 752 \| 0.3263 \| 0.8824 \| \| 0.167 \| 95.0 \| 760 \| 0.3282 \| 0.8824 \| \| 0.167 \| 96.0 \| 768 \| 0.3290 \| 0.8824 \| \| 0.166 \| 97.0 \| 776 \| 0.3253 \| 0.8902 \| \| 0.1756 \| 98.0 \| 784 \| 0.3231 \| 0.8863 \| \| 0.157 \| 99.0 \| 792 \| 0.3215 \| 0.8902 \| \| 0.1492 \| 100.0 \| 800 \| 0.3209 \| 0.8902 \| ### Framework versions - Transformers 4.33.3 - Pytorch 1.11.0+cu113 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "beijing", "biaoend", "biaomiddle", "biaoout", "biaoxie", "biaozheng", "dazhe", "kaishi", "loufeng", "panend", "panout", "panxie", "panzheng", "weibu" ]
GayatriC/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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
DamarJati/Face-Mask-Detection	<!-- 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. --> # Face-Mask-Detection 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: 0.0051 - Accuracy: 0.9992 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0344 \| 1.0 \| 83 \| 0.0051 \| 0.9992 \| \| 0.0112 \| 2.0 \| 166 \| 0.0052 \| 0.9983 \| \| 0.0146 \| 3.0 \| 249 \| 0.0045 \| 0.9992 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "withmask", "withoutmask" ]
navradio/swin-tiny-patch4-window7-224-PE	<!-- 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-PE 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: 0.4489 - Accuracy: 0.7980 ## 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: 0.0025 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6872 \| 1.0 \| 11 \| 0.6535 \| 0.6061 \| \| 0.7287 \| 2.0 \| 22 \| 0.6601 \| 0.6397 \| \| 0.7212 \| 3.0 \| 33 \| 0.6740 \| 0.5657 \| \| 0.6947 \| 4.0 \| 44 \| 0.6531 \| 0.6532 \| \| 0.6783 \| 5.0 \| 55 \| 0.6739 \| 0.5724 \| \| 0.6816 \| 6.0 \| 66 \| 0.6274 \| 0.6599 \| \| 0.6428 \| 7.0 \| 77 \| 0.6671 \| 0.6330 \| \| 0.6928 \| 8.0 \| 88 \| 0.6380 \| 0.6498 \| \| 0.6767 \| 9.0 \| 99 \| 0.6875 \| 0.6061 \| \| 0.6918 \| 10.0 \| 110 \| 0.6859 \| 0.5690 \| \| 0.6845 \| 11.0 \| 121 \| 0.6810 \| 0.5657 \| \| 0.6826 \| 12.0 \| 132 \| 0.6919 \| 0.5185 \| \| 0.6877 \| 13.0 \| 143 \| 0.6693 \| 0.6061 \| \| 0.6709 \| 14.0 \| 154 \| 0.6660 \| 0.5690 \| \| 0.6707 \| 15.0 \| 165 \| 0.6764 \| 0.5690 \| \| 0.6703 \| 16.0 \| 176 \| 0.6467 \| 0.6296 \| \| 0.6629 \| 17.0 \| 187 \| 0.6471 \| 0.6431 \| \| 0.6557 \| 18.0 \| 198 \| 0.6597 \| 0.6229 \| \| 0.659 \| 19.0 \| 209 \| 0.6451 \| 0.6027 \| \| 0.65 \| 20.0 \| 220 \| 0.6638 \| 0.6094 \| \| 0.6453 \| 21.0 \| 231 \| 0.6544 \| 0.6162 \| \| 0.6426 \| 22.0 \| 242 \| 0.6565 \| 0.5825 \| \| 0.6339 \| 23.0 \| 253 \| 0.6743 \| 0.6296 \| \| 0.6236 \| 24.0 \| 264 \| 0.6669 \| 0.5960 \| \| 0.6427 \| 25.0 \| 275 \| 0.6379 \| 0.6532 \| \| 0.6439 \| 26.0 \| 286 \| 0.6361 \| 0.6263 \| \| 0.6212 \| 27.0 \| 297 \| 0.6540 \| 0.6465 \| \| 0.6186 \| 28.0 \| 308 \| 0.5925 \| 0.6700 \| \| 0.6162 \| 29.0 \| 319 \| 0.6224 \| 0.6734 \| \| 0.6237 \| 30.0 \| 330 \| 0.6018 \| 0.6667 \| \| 0.6061 \| 31.0 \| 341 \| 0.5735 \| 0.6801 \| \| 0.6138 \| 32.0 \| 352 \| 0.6425 \| 0.6566 \| \| 0.595 \| 33.0 \| 363 \| 0.5827 \| 0.6768 \| \| 0.5869 \| 34.0 \| 374 \| 0.5956 \| 0.7172 \| \| 0.577 \| 35.0 \| 385 \| 0.5458 \| 0.7003 \| \| 0.5766 \| 36.0 \| 396 \| 0.5603 \| 0.6869 \| \| 0.5726 \| 37.0 \| 407 \| 0.5339 \| 0.7340 \| \| 0.5702 \| 38.0 \| 418 \| 0.5577 \| 0.7138 \| \| 0.5762 \| 39.0 \| 429 \| 0.5262 \| 0.7374 \| \| 0.5543 \| 40.0 \| 440 \| 0.5091 \| 0.7441 \| \| 0.5339 \| 41.0 \| 451 \| 0.5185 \| 0.7542 \| \| 0.5428 \| 42.0 \| 462 \| 0.5023 \| 0.7542 \| \| 0.5349 \| 43.0 \| 473 \| 0.5439 \| 0.7306 \| \| 0.5319 \| 44.0 \| 484 \| 0.4745 \| 0.7811 \| \| 0.5294 \| 45.0 \| 495 \| 0.5432 \| 0.7172 \| \| 0.5314 \| 46.0 \| 506 \| 0.4511 \| 0.7912 \| \| 0.5073 \| 47.0 \| 517 \| 0.4379 \| 0.8047 \| \| 0.5028 \| 48.0 \| 528 \| 0.4487 \| 0.7980 \| \| 0.4985 \| 49.0 \| 539 \| 0.4550 \| 0.7946 \| \| 0.4826 \| 50.0 \| 550 \| 0.4489 \| 0.7980 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "cm", "non_cm" ]
fahmindra/activity_classification	<!-- 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. --> # activity_classification 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: - Loss: 0.7087 - Accuracy: 0.8012 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.7167 \| 1.0 \| 157 \| 1.6188 \| 0.6964 \| \| 1.0511 \| 2.0 \| 315 \| 1.0981 \| 0.7381 \| \| 0.9184 \| 3.0 \| 472 \| 0.9225 \| 0.7710 \| \| 0.7396 \| 4.0 \| 630 \| 0.8333 \| 0.7802 \| \| 0.6873 \| 5.0 \| 787 \| 0.7917 \| 0.7849 \| \| 0.6579 \| 6.0 \| 945 \| 0.7510 \| 0.7845 \| \| 0.5857 \| 7.0 \| 1102 \| 0.7672 \| 0.7845 \| \| 0.4968 \| 8.0 \| 1260 \| 0.7467 \| 0.7857 \| \| 0.513 \| 9.0 \| 1417 \| 0.7156 \| 0.7940 \| \| 0.4957 \| 9.97 \| 1570 \| 0.7073 \| 0.8024 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]
awrysfab/human_action_classification	<!-- 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. --> # human_action_classification 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: - Loss: 3.3689 - Accuracy: 0.0728 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3354 \| 1.0 \| 197 \| 2.9994 \| 0.0717 \| \| 0.9519 \| 2.0 \| 394 \| 3.3635 \| 0.0778 \| \| 0.8178 \| 3.0 \| 591 \| 3.5103 \| 0.0763 \| \| 0.7122 \| 4.0 \| 788 \| 3.7261 \| 0.0683 \| \| 0.7532 \| 5.0 \| 985 \| 3.7279 \| 0.0661 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]
michaelsinanta/smoke_detector	<!-- 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. --> # smoke_detector 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 smokedataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0187 - Accuracy: 0.9951 ## 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 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1404 \| 1.0 \| 716 \| 0.0396 \| 0.9902 \| \| 0.0493 \| 2.0 \| 1432 \| 0.0337 \| 0.9920 \| \| 0.0237 \| 3.0 \| 2148 \| 0.0263 \| 0.9934 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "cloud", "other", "smoke" ]
mmuratarat/kvasir-v2-classifier	This repo contains the artifacts of a model, using a pre-trained Visual Transformer model and fine-tuning it on a custom dataset. There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch 🤗 Hugging Face Transformers provides access to thousands of pretrained models for a wide range of tasks. When you use a pretrained model, you train it on a dataset specific to your task. This is known as fine-tuning, an incredibly powerful training technique. ## Dataset Here, we use Kvasir dataset v2. It consists of images, annotated and verified by medical doctors (experienced endoscopists), including several classes showing anatomical landmarks, phatological findings or endoscopic procedures in the gastrointestinal tract It is a multi-class dataset consisting of 1,000 images per class with a total of 8,000 images for eight different classes. These classes consist of pathological findings (esophagitis, polyps, ulcerative colitis), anatomical landmarks (z-line, pylorus, cecum), and normal and regular findings (normal colon mucosa, stool), and polyp removal cases (dyed and lifted polyps, dyed resection margins) The dataset can be download from [here](https://datasets.simula.no/kvasir/) which weights around ~2.3 GB. and is free for research and educational purposes only. ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/kvasir_v2_examples.png?raw=true) ## Model The [Hugging Face transformers](https://huggingface.co/docs/transformers/index) package is a very popular Python library which provides access to the HuggingFace Hub where we can find a lot of pretrained models and pipelines for a variety of tasks in domains such as Natural Language Processing (NLP), Computer Vision (CV) or Automatic Speech Recognition (ASR). A Vision Transformer-based model is used in this experiment. Vision Transformer (ViT) was introduced in June 2021 by a team of researchers at Google Brain (https://arxiv.org/abs/2010.11929). The Vision Transformer (ViT) model was proposed in "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. It’s the first paper that successfully trains a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/vit_architecture.jpg?raw=true) In this case, we'll be using [the google/vit-base-patch16-224-in21k model](https://huggingface.co/google/vit-base-patch16-224-in21k) from Hugging Face. ## Training hyperparameters The following hyperparameters were used during training: * learning_rate: 2e-5 * lr_scheduler_type: linear * warmup_steps = 500 * weight_decay = 0.01 * warmup_ratio=0.0 * train_batch_size: 16 * eval_batch_size: 32 * seed: 42 * num_epochs: 5 * optimizer: Adam * adam_beta1=0.9, * adam_beta2=0.999, * adam_epsilon=1e-08, ## Evaluation Metrics We have used usual evaluation metrics for this image classification task, that include weighted average precision, F1 score and recall, and overall accuracy. In multi-class classification problems, the weighted average method adjusts for class imbalance by assigning a weight proportional to the number of instances in each class. ## Training results \| Epoch \| Training Loss \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:---------: \|:-----------------: \|:-------------------: \|:------------: \|:-------: \|:-------------: \|:----------: \| \| 1 \| 1.4341 \| 0.62736 \| 0.89417 \| 0.89285 \| 0.90208 \| 0.89417 \| \| 2 \| 0.4203 \| 0.3135 \| 0.92917 \| 0.929 \| 0.93058 \| 0.92917 \| \| 3 \| 0.2272 \| 0.251 \| 0.9375 \| 0.93745 \| 0.938 \| 0.9375 \| \| 4 \| 0.146 \| 0.24937 \| 0.93833 \| 0.93814 \| 0.94072 \| 0.93833 \| \| 5 \| 0.1034 \| 0.2383 \| 0.93917 \| 0.9391 \| 0.93992 \| 0.93917 \| ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/kvasir_vit_model_progress.png?raw=true) ## Training Metrics epoch = 5.0 total_flos = 2.634869332279296e+18 train_loss = 0.46618968290441176 train_runtime = 0:01:51.45 train_samples_per_second = 5.07 train_steps_per_second = 0.317 global_step = 2125 ## Fine-tuned model The pre-trained model has been pushed to Hugging Face Hub and can be found on https://huggingface.co/mmuratarat/kvasir-v2-classifier. You can make inferences by either using "Hosted Inference API" on the Hub or locally pulling the model from the Hub. ## How to use Here is how to use this pre-trained model to classify an image of GI tract: ```python from transformers import AutoModelForImageClassification, AutoFeatureExtractor from PIL import Image import requests # this is an image from "polyps" class url = 'https://github.com/mmuratarat/turkish/blob/master/_posts/images/example_polyps_image.jpg?raw=true' image = Image.open(requests.get(url, stream=True).raw) model = AutoModelForImageClassification.from_pretrained("mmuratarat/kvasir-v2-classifier") feature_extractor = AutoFeatureExtractor.from_pretrained("mmuratarat/kvasir-v2-classifier") inputs = feature_extractor(image, return_tensors="pt") id2label = {'0': 'dyed-lifted-polyps', '1': 'dyed-resection-margins', '2': 'esophagitis', '3': 'normal-cecum', '4': 'normal-pylorus', '5': 'normal-z-line', '6': 'polyps', '7': 'ulcerative-colitis'} logits = model(*inputs).logits predicted_label = logits.argmax(-1).item() predicted_class = id2label[str(predicted_label)] predicted_class ``` ## Framework versions Transformers 4.34.0 * Pytorch 2.0.1+cu118 * Datasets 2.14.5 * Tokenizers 0.14.0 * scikit-learn 1.2.2 * scipy 1.11.3 * numpy 1.23.5 * accelerate 0.23.0 * pandas 1.5.3 ## Contact Please reach out to [email protected] if you have any questions or feedback. ## Source Code You can find the source code for obtaining this pre-trained model on [mmuratarat/kvasir-v2-ViT-classifier]( https://github.com/mmuratarat/kvasir-v2-ViT-classifier) repository of Github. Note that `Kvasir_ViT.ipynb` file contains Turkish commentary but the code itself is self-explanatory. ## Citation In all documents and papers that use or refer to this pre-trained model or report benchmarking results, a reference to this model have to be included.	[ "dyed-lifted-polyps", "dyed-resection-margins", "esophagitis", "normal-cecum", "normal-pylorus", "normal-z-line", "polyps", "ulcerative-colitis" ]
CHMD08/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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
dima806/top_15_anime_characters_image_detection	Returns anime character name given an image with about 98% accuracy. See https://www.kaggle.com/code/dima806/anime-character-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Killua 1.0000 1.0000 1.0000 57 Sakata Gintoki 1.0000 0.9655 0.9825 58 Eren Yeager 0.9649 0.9649 0.9649 57 Ichigo 0.9825 0.9825 0.9825 57 Lelouch Lamperouge 1.0000 1.0000 1.0000 58 Naruto 1.0000 1.0000 1.0000 58 Goku 0.9655 0.9825 0.9739 57 Vegeta 0.9649 0.9649 0.9649 57 Zoro 0.9355 1.0000 0.9667 58 Natsu Dragneel 1.0000 1.0000 1.0000 58 Gon 1.0000 0.9310 0.9643 58 Sasuke 0.9333 0.9655 0.9492 58 Elric Edward 1.0000 0.9825 0.9912 57 Light Yagami 0.9828 0.9828 0.9828 58 Luffy 1.0000 1.0000 1.0000 58 accuracy 0.9815 864 macro avg 0.9820 0.9815 0.9815 864 weighted avg 0.9820 0.9815 0.9815 864 ```	[ "killua", "sakata gintoki", "eren yeager", "ichigo", "lelouch lamperouge", "naruto", "goku", "vegeta", "zoro", "natsu dragneel", "gon", "sasuke", "elric edward", "light yagami", "luffy" ]
farhanyh/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. --> # 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: 1.6470 - Accuracy: 0.909 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.718 \| 0.99 \| 62 \| 2.5596 \| 0.842 \| \| 1.8555 \| 2.0 \| 125 \| 1.8344 \| 0.873 \| \| 1.6437 \| 2.98 \| 186 \| 1.6470 \| 0.909 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "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" ]
crangana/trained-race	<!-- 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. --> # trained-race This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.9830 - Accuracy: 0.6258 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3923 \| 0.18 \| 1000 \| 1.3550 \| 0.4712 \| \| 1.1517 \| 0.37 \| 2000 \| 1.1854 \| 0.5429 \| \| 1.2405 \| 0.55 \| 3000 \| 1.1001 \| 0.5754 \| \| 1.0752 \| 0.74 \| 4000 \| 1.0330 \| 0.6018 \| \| 1.0986 \| 0.92 \| 5000 \| 0.9973 \| 0.6173 \| \| 1.0007 \| 1.11 \| 6000 \| 0.9735 \| 0.6279 \| \| 0.9851 \| 1.29 \| 7000 \| 0.9830 \| 0.6258 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "east asian", "indian", "black", "white", "middle eastern", "latino_hispanic", "southeast asian" ]
ahyar002/vit-pneumonia-classification	<!-- 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-pneumonia-classification 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 chest-xray-classification dataset. It achieves the following results on the evaluation set: - Loss: 0.1301 - Accuracy: 0.9561 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4786 \| 1.0 \| 32 \| 0.3081 \| 0.8609 \| \| 0.213 \| 2.0 \| 64 \| 0.1645 \| 0.9399 \| \| 0.1724 \| 3.0 \| 96 \| 0.1419 \| 0.9502 \| \| 0.1438 \| 4.0 \| 128 \| 0.0950 \| 0.9734 \| \| 0.1267 \| 5.0 \| 160 \| 0.1225 \| 0.9579 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "normal", "pneumonia" ]
crangana/trained-age	<!-- 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. --> # trained-age This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 1.1340 - Accuracy: 0.5164 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3347 \| 0.18 \| 1000 \| 1.3819 \| 0.4296 \| \| 1.3071 \| 0.37 \| 2000 \| 1.2799 \| 0.4642 \| \| 1.297 \| 0.55 \| 3000 \| 1.2503 \| 0.4721 \| \| 1.3121 \| 0.74 \| 4000 \| 1.1661 \| 0.4995 \| \| 1.1806 \| 0.92 \| 5000 \| 1.1137 \| 0.5240 \| \| 1.0839 \| 1.11 \| 6000 \| 1.1340 \| 0.5164 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "more than 70" ]
dima806/ball_types_image_detection	Returns ball type given an image. See https://www.kaggle.com/code/dima806/ball-types-image-detection for more details. ``` Classification report: precision recall f1-score support wiffle ball 1.0000 1.0000 1.0000 39 puffballs 1.0000 1.0000 1.0000 38 chrochet ball 1.0000 1.0000 1.0000 39 golf ball 1.0000 1.0000 1.0000 39 pokeman balls 1.0000 1.0000 1.0000 39 water polo ball 1.0000 1.0000 1.0000 39 football 1.0000 1.0000 1.0000 39 marble 1.0000 1.0000 1.0000 38 medicine ball 1.0000 1.0000 1.0000 39 tether ball 1.0000 1.0000 1.0000 38 billiard ball 1.0000 1.0000 1.0000 38 cannon ball 1.0000 1.0000 1.0000 39 crystal ball 1.0000 1.0000 1.0000 38 cricket ball 1.0000 1.0000 1.0000 39 sepak takraw ball 1.0000 1.0000 1.0000 39 tennis ball 1.0000 1.0000 1.0000 39 wrecking ball 1.0000 1.0000 1.0000 38 rubberband ball 1.0000 1.0000 1.0000 39 buckeyballs 1.0000 1.0000 1.0000 39 bowling ball 1.0000 1.0000 1.0000 38 eyeballs 1.0000 1.0000 1.0000 38 meat ball 1.0000 1.0000 1.0000 38 brass 1.0000 1.0000 1.0000 39 screwballs 1.0000 1.0000 1.0000 38 baseball 1.0000 1.0000 1.0000 38 beachballs 1.0000 1.0000 1.0000 39 soccer ball 1.0000 1.0000 1.0000 38 basketball 1.0000 1.0000 1.0000 39 volley ball 1.0000 1.0000 1.0000 39 paint balls 1.0000 1.0000 1.0000 39 accuracy 1.0000 1158 macro avg 1.0000 1.0000 1.0000 1158 weighted avg 1.0000 1.0000 1.0000 1158 ```	[ "wiffle ball", "puffballs", "chrochet ball", "golf ball", "pokeman balls", "water polo ball", "football", "marble", "medicine ball", "tether ball", "billiard ball", "cannon ball", "crystal ball", "cricket ball", "sepak takraw ball", "tennis ball", "wrecking ball", "rubberband ball", "buckeyballs", "bowling ball", "eyeballs", "meat ball", "brass", "screwballs", "baseball", "beachballs", "soccer ball", "basketball", "volley ball", "paint balls" ]
crangana/trained-gender	<!-- 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. --> # trained-gender This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.2437 - Accuracy: 0.8986 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4277 \| 0.18 \| 1000 \| 0.4054 \| 0.8089 \| \| 0.315 \| 0.37 \| 2000 \| 0.3487 \| 0.8318 \| \| 0.3082 \| 0.55 \| 3000 \| 0.3052 \| 0.8633 \| \| 0.3235 \| 0.74 \| 4000 \| 0.2899 \| 0.8684 \| \| 0.2505 \| 0.92 \| 5000 \| 0.2693 \| 0.8785 \| \| 0.2484 \| 1.11 \| 6000 \| 0.2547 \| 0.8889 \| \| 0.1933 \| 1.29 \| 7000 \| 0.2521 \| 0.8901 \| \| 0.1497 \| 1.48 \| 8000 \| 0.2443 \| 0.8929 \| \| 0.326 \| 1.66 \| 9000 \| 0.2406 \| 0.8958 \| \| 0.215 \| 1.84 \| 10000 \| 0.2381 \| 0.9007 \| \| 0.2035 \| 2.03 \| 11000 \| 0.2437 \| 0.8986 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "male", "female" ]
Manixtox/swin-tiny-patch4-window7-224-finetuned-shotclass	<!-- 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-shotclass 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: 0.2648 - Accuracy: 0.9275 ## 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 \| 0.97 \| 8 \| 0.3702 \| 0.8855 \| \| 0.3802 \| 1.94 \| 16 \| 0.2648 \| 0.9275 \| \| 0.2783 \| 2.91 \| 24 \| 0.3685 \| 0.8740 \| \| 0.2403 \| 4.0 \| 33 \| 0.3235 \| 0.8893 \| \| 0.1864 \| 4.85 \| 40 \| 0.3339 \| 0.8740 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "plano_entero", "plano_medio", "primer_plano", "xlongshots" ]
platzi/platzi-vit-model-gabriel-salazar	<!-- 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. --> # platzi-vit-model-gabriel-salazar 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1267 - Accuracy: 0.9774 ## 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: 8 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0535 \| 3.85 \| 500 \| 0.1267 \| 0.9774 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "angular_leaf_spot", "bean_rust", "healthy" ]
dima806/shoe_types_image_detection	Return shoe type given an image. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/VRp8h5fKGtsdi_ydGM2Oj.png) See https://www.kaggle.com/code/dima806/shoe-type-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Clog 0.9748 0.9598 0.9672 1169 Brogue 0.9804 0.9812 0.9808 1170 Sneaker 0.9718 0.9735 0.9727 1170 Boat 0.9642 0.9658 0.9650 1170 Ballet Flat 0.9729 0.9837 0.9783 1169 accuracy 0.9728 5848 macro avg 0.9728 0.9728 0.9728 5848 weighted avg 0.9728 0.9728 0.9728 5848 ```	[ "clog", "brogue", "sneaker", "boat", "ballet flat" ]
flatmoon102/fruits_and_vegetables_image_classification	<!-- 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. --> # fruits_and_vegetables_image_classification 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.3835 - Accuracy: 0.9159 ## 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: 8e-05 - train_batch_size: 32 - eval_batch_size: 8 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 87 \| 1.6751 \| 0.8768 \| \| No log \| 2.0 \| 174 \| 1.0260 \| 0.8957 \| \| No log \| 3.0 \| 261 \| 0.6767 \| 0.8957 \| \| No log \| 4.0 \| 348 \| 0.5445 \| 0.8986 \| \| No log \| 5.0 \| 435 \| 0.4685 \| 0.9072 \| \| 0.8955 \| 6.0 \| 522 \| 0.4328 \| 0.9072 \| \| 0.8955 \| 7.0 \| 609 \| 0.4028 \| 0.9 \| \| 0.8955 \| 8.0 \| 696 \| 0.3958 \| 0.9145 \| \| 0.8955 \| 9.0 \| 783 \| 0.3835 \| 0.9159 \| \| 0.8955 \| 10.0 \| 870 \| 0.3842 \| 0.9145 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "pineapple", "lettuce", "potato", "bell pepper", "pomegranate", "chilli pepper", "eggplant", "mango", "cabbage", "lemon", "capsicum", "spinach", "corn", "watermelon", "apple", "garlic", "sweetcorn", "grapes", "ginger", "sweetpotato", "raddish", "tomato", "paprika", "carrot", "cucumber", "cauliflower", "kiwi", "orange", "banana", "soy beans", "beetroot", "jalepeno", "onion", "peas", "pear", "turnip" ]
ammardaffa/fruit_veg_detection	<!-- 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. --> # fruit_veg_detection 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.6689 - Accuracy: 0.9116 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 87 \| 0.8126 \| 0.8913 \| \| No log \| 2.0 \| 174 \| 0.6689 \| 0.9116 \| \| No log \| 3.0 \| 261 \| 0.5979 \| 0.9087 \| \| No log \| 4.0 \| 348 \| 0.5629 \| 0.9116 \| \| No log \| 5.0 \| 435 \| 0.5583 \| 0.9014 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "spinach", "lettuce", "raddish", "capsicum", "cucumber", "kiwi", "tomato", "jalepeno", "chilli pepper", "mango", "garlic", "paprika", "sweetpotato", "potato", "pear", "ginger", "peas", "pineapple", "corn", "sweetcorn", "bell pepper", "eggplant", "lemon", "banana", "soy beans", "watermelon", "apple", "carrot", "cabbage", "orange", "onion", "beetroot", "pomegranate", "cauliflower", "turnip", "grapes" ]
dima806/face_obstruction_image_detection	Returns face obstruction type given a facial image with about 91% accuracy. See https://www.kaggle.com/code/dima806/face-obstruction-image-detection-vit for more details. ``` Classification report: precision recall f1-score support sunglasses 0.9974 0.9985 0.9980 3422 glasses 0.9896 0.9968 0.9932 3422 other 0.7198 0.7613 0.7400 3422 mask 0.9971 0.9985 0.9978 3422 hand 0.7505 0.7086 0.7290 3422 none 0.9976 0.9860 0.9918 3422 accuracy 0.9083 20532 macro avg 0.9087 0.9083 0.9083 20532 weighted avg 0.9087 0.9083 0.9083 20532 ```	[ "sunglasses", "glasses", "other", "mask", "hand", "none" ]
tejp/fine-tuned	<!-- 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. --> # fine-tuned This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.0068 - Accuracy: 0.2857 - F1: 0.2030 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "boat", "children", "water", "dogs", "fireman", "firetruck", "mountains", "people", "river", "snow", "stairs" ]
Ahmeng/image_classification	<!-- 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. --> # image_classification 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1943 - Accuracy: 0.9469 ## 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 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 52 \| 0.2654 \| 0.9324 \| \| No log \| 2.0 \| 104 \| 0.1310 \| 0.9807 \| \| No log \| 3.0 \| 156 \| 0.1485 \| 0.9662 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
lucascruz/CheXpert-ViT-U-MultiClass	<!-- 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. --> # CheXpert-ViT-U-MultiClass This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown 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: 0.0004 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.1	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14" ]
zguo0525/myshell_nsfw_filter	## Example Usage Here's a step-by-step Python code example to classify an image: ```python from transformers import pipeline import requests from PIL import Image from io import BytesIO # 1. Load the pipeline for image classification pipe = pipeline("image-classification", model="zguo0525/myshell_nsfw_filter") # 2. Load the image into memory (assuming you have the URL for the image) image_url = 'https://img-myshell.net/meinamix/red_hair_girl' response = requests.get(image_url) image = Image.open(BytesIO(response.content)) # 3. Use the pipeline to classify the image results = pipe(image) # 4. Print the results label = results[0]['label'] score = results[0]['score'] print(f"{label}: {score:.4f}") ```	[ "ero", "porn", "safe" ]
qzheng75/swin-tiny-patch4-window7-224-finetuned-plot-images	<!-- 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-plot-images 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: 0.0093 - Accuracy: 0.9960 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0333 \| 1.0 \| 426 \| 0.0261 \| 0.9937 \| \| 0.0223 \| 2.0 \| 852 \| 0.0072 \| 0.9975 \| \| 0.0103 \| 3.0 \| 1278 \| 0.0093 \| 0.9960 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "dot", "horizontal_bar", "line", "scatter", "vertical_bar" ]
Antdochi/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 [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1744 - Accuracy: 0.9428 - F1: 0.9428 - Precision: 0.9428 - Recall: 0.9428 ## 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: 0.01 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 16 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.2883 \| 1.0 \| 99 \| 0.3757 \| 0.8563 \| 0.8563 \| 0.8563 \| 0.8563 \| \| 0.2195 \| 2.0 \| 198 \| 0.2293 \| 0.9178 \| 0.9178 \| 0.9178 \| 0.9178 \| \| 0.1936 \| 3.0 \| 297 \| 0.2120 \| 0.9149 \| 0.9149 \| 0.9149 \| 0.9149 \| \| 0.163 \| 4.0 \| 396 \| 0.1744 \| 0.9428 \| 0.9428 \| 0.9428 \| 0.9428 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
lantian-chen/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: 1.6473 - Accuracy: 0.874 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7147 \| 0.99 \| 62 \| 2.5361 \| 0.804 \| \| 1.8577 \| 2.0 \| 125 \| 1.8141 \| 0.852 \| \| 1.6359 \| 2.98 \| 186 \| 1.6473 \| 0.874 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "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" ]
luminoussg/autotrain-xray-93756145904	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 93756145904 - CO2 Emissions (in grams): 5.1814 ## Validation Metrics - Loss: 0.062 - Accuracy: 0.977 - Precision: 0.983 - Recall: 0.986 - AUC: 0.997 - F1: 0.984	[ "normal", "pneumonia" ]
qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not	<!-- 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-image-is-plot-or-not 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: 0.0000 - 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: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0006 \| 1.0 \| 448 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "is_not_plot", "is_plot" ]
qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not-finetuned-image-is-plot-or-not	<!-- 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-image-is-plot-or-not-finetuned-image-is-plot-or-not This model is a fine-tuned version of [qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not](https://huggingface.co/qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0012 - Accuracy: 0.9997 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0001 \| 1.0 \| 90 \| 0.0009 \| 0.9997 \| \| 0.0014 \| 1.99 \| 180 \| 0.0022 \| 0.9993 \| \| 0.0054 \| 2.99 \| 270 \| 0.0012 \| 0.9997 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "is_not_plot", "is_plot" ]
dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2	<!-- 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-uploads-classifier-v2 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: 0.0745 - Accuracy: 0.9843 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2482 \| 1.0 \| 18 \| 0.4781 \| 0.8824 \| \| 0.3036 \| 2.0 \| 36 \| 0.0936 \| 0.9804 \| \| 0.1687 \| 3.0 \| 54 \| 0.0745 \| 0.9843 \| \| 0.1392 \| 4.0 \| 72 \| 0.0980 \| 0.9725 \| \| 0.14 \| 5.0 \| 90 \| 0.0778 \| 0.9765 \| \| 0.1186 \| 6.0 \| 108 \| 0.0837 \| 0.9725 \| \| 0.1088 \| 7.0 \| 126 \| 0.0645 \| 0.9804 \| \| 0.0789 \| 8.0 \| 144 \| 0.0675 \| 0.9765 \| \| 0.0644 \| 9.0 \| 162 \| 0.0940 \| 0.9686 \| \| 0.0582 \| 10.0 \| 180 \| 0.0879 \| 0.9725 \| \| 0.0591 \| 11.0 \| 198 \| 0.0935 \| 0.9686 \| \| 0.0538 \| 12.0 \| 216 \| 0.0540 \| 0.9804 \| \| 0.0588 \| 13.0 \| 234 \| 0.0725 \| 0.9686 \| \| 0.0538 \| 14.0 \| 252 \| 0.0637 \| 0.9765 \| \| 0.0462 \| 15.0 \| 270 \| 0.0694 \| 0.9725 \| \| 0.0352 \| 16.0 \| 288 \| 0.0771 \| 0.9686 \| \| 0.0536 \| 17.0 \| 306 \| 0.0629 \| 0.9804 \| \| 0.0403 \| 18.0 \| 324 \| 0.0933 \| 0.9686 \| \| 0.0412 \| 19.0 \| 342 \| 0.0848 \| 0.9725 \| \| 0.0305 \| 20.0 \| 360 \| 0.0820 \| 0.9725 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "full-bleed", "illustration", "logo", "photo", "qr-code" ]
tejp/fine-tuned-augmented	<!-- 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. --> # fine-tuned-augmented This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom_dataset_augmented dataset. It achieves the following results on the evaluation set: - Loss: 2.2134 - Accuracy: 0.2333 - F1: 0.0455 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "aeroplane", "car", "people", "river", "ski", "snow", "deer", "dogsledge", "factory", "fireman", "firetruck", "ladder", "lake", "mountain" ]
kenghweetan/clothing_category_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. --> # clothing_category_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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4070 - Accuracy: 0.2103 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.695 \| 1.0 \| 551 \| 4.4070 \| 0.2103 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cpu - Datasets 2.14.5 - Tokenizers 0.13.3	[ "accessories", "apparel set", "eyewear", "flip flops", "fragrance", "free gifts", "gloves", "hair", "headwear", "home furnishing", "innerwear", "jewellery", "bags", "lips", "loungewear and nightwear", "makeup", "mufflers", "nails", "perfumes", "sandal", "saree", "scarves", "shoe accessories", "bath and body", "shoes", "skin", "skin care", "socks", "sports accessories", "sports equipment", "stoles", "ties", "topwear", "umbrellas", "beauty accessories", "wallets", "watches", "water bottle", "wristbands", "belts", "bottomwear", "cufflinks", "dress", "eyes" ]
Frank0930/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 cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0864 - Accuracy: 0.9726 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4809 \| 1.0 \| 351 \| 0.1388 \| 0.9528 \| \| 0.3489 \| 2.0 \| 703 \| 0.0945 \| 0.9692 \| \| 0.3528 \| 2.99 \| 1053 \| 0.0864 \| 0.9726 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
Devarshi/Armature_Defect_Detection_Resin	<!-- 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. --> # Armature_Defect_Detection_Resin 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: 0.4978 - Accuracy: 0.76 - F1: 0.76 - Recall: 0.76 - Precision: 0.76 ## 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 \| F1 \| Recall \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----:\|:------:\|:---------:\| \| No log \| 0.57 \| 1 \| 0.7205 \| 0.44 \| 0.44 \| 0.44 \| 0.44 \| \| No log \| 1.57 \| 2 \| 0.5926 \| 0.6 \| 0.6 \| 0.6 \| 0.6 \| \| No log \| 2.57 \| 3 \| 0.4978 \| 0.76 \| 0.76 \| 0.76 \| 0.76 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1	[ "resin under fill", "good pieces" ]
romitbarua/autotrain-deepfakeface-94074145984	# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 32.1392 ## Validation Metricsg loss: 0.23420386016368866 f1_macro: 0.9410988547155245 f1_micro: 0.941 f1_weighted: 0.9410988547155245 precision_macro: 0.9415975677612235 precision_micro: 0.941 precision_weighted: 0.9415975677612235 recall_macro: 0.941 recall_micro: 0.941 recall_weighted: 0.941 accuracy: 0.941	[ "inpainting", "insight", "text2img", "wiki" ]
EscvNcl/MobileNet-V2-Retinopathy	<!-- 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. --> # MobileNet-V2-Retinopathy This model is a fine-tuned version of [google/mobilenet_v2_1.4_224](https://huggingface.co/google/mobilenet_v2_1.4_224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2044 - Accuracy: 0.9307 ## 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: 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4403 \| 1.0 \| 113 \| 0.5330 \| 0.7079 \| \| 0.5538 \| 2.0 \| 227 \| 0.4312 \| 0.7723 \| \| 0.542 \| 3.0 \| 340 \| 0.5137 \| 0.7426 \| \| 0.4776 \| 4.0 \| 454 \| 0.4656 \| 0.7723 \| \| 0.4244 \| 5.0 \| 567 \| 1.0400 \| 0.5990 \| \| 0.4694 \| 6.0 \| 681 \| 0.5936 \| 0.7228 \| \| 0.4494 \| 7.0 \| 794 \| 0.4667 \| 0.7822 \| \| 0.4647 \| 8.0 \| 908 \| 0.2629 \| 0.8960 \| \| 0.3646 \| 9.0 \| 1021 \| 0.2287 \| 0.8861 \| \| 0.4827 \| 10.0 \| 1135 \| 1.7967 \| 0.5149 \| \| 0.3679 \| 11.0 \| 1248 \| 0.4184 \| 0.8267 \| \| 0.3454 \| 12.0 \| 1362 \| 0.1885 \| 0.9406 \| \| 0.3562 \| 13.0 \| 1475 \| 0.2798 \| 0.9059 \| \| 0.3397 \| 14.0 \| 1589 \| 1.6444 \| 0.5891 \| \| 0.4047 \| 14.93 \| 1695 \| 0.2044 \| 0.9307 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "nrdr", "rdr" ]
chanelcolgate/vit-base-image-classification-yenthienviet	<!-- 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-image-classification-yenthienviet 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 image-classification-yenthienviet dataset. It achieves the following results on the evaluation set: - Loss: 0.2380 - Accuracy: 0.9344 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6118 \| 0.56 \| 100 \| 0.4854 \| 0.8616 \| \| 0.329 \| 1.11 \| 200 \| 0.4473 \| 0.8616 \| \| 0.3002 \| 1.67 \| 300 \| 0.4167 \| 0.8637 \| \| 0.1549 \| 2.22 \| 400 \| 0.2911 \| 0.9178 \| \| 0.1993 \| 2.78 \| 500 \| 0.2934 \| 0.9168 \| \| 0.1071 \| 3.33 \| 600 \| 0.2389 \| 0.9324 \| \| 0.1027 \| 3.89 \| 700 \| 0.2380 \| 0.9344 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "botkhi", "thuytinh", "ocvit", "ban", "contrung", "kimloai", "toc" ]
wasifh/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. --> # 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: 0.8671 - Accuracy: 0.8235 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9738 \| 0.94 \| 8 \| 1.1530 \| 0.5882 \| \| 0.8674 \| 2.0 \| 17 \| 1.0818 \| 0.5882 \| \| 0.708 \| 2.94 \| 25 \| 1.0412 \| 0.5882 \| \| 0.7004 \| 4.0 \| 34 \| 0.9774 \| 0.7647 \| \| 0.5957 \| 4.94 \| 42 \| 1.0344 \| 0.6471 \| \| 0.5273 \| 5.65 \| 48 \| 0.8671 \| 0.8235 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "blistering", "flatspot", "graining", "none" ]
superdinmc/autotrain-orbit-millets-94211146034	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94211146034 - CO2 Emissions (in grams): 0.0110 ## Validation Metrics - Loss: 1.742 - Accuracy: 0.357 - Macro F1: 0.314 - Micro F1: 0.357 - Weighted F1: 0.314 - Macro Precision: 0.321 - Micro Precision: 0.357 - Weighted Precision: 0.321 - Macro Recall: 0.357 - Micro Recall: 0.357 - Weighted Recall: 0.357	[ "bajra", "barri", "jhangora", "jowar", "kangni", "kodra", "ragi" ]
zkdeng/deit-base-patch16-224-finetuned-dangerousSpiders	<!-- 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. --> # deit-base-patch16-224-finetuned-dangerousSpiders This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1957 - eval_accuracy: 0.915 - eval_precision: 0.8899 - eval_recall: 0.9510 - eval_f1: 0.9194 - eval_runtime: 5.3671 - eval_samples_per_second: 37.264 - eval_steps_per_second: 2.422 - step: 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: 2 ### Framework versions - Transformers 4.33.2 - Pytorch 2.2.0.dev20230921 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]
dima806/oxford_flowers_image_detection	Returns flower type given an image. See https://www.kaggle.com/code/dima806/oxford-flowers-image-detection-vit for more details. ``` Classification report: precision recall f1-score support bolero deep blue 1.0000 1.0000 1.0000 94 toad lily 1.0000 1.0000 1.0000 94 bougainvillea 1.0000 1.0000 1.0000 94 blanket flower 1.0000 1.0000 1.0000 93 prince of wales feathers 1.0000 1.0000 1.0000 94 english marigold 1.0000 1.0000 1.0000 93 common dandelion 1.0000 1.0000 1.0000 94 mallow 1.0000 1.0000 1.0000 94 barbeton daisy 1.0000 1.0000 1.0000 94 desert-rose 1.0000 1.0000 1.0000 94 anthurium 1.0000 1.0000 1.0000 94 cyclamen 1.0000 1.0000 1.0000 94 marigold 1.0000 1.0000 1.0000 93 spring crocus 1.0000 1.0000 1.0000 94 petunia 1.0000 1.0000 1.0000 94 foxglove 1.0000 1.0000 1.0000 94 primula 1.0000 1.0000 1.0000 94 cape flower 1.0000 1.0000 1.0000 94 colt's foot 1.0000 1.0000 1.0000 93 osteospermum 1.0000 1.0000 1.0000 93 buttercup 1.0000 1.0000 1.0000 94 balloon flower 1.0000 1.0000 1.0000 94 fire lily 1.0000 1.0000 1.0000 93 bromelia 1.0000 1.0000 1.0000 93 artichoke 1.0000 1.0000 1.0000 93 daffodil 1.0000 1.0000 1.0000 94 pink-yellow dahlia 1.0000 1.0000 1.0000 93 geranium 1.0000 1.0000 1.0000 94 peruvian lily 1.0000 1.0000 1.0000 93 king protea 1.0000 1.0000 1.0000 94 silverbush 1.0000 1.0000 1.0000 94 alpine sea holly 1.0000 1.0000 1.0000 94 hibiscus 1.0000 1.0000 1.0000 93 giant white arum lily 1.0000 1.0000 1.0000 94 canna lily 1.0000 1.0000 1.0000 94 sunflower 1.0000 1.0000 1.0000 94 sweet pea 1.0000 1.0000 1.0000 94 mexican aster 1.0000 1.0000 1.0000 93 californian poppy 1.0000 1.0000 1.0000 94 pincushion flower 1.0000 1.0000 1.0000 93 black-eyed susan 1.0000 1.0000 1.0000 94 blackberry lily 1.0000 1.0000 1.0000 93 gaura 1.0000 1.0000 1.0000 94 love in the mist 1.0000 1.0000 1.0000 93 spear thistle 1.0000 1.0000 1.0000 94 orange dahlia 1.0000 1.0000 1.0000 93 wallflower 1.0000 1.0000 1.0000 93 tiger lily 1.0000 1.0000 1.0000 94 stemless gentian 1.0000 1.0000 1.0000 93 morning glory 1.0000 1.0000 1.0000 93 frangipani 1.0000 1.0000 1.0000 94 lotus lotus 1.0000 1.0000 1.0000 93 red ginger 1.0000 1.0000 1.0000 94 oxeye daisy 1.0000 1.0000 1.0000 94 windflower 1.0000 1.0000 1.0000 93 monkshood 1.0000 1.0000 1.0000 94 bishop of llandaff 1.0000 1.0000 1.0000 93 globe-flower 1.0000 1.0000 1.0000 93 globe thistle 1.0000 1.0000 1.0000 93 poinsettia 1.0000 1.0000 1.0000 94 wild pansy 1.0000 1.0000 1.0000 93 water lily 1.0000 1.0000 1.0000 94 watercress 1.0000 1.0000 1.0000 93 mexican petunia 1.0000 1.0000 1.0000 94 corn poppy 1.0000 1.0000 1.0000 93 bearded iris 1.0000 1.0000 1.0000 93 azalea 1.0000 1.0000 1.0000 93 camellia 1.0000 1.0000 1.0000 94 tree poppy 1.0000 1.0000 1.0000 93 moon orchid 1.0000 1.0000 1.0000 94 magnolia 1.0000 1.0000 1.0000 94 bee balm 1.0000 1.0000 1.0000 94 lenten rose 1.0000 1.0000 1.0000 94 trumpet creeper 1.0000 1.0000 1.0000 94 passion flower 1.0000 1.0000 1.0000 94 yellow iris 1.0000 1.0000 1.0000 93 pelargonium 1.0000 1.0000 1.0000 93 tree mallow 1.0000 1.0000 1.0000 94 thorn apple 1.0000 1.0000 1.0000 94 garden phlox 1.0000 1.0000 1.0000 94 sword lily 1.0000 1.0000 1.0000 94 carnation 1.0000 1.0000 1.0000 94 ruby-lipped cattleya 1.0000 1.0000 1.0000 94 ball moss 1.0000 1.0000 1.0000 94 columbine 1.0000 1.0000 1.0000 93 siam tulip 1.0000 1.0000 1.0000 94 snapdragon 1.0000 1.0000 1.0000 94 cautleya spicata 1.0000 1.0000 1.0000 94 hard-leaved pocket orchid 1.0000 1.0000 1.0000 93 pink primrose 1.0000 1.0000 1.0000 94 gazania 1.0000 1.0000 1.0000 93 hippeastrum 1.0000 1.0000 1.0000 93 fritillary 1.0000 1.0000 1.0000 93 canterbury bells 1.0000 1.0000 1.0000 94 great masterwort 1.0000 1.0000 1.0000 93 sweet william 1.0000 1.0000 1.0000 94 clematis 1.0000 1.0000 1.0000 93 purple coneflower 1.0000 1.0000 1.0000 94 japanese anemone 1.0000 1.0000 1.0000 94 bird of paradise 1.0000 1.0000 1.0000 93 rose 1.0000 1.0000 1.0000 94 grape hyacinth 1.0000 1.0000 1.0000 94 accuracy 1.0000 9548 macro avg 1.0000 1.0000 1.0000 9548 weighted avg 1.0000 1.0000 1.0000 9548 ```	[ "bolero deep blue", "toad lily", "bougainvillea", "blanket flower", "prince of wales feathers", "english marigold", "common dandelion", "mallow", "barbeton daisy", "desert-rose", "anthurium", "cyclamen", "marigold", "spring crocus", "petunia", "foxglove", "primula", "cape flower", "colt's foot", "osteospermum", "buttercup", "balloon flower", "fire lily", "bromelia", "artichoke", "daffodil", "pink-yellow dahlia", "geranium", "peruvian lily", "king protea", "silverbush", "alpine sea holly", "hibiscus", "giant white arum lily", "canna lily", "sunflower", "sweet pea", "mexican aster", "californian poppy", "pincushion flower", "black-eyed susan", "blackberry lily", "gaura", "love in the mist", "spear thistle", "orange dahlia", "wallflower", "tiger lily", "stemless gentian", "morning glory", "frangipani", "lotus lotus", "red ginger", "oxeye daisy", "windflower", "monkshood", "bishop of llandaff", "globe-flower", "globe thistle", "poinsettia", "wild pansy", "water lily", "watercress", "mexican petunia", "corn poppy", "bearded iris", "azalea", "camellia", "tree poppy", "moon orchid", "magnolia", "bee balm", "lenten rose", "trumpet creeper", "passion flower", "yellow iris", "pelargonium", "tree mallow", "thorn apple", "garden phlox", "sword lily", "carnation", "ruby-lipped cattleya", "ball moss", "columbine", "siam tulip", "snapdragon", "cautleya spicata", "hard-leaved pocket orchid", "pink primrose", "gazania", "hippeastrum", "fritillary", "canterbury bells", "great masterwort", "sweet william", "clematis", "purple coneflower", "japanese anemone", "bird of paradise", "rose", "grape hyacinth" ]
nandyc/swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2	<!-- 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_ASL_Isolated_Swin_dataset2 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 ASL_Isolated_Swin_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1269 - Accuracy: 0.9769 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5439 \| 1.09 \| 100 \| 1.4188 \| 0.5538 \| \| 0.8646 \| 2.17 \| 200 \| 0.4542 \| 0.8885 \| \| 0.5485 \| 3.26 \| 300 \| 0.4103 \| 0.8538 \| \| 0.5082 \| 4.35 \| 400 \| 0.2925 \| 0.8962 \| \| 0.5302 \| 5.43 \| 500 \| 0.2471 \| 0.9269 \| \| 0.4072 \| 6.52 \| 600 \| 0.2676 \| 0.9231 \| \| 0.4424 \| 7.61 \| 700 \| 0.4150 \| 0.9038 \| \| 0.3409 \| 8.7 \| 800 \| 0.1922 \| 0.9538 \| \| 0.3046 \| 9.78 \| 900 \| 0.1917 \| 0.9462 \| \| 0.2911 \| 10.87 \| 1000 \| 0.2272 \| 0.9423 \| \| 0.269 \| 11.96 \| 1100 \| 0.0722 \| 0.9692 \| \| 0.3709 \| 13.04 \| 1200 \| 0.1473 \| 0.9654 \| \| 0.3443 \| 14.13 \| 1300 \| 0.1545 \| 0.9615 \| \| 0.187 \| 15.22 \| 1400 \| 0.1060 \| 0.9731 \| \| 0.1879 \| 16.3 \| 1500 \| 0.1124 \| 0.9692 \| \| 0.2183 \| 17.39 \| 1600 \| 0.1377 \| 0.9615 \| \| 0.1478 \| 18.48 \| 1700 \| 0.1269 \| 0.9769 \| \| 0.1944 \| 19.57 \| 1800 \| 0.0909 \| 0.9769 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z" ]
superdinmc/autotrain-orbit-millets-2-94372146064	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94372146064 - CO2 Emissions (in grams): 0.0330 ## Validation Metrics - Loss: 1.710 - Accuracy: 0.327 - Macro F1: 0.262 - Micro F1: 0.327 - Weighted F1: 0.309 - Macro Precision: 0.277 - Micro Precision: 0.327 - Weighted Precision: 0.312 - Macro Recall: 0.270 - Micro Recall: 0.327 - Weighted Recall: 0.327	[ "bajra", "barri", "jhangora", "jowar", "kangni", "kodra", "ragi" ]
EscvNcl/ConvNext-V2-Retinopathy	<!-- 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-V2-Retinopathy This model is a fine-tuned version of [syedmuhammad/ConvNextV2-Diabetec-Retinopathy](https://huggingface.co/syedmuhammad/ConvNextV2-Diabetec-Retinopathy) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0219 - Accuracy: 0.9901 ## 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: 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.125 \| 1.0 \| 113 \| 0.0339 \| 0.9901 \| \| 0.2206 \| 2.0 \| 227 \| 0.0139 \| 0.9901 \| \| 0.1751 \| 3.0 \| 340 \| 0.0114 \| 0.9950 \| \| 0.0599 \| 4.0 \| 454 \| 0.0277 \| 0.9950 \| \| 0.1122 \| 5.0 \| 567 \| 0.0328 \| 0.9950 \| \| 0.093 \| 6.0 \| 681 \| 0.0240 \| 0.9901 \| \| 0.0673 \| 7.0 \| 794 \| 0.0251 \| 0.9950 \| \| 0.0718 \| 8.0 \| 908 \| 0.0458 \| 0.9851 \| \| 0.0632 \| 9.0 \| 1021 \| 0.0477 \| 0.9901 \| \| 0.0263 \| 10.0 \| 1135 \| 0.0399 \| 0.9950 \| \| 0.0304 \| 11.0 \| 1248 \| 0.0295 \| 0.9901 \| \| 0.0892 \| 12.0 \| 1362 \| 0.0330 \| 0.9950 \| \| 0.0227 \| 13.0 \| 1475 \| 0.0287 \| 0.9901 \| \| 0.0253 \| 14.0 \| 1589 \| 0.0262 \| 0.9901 \| \| 0.1242 \| 14.93 \| 1695 \| 0.0219 \| 0.9901 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "nrdr", "rdr" ]
wang1215/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: 1.6235 - Accuracy: 0.892 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7037 \| 0.99 \| 62 \| 2.5304 \| 0.832 \| \| 1.8523 \| 2.0 \| 125 \| 1.8095 \| 0.865 \| \| 1.5914 \| 2.98 \| 186 \| 1.6235 \| 0.892 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "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" ]
andriydovgal/mvp_flowers	<!-- 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. --> # mvp_flowers 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: - Loss: 3.0181 - Accuracy: 0.907 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.8355 \| 0.99 \| 62 \| 3.7493 \| 0.711 \| \| 3.2592 \| 2.0 \| 125 \| 3.1841 \| 0.886 \| \| 2.9952 \| 2.98 \| 186 \| 3.0181 \| 0.907 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "1", "10", "16", "98", "99", "17", "18", "19", "2", "20", "21", "22", "23", "24", "100", "25", "26", "27", "28", "29", "3", "30", "31", "32", "33", "101", "34", "35", "36", "37", "38", "39", "4", "40", "41", "42", "102", "43", "44", "45", "46", "47", "48", "49", "5", "50", "51", "11", "52", "53", "54", "55", "56", "57", "58", "59", "6", "60", "12", "61", "62", "63", "64", "65", "66", "67", "68", "69", "7", "13", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "14", "8", "80", "81", "82", "83", "84", "85", "86", "87", "88", "15", "89", "9", "90", "91", "92", "93", "94", "95", "96", "97" ]
galbitang/autotrain-sofa_style_classification-94412146080	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94412146080 - CO2 Emissions (in grams): 3.4207 ## Validation Metrics - Loss: 0.863 - Accuracy: 0.722 - Macro F1: 0.660 - Micro F1: 0.722 - Weighted F1: 0.711 - Macro Precision: 0.720 - Micro Precision: 0.722 - Weighted Precision: 0.735 - Macro Recall: 0.667 - Micro Recall: 0.722 - Weighted Recall: 0.722	[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]
galbitang/autotrain-bed_frame_style_classification-94482146114	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94482146114 - CO2 Emissions (in grams): 0.0920 ## Validation Metrics - Loss: 0.544 - Accuracy: 0.824 - Macro F1: 0.820 - Micro F1: 0.824 - Weighted F1: 0.822 - Macro Precision: 0.829 - Micro Precision: 0.824 - Weighted Precision: 0.825 - Macro Recall: 0.816 - Micro Recall: 0.824 - Weighted Recall: 0.824	[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]
galbitang/autotrain-chair_style_classification-94502146123	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94502146123 - CO2 Emissions (in grams): 0.0613 ## Validation Metrics - Loss: 0.736 - Accuracy: 0.760 - Macro F1: 0.688 - Micro F1: 0.760 - Weighted F1: 0.753 - Macro Precision: 0.764 - Micro Precision: 0.760 - Weighted Precision: 0.766 - Macro Recall: 0.671 - Micro Recall: 0.760 - Weighted Recall: 0.760	[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]
hongerzh/my_NFT_sale_classifier	<!-- 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_NFT_sale_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: - Loss: 0.6323 - Accuracy: 0.6560 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6234 \| 1.0 \| 112 \| 0.6335 \| 0.6565 \| \| 0.6077 \| 2.0 \| 225 \| 0.6335 \| 0.6583 \| \| 0.5896 \| 2.99 \| 336 \| 0.6323 \| 0.6560 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.14.0	[ "notsale", "sale" ]
Tushar86/yolo-testing	# Usage ``` from transformers import pipeline p = pipeline("image-classification", model="juliensimon/autotrain-food101-1471154053") result = p("my_image.jpg") ``` # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1471154053 - CO2 Emissions (in grams): 179.1154 ## Validation Metrics - Loss: 0.301 - Accuracy: 0.915 - Macro F1: 0.915 - Micro F1: 0.915 - Weighted F1: 0.915 - Macro Precision: 0.917 - Micro Precision: 0.915 - Weighted Precision: 0.917 - Macro Recall: 0.915 - Micro Recall: 0.915 - Weighted Recall: 0.915	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "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" ]
galbitang/autotrain-table_style_classification2-94510146124	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94510146124 - CO2 Emissions (in grams): 0.0786 ## Validation Metrics - Loss: 0.806 - Accuracy: 0.766 - Macro F1: 0.683 - Micro F1: 0.766 - Weighted F1: 0.750 - Macro Precision: 0.710 - Micro Precision: 0.766 - Weighted Precision: 0.744 - Macro Recall: 0.676 - Micro Recall: 0.766 - Weighted Recall: 0.766	[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]
fengdavid/my_awesome_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_model This model is a fine-tuned version of [distbert](https://huggingface.co/distbert) on the blala dataset. It achieves the following results on the evaluation set: - Loss: 0.1908 - Accuracy: 0.929 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 391 \| 0.1959 \| 0.9243 \| \| 0.2489 \| 2.0 \| 782 \| 0.1908 \| 0.929 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "negative", "positive" ]
lucascruz/CheXpert-ViT-U-SelfTrained	<!-- 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. --> # CheXpert-ViT-U-SelfTrained This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown 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: 0.0004 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.1	[ "label_0", "label_1", "label_2", "label_3", "label_4" ]
hyeongjin99/resnet_50_base_aihub_model_py	<!-- 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_base_aihub_model_py 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: 0.0987 - Accuracy: 0.9681 - Precision: 0.9712 - Recall: 0.9624 - F1: 0.9667 ## 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: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - 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 \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5577 \| 1.0 \| 149 \| 0.4027 \| 0.8453 \| 0.8514 \| 0.8415 \| 0.8435 \| \| 0.323 \| 2.0 \| 299 \| 0.2346 \| 0.9097 \| 0.9208 \| 0.8962 \| 0.9074 \| \| 0.2467 \| 3.0 \| 448 \| 0.1786 \| 0.9303 \| 0.9465 \| 0.9216 \| 0.9326 \| \| 0.1953 \| 4.0 \| 598 \| 0.1266 \| 0.9573 \| 0.9591 \| 0.9483 \| 0.9535 \| \| 0.1456 \| 4.98 \| 745 \| 0.0987 \| 0.9681 \| 0.9712 \| 0.9624 \| 0.9667 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3	[ "cloudy", "normal", "rainy", "snowy" ]
ombhojane/healthyPlantsModel	<!-- 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. --> # healthy-plant-disease-identification This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the [Kaggle version](https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset) of the [Plant Village dataset](https://github.com/spMohanty/PlantVillage-Dataset). It achieves the following results on the evaluation set: - Cross Entropy Loss: 0.15 - Accuracy: 0.9541 ## Intended uses & limitations For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts. ## Training and evaluation data The plant village dataset consists of 38 classes of diseases in common crops (including healthy/normal crops). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 256 - eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 6 ### Framework versions - Transformers 4.27.3 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2	[ "apple scab", "apple with black rot", "cedar apple rust", "healthy apple", "healthy blueberry plant", "cherry with powdery mildew", "healthy cherry plant", "corn (maize) with cercospora and gray leaf spot", "corn (maize) with common rust", "corn (maize) with northern leaf blight", "healthy corn (maize) plant", "grape with black rot", "grape with esca (black measles)", "grape with isariopsis leaf spot", "healthy grape plant", "orange with citrus greening", "peach with bacterial spot", "healthy peach plant", "bell pepper with bacterial spot", "healthy bell pepper plant", "potato with early blight", "potato with late blight", "healthy potato plant", "healthy raspberry plant", "healthy soybean plant", "squash with powdery mildew", "strawberry with leaf scorch", "healthy strawberry plant", "tomato with bacterial spot", "tomato with early blight", "tomato with late blight", "tomato with leaf mold", "tomato with septoria leaf spot", "tomato with spider mites or two-spotted spider mite", "tomato with target spot", "tomato yellow leaf curl virus", "tomato mosaic virus", "healthy tomato plant" ]
merve/beans-vit-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. --> # beans-vit-224 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.3256 - Accuracy: 0.9375 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0032 \| 0.98 \| 16 \| 0.6540 \| 0.8828 \| \| 0.4711 \| 1.97 \| 32 \| 0.4180 \| 0.9297 \| \| 0.3711 \| 2.95 \| 48 \| 0.3256 \| 0.9375 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "angular_leaf_spot", "bean_rust", "healthy" ]
platzi/platzi-vit-model_JPLC	<!-- 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. --> # platzi-vit-model_JPLC 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0400 - Accuracy: 0.9850 ## 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: 8 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1287 \| 3.85 \| 500 \| 0.0400 \| 0.9850 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "angular_leaf_spot", "bean_rust", "healthy" ]
dima806/surface_crack_image_detection	Check whether there is a surface crack given surface image. See https://www.kaggle.com/code/dima806/surface-crack-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Positive 0.9988 0.9995 0.9991 4000 Negative 0.9995 0.9988 0.9991 4000 accuracy 0.9991 8000 macro avg 0.9991 0.9991 0.9991 8000 weighted avg 0.9991 0.9991 0.9991 8000 ```	[ "positive", "negative" ]
romitbarua/autotrain-deepfakeface_only_faces-94737146192	# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 33.9961 ## Validation Metricsg loss: 0.479949951171875 f1_macro: 0.813704872275267 f1_micro: 0.8058823529411765 f1_weighted: 0.8066289996092723 precision_macro: 0.8195022394186146 precision_micro: 0.8058823529411765 precision_weighted: 0.808552105878264 recall_macro: 0.8091827901264657 recall_micro: 0.8058823529411765 recall_weighted: 0.8058823529411765 accuracy: 0.8058823529411765	[ "inpainting", "insight", "real", "text2img" ]
sweetzinc/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: 0.1113 - Accuracy: 0.9641 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1937 \| 1.0 \| 190 \| 0.1113 \| 0.9641 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cpu - Datasets 2.14.5 - Tokenizers 0.14.1	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
dima806/buscuit_wrappers_image_detection	Returns biscuit wrapper type based on an image with about 93% accuracy. See https://www.kaggle.com/code/dima806/biscuit-wrappers-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Americana Coconut Cookies 0.9677 0.9677 0.9677 31 Amul Chocolate Cookies 0.9688 1.0000 0.9841 31 Amul Elaichi Rusk 0.9143 1.0000 0.9552 32 Bhagwati Choco Vanilla Puff Biscuits 1.0000 1.0000 1.0000 32 Bhagwati Lemony Puff Biscuits 1.0000 1.0000 1.0000 31 Bisk Farm Sugar Free Biscuits 0.9688 1.0000 0.9841 31 Bonn Jeera Bite Biscuits 1.0000 1.0000 1.0000 31 Britannia 50-50 Maska Chaska 0.8750 0.4516 0.5957 31 Britannia 50-50 Maska Chaska Salted Biscuits 0.5111 0.7419 0.6053 31 Britannia 50-50 Potazos - Masti Masala 1.0000 1.0000 1.0000 31 Britannia 50-50 Sweet and Salty Biscuits 1.0000 0.8387 0.9123 31 Britannia 50-50 Timepass Classic Salted Biscuit 1.0000 1.0000 1.0000 31 Britannia Biscafe Coffee Cracker 0.8333 0.6452 0.7273 31 Britannia Bourbon 1.0000 0.8710 0.9310 31 Britannia Bourbon The Original Cream Biscuits 0.8889 1.0000 0.9412 32 Britannia Chocolush - Pure Magic 0.7045 1.0000 0.8267 31 Britannia Good Day - Chocochip Cookies 1.0000 0.9677 0.9836 31 Britannia Good Day Cashew Almond Cookies 0.6944 0.8065 0.7463 31 Britannia Good Day Harmony Biscuit 1.0000 0.7812 0.8772 32 Britannia Good Day Pista Badam Cookies 0.8378 1.0000 0.9118 31 Britannia Little Hearts 0.9688 1.0000 0.9841 31 Britannia Marie Gold Biscuit 1.0000 0.9688 0.9841 32 Britannia Milk Bikis Milk Biscuits 0.7381 1.0000 0.8493 31 Britannia Nice Time - Coconut Biscuits 0.8889 1.0000 0.9412 32 Britannia Nutri Choice Oats Cookies - Chocolate and Almonds 0.7500 0.8710 0.8060 31 Britannia Nutri Choice Oats Cookies - Orange With Almonds 1.0000 0.7097 0.8302 31 Britannia Nutri Choice Seed Biscuits 1.0000 0.9032 0.9492 31 Britannia Nutri Choice Sugar Free Cream Cracker Biscuits 1.0000 1.0000 1.0000 31 Britannia Nutrichoice Herbs Biscuits 1.0000 1.0000 1.0000 31 Britannia Tiger Glucose Biscuit 0.9667 0.9355 0.9508 31 Britannia Tiger Kreemz - Chocolate Cream Biscuits 0.9091 0.9375 0.9231 32 Britannia Tiger Kreemz - Elaichi Cream Biscuits 0.9688 1.0000 0.9841 31 Britannia Tiger Kreemz - Orange Cream Biscuits 0.8889 0.7742 0.8276 31 Britannia Tiger Krunch Chocochips Biscuit 0.8710 0.8710 0.8710 31 Britannia Treat Chocolate Cream Biscuits 1.0000 0.9032 0.9492 31 Britannia Treat Crazy Pineapple Cream Biscuit 0.9697 1.0000 0.9846 32 Britannia Treat Jim Jam Cream Biscuit 1.0000 1.0000 1.0000 31 Britannia Treat Osom Orange Cream Biscuit 0.9667 0.9355 0.9508 31 Britannia Vita Marie Gold Biscuits 1.0000 1.0000 1.0000 31 Cadbury Bournvita Biscuits 0.9667 0.9062 0.9355 32 Cadbury Chocobakes Choc Filled Cookies 1.0000 1.0000 1.0000 32 Cadbury Oreo Chocolate Flavour Biscuit Cream Sandwich 1.0000 0.8065 0.8929 31 Cadbury Oreo Strawberry Flavour Creme Sandwich Biscuit 1.0000 0.9677 0.9836 31 Canberra Big Orange Cream Biscuits 1.0000 0.8125 0.8966 32 CookieMan Hand Pound Chocolate Cookies 0.9394 1.0000 0.9688 31 Cremica Coconut Cookies 1.0000 1.0000 1.0000 31 Cremica Elaichi Sandwich Biscuits 1.0000 1.0000 1.0000 31 Cremica Jeera Lite 1.0000 0.9677 0.9836 31 Cremica Non-Stop Thin Potato Crackers - Baked, Crunchy Masala 1.0000 0.9355 0.9667 31 Cremica Orange Sandwich Biscuits 1.0000 0.8710 0.9310 31 Krown Black Magic Cream Biscuits 0.9655 0.9032 0.9333 31 MARIO Coconut Crunchy Biscuits 0.8378 1.0000 0.9118 31 McVities Bourbon Cream Biscuits 0.9688 0.9688 0.9688 32 McVities Dark Cookie Cream 1.0000 0.8065 0.8929 31 McVities Marie Biscuit 0.8710 0.8710 0.8710 31 Parle 20-20 Cashew Cookies 1.0000 1.0000 1.0000 32 Parle 20-20 Nice Biscuits 1.0000 1.0000 1.0000 32 Parle Happy Happy Choco-Chip Cookies 0.9394 1.0000 0.9688 31 Parle Hide and Seek 0.9333 0.9032 0.9180 31 Parle Hide and Seek - Black Bourbon Choco 0.9032 0.9032 0.9032 31 Parle Hide and Seek - Milano Choco Chip Cookies 1.0000 0.9677 0.9836 31 Parle Hide and Seek Caffe Mocha Cookies 0.9565 0.7097 0.8148 31 Parle Hide and Seek Chocolate and Almonds 0.9655 0.8750 0.9180 32 Parle Krack Jack Original Sweet and Salty Cracker Biscuit 0.9333 0.9032 0.9180 31 Parle Krackjack Biscuits 0.9643 0.8710 0.9153 31 Parle Magix Sandwich Biscuits - Chocolate 0.9375 0.9677 0.9524 31 Parle Milk Shakti Biscuits 0.9091 0.9677 0.9375 31 Parle Monaco Biscuit - Classic Regular 1.0000 0.9688 0.9841 32 Parle Monaco Piri Piri 1.0000 0.9062 0.9508 32 Parle Platina Hide and Seek Creme Sandwich - Vanilla 0.9412 1.0000 0.9697 32 Parle-G Gold Gluco Biscuits 0.9677 0.9677 0.9677 31 Parle-G Original Gluco Biscuits 1.0000 0.9677 0.9836 31 Patanjali Doodh Biscuit 1.0000 0.9688 0.9841 32 Priyagold Butter Delite Biscuits 1.0000 1.0000 1.0000 31 Priyagold CNC Biscuits 1.0000 0.8065 0.8929 31 Priyagold Cheese Chacker Biscuits 0.9333 0.9032 0.9180 31 Priyagold Snacks Zig Zag Biscuits 0.9688 1.0000 0.9841 31 Richlite Rich Butter Cookies 0.9688 1.0000 0.9841 31 RiteBite Max Protein 7 Grain Breakfast Cookies - Cashew Delite 1.0000 1.0000 1.0000 31 Sagar Coconut Munch Biscuits 1.0000 1.0000 1.0000 31 Sri Sri Tattva Cashew Nut Cookies 1.0000 1.0000 1.0000 31 Sri Sri Tattva Choco Hazelnut Cookies 0.8056 0.9355 0.8657 31 Sri Sri Tattva Coconut Cookies 0.8378 1.0000 0.9118 31 Sri Sri Tattva Digestive Cookies 1.0000 0.8710 0.9310 31 Sunfeast All Rounder - Cream and Herb 1.0000 0.9355 0.9667 31 Sunfeast All Rounder - Thin, Light and Crunchy Potato Biscuit With Chatpata Masala Flavour 1.0000 0.8387 0.9123 31 Sunfeast Bounce Creme Biscuits 0.9259 0.8065 0.8621 31 Sunfeast Bounce Creme Biscuits - Elaichi 0.7949 1.0000 0.8857 31 Sunfeast Bounce Creme Biscuits - Pineapple Zing 0.7949 1.0000 0.8857 31 Sunfeast Dark Fantasy - Choco Creme 0.7949 1.0000 0.8857 31 Sunfeast Dark Fantasy Bourbon Biscuits 0.6889 1.0000 0.8158 31 Sunfeast Dark Fantasy Choco Fills 1.0000 0.8065 0.8929 31 Sunfeast Glucose Biscuits 0.9310 0.8710 0.9000 31 Sunfeast Moms Magic - Fruit and Milk Cookies 0.8158 1.0000 0.8986 31 Sunfeast Moms Magic - Rich Butter Cookies 1.0000 0.9677 0.9836 31 Sunfeast Moms Magic - Rich Cashew and Almond Cookies 1.0000 0.9062 0.9508 32 Tasties Chocochip Cookies 1.0000 1.0000 1.0000 31 Tasties Coconut Cookies 1.0000 0.8750 0.9333 32 UNIBIC Choco Chip Cookies 0.8333 0.9677 0.8955 31 UNIBIC Pista Badam Cookies 0.8857 1.0000 0.9394 31 UNIBIC Snappers Potato Crackers 0.9667 0.9355 0.9508 31 accuracy 0.9305 3152 macro avg 0.9396 0.9304 0.9306 3152 weighted avg 0.9398 0.9305 0.9307 3152 ```	[ "americana coconut cookies", "amul chocolate cookies", "amul elaichi rusk", "bhagwati choco vanilla puff biscuits", "bhagwati lemony puff biscuits", "bisk farm sugar free biscuits", "bonn jeera bite biscuits", "britannia 50-50 maska chaska", "britannia 50-50 maska chaska salted biscuits", "britannia 50-50 potazos - masti masala", "britannia 50-50 sweet and salty biscuits", "britannia 50-50 timepass classic salted biscuit", "britannia biscafe coffee cracker", "britannia bourbon", "britannia bourbon the original cream biscuits", "britannia chocolush - pure magic", "britannia good day - chocochip cookies", "britannia good day cashew almond cookies", "britannia good day harmony biscuit", "britannia good day pista badam cookies", "britannia little hearts", "britannia marie gold biscuit", "britannia milk bikis milk biscuits", "britannia nice time - coconut biscuits", "britannia nutri choice oats cookies - chocolate and almonds", "britannia nutri choice oats cookies - orange with almonds", "britannia nutri choice seed biscuits", "britannia nutri choice sugar free cream cracker biscuits", "britannia nutrichoice herbs biscuits", "britannia tiger glucose biscuit", "britannia tiger kreemz - chocolate cream biscuits", "britannia tiger kreemz - elaichi cream biscuits", "britannia tiger kreemz - orange cream biscuits", "britannia tiger krunch chocochips biscuit", "britannia treat chocolate cream biscuits", "britannia treat crazy pineapple cream biscuit", "britannia treat jim jam cream biscuit", "britannia treat osom orange cream biscuit", "britannia vita marie gold biscuits", "cadbury bournvita biscuits", "cadbury chocobakes choc filled cookies", "cadbury oreo chocolate flavour biscuit cream sandwich", "cadbury oreo strawberry flavour creme sandwich biscuit", "canberra big orange cream biscuits", "cookieman hand pound chocolate cookies", "cremica coconut cookies", "cremica elaichi sandwich biscuits", "cremica jeera lite", "cremica non-stop thin potato crackers - baked, crunchy masala", "cremica orange sandwich biscuits", "krown black magic cream biscuits", "mario coconut crunchy biscuits", "mcvities bourbon cream biscuits", "mcvities dark cookie cream", "mcvities marie biscuit", "parle 20-20 cashew cookies", "parle 20-20 nice biscuits", "parle happy happy choco-chip cookies", "parle hide and seek", "parle hide and seek - black bourbon choco", "parle hide and seek - milano choco chip cookies", "parle hide and seek caffe mocha cookies", "parle hide and seek chocolate and almonds", "parle krack jack original sweet and salty cracker biscuit", "parle krackjack biscuits", "parle magix sandwich biscuits - chocolate", "parle milk shakti biscuits", "parle monaco biscuit - classic regular", "parle monaco piri piri", "parle platina hide and seek creme sandwich - vanilla", "parle-g gold gluco biscuits", "parle-g original gluco biscuits", "patanjali doodh biscuit", "priyagold butter delite biscuits", "priyagold cnc biscuits", "priyagold cheese chacker biscuits", "priyagold snacks zig zag biscuits", "richlite rich butter cookies", "ritebite max protein 7 grain breakfast cookies - cashew delite", "sagar coconut munch biscuits", "sri sri tattva cashew nut cookies", "sri sri tattva choco hazelnut cookies", "sri sri tattva coconut cookies", "sri sri tattva digestive cookies", "sunfeast all rounder - cream and herb", "sunfeast all rounder - thin, light and crunchy potato biscuit with chatpata masala flavour", "sunfeast bounce creme biscuits", "sunfeast bounce creme biscuits - elaichi", "sunfeast bounce creme biscuits - pineapple zing", "sunfeast dark fantasy - choco creme", "sunfeast dark fantasy bourbon biscuits", "sunfeast dark fantasy choco fills", "sunfeast glucose biscuits", "sunfeast moms magic - fruit and milk cookies", "sunfeast moms magic - rich butter cookies", "sunfeast moms magic - rich cashew and almond cookies", "tasties chocochip cookies", "tasties coconut cookies", "unibic choco chip cookies", "unibic pista badam cookies", "unibic snappers potato crackers" ]
ericrong888/logo_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. --> # ericrong888/logo_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: 0.7196 - Validation Loss: 0.8069 - Train Accuracy: 1.0 - 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': 75, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.1054 \| 1.0410 \| 0.8333 \| 0 \| \| 0.9869 \| 0.9692 \| 0.8333 \| 1 \| \| 0.8856 \| 0.9035 \| 1.0 \| 2 \| \| 0.8117 \| 0.8585 \| 1.0 \| 3 \| \| 0.7196 \| 0.8069 \| 1.0 \| 4 \| ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "amazon", "starbucks", "wellsfargo" ]
wangyk22/histoSwin-base	<!-- 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. --> # histoSwin-base This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the nctcrche100_k dataset. It achieves the following results on the evaluation set: - Loss: 0.1260 - Accuracy: 0.9709 ## Model description More information needed ## Intended uses & limitations For TMA classification. ## 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: 2 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0386 \| 1.0 \| 1562 \| 0.1740 \| 0.9592 \| \| 0.05 \| 2.0 \| 3125 \| 0.2313 \| 0.9524 \| \| 0.0324 \| 3.0 \| 4687 \| 0.2606 \| 0.9604 \| \| 0.005 \| 4.0 \| 6250 \| 0.1724 \| 0.9660 \| \| 0.0037 \| 5.0 \| 7810 \| 0.1260 \| 0.9709 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3	[ "str", "mus", "adi", "tum", "deb", "lym", "back", "muc", "norm" ]
dima806/beard_face_image_detection	Predicts the presence of a beard given a facial image. See https://www.kaggle.com/code/dima806/beard-face-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Beard 1.0000 1.0000 1.0000 34 No Beard 1.0000 1.0000 1.0000 34 accuracy 1.0000 68 macro avg 1.0000 1.0000 1.0000 68 weighted avg 1.0000 1.0000 1.0000 68 ```	[ "beard", "no beard" ]
dima806/food_beverages_japan_image_detection	Returns whether the Japanese food or beverage is in an image with about 89% accuracy. See https://www.kaggle.com/code/dima806/food-beverages-japan-image-detection-vit for more details. ``` Classification report: precision recall f1-score support food 0.8898 0.8879 0.8889 473 beverage 0.8882 0.8901 0.8891 473 accuracy 0.8890 946 macro avg 0.8890 0.8890 0.8890 946 weighted avg 0.8890 0.8890 0.8890 946 ```	[ "food", "beverage" ]
dima806/tyre_quality_image_detection	Retuns tyre quality given a tyre image with about 99.3% accuracy. See https://www.kaggle.com/code/dima806/tyre-quality-image-detection-vit for more details. ``` Classification report: precision recall f1-score support defective 1.0000 0.9854 0.9926 411 good 0.9856 1.0000 0.9928 412 accuracy 0.9927 823 macro avg 0.9928 0.9927 0.9927 823 weighted avg 0.9928 0.9927 0.9927 823 ```	[ "defective", "good" ]
dima806/full_flat_tyre_image_detection	Check whether the tyre is flat given an image. See https://www.kaggle.com/code/dima806/full-flat-tyre-image-detection-vit for more details. ``` Classification report: precision recall f1-score support flat 1.0000 1.0000 1.0000 60 no-tire 1.0000 1.0000 1.0000 60 full 1.0000 1.0000 1.0000 60 accuracy 1.0000 180 macro avg 1.0000 1.0000 1.0000 180 weighted avg 1.0000 1.0000 1.0000 180 ```	[ "flat", "no-tire", "full" ]
romitbarua/autotrain-deepfakeface_only_faces_insightface-94902146221	# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 0.3674 ## Validation Metricsg loss: 0.39755979180336 f1: 0.8185757948998676 precision: 0.8184637839810254 recall: 0.8186878364814308 auc: 0.9113633194857089 accuracy: 0.8150662636596141	[ "insight", "real" ]
galbitang/autotrain-jeongmi_lamp-94917146228	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94917146228 - CO2 Emissions (in grams): 0.0453 ## Validation Metrics - Loss: 1.114 - Accuracy: 0.642 - Macro F1: 0.465 - Micro F1: 0.642 - Weighted F1: 0.612 - Macro Precision: 0.482 - Micro Precision: 0.642 - Weighted Precision: 0.595 - Macro Recall: 0.468 - Micro Recall: 0.642 - Weighted Recall: 0.642	[ "classicantique", "frenchprovence", "vintageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]
galbitang/autotrain-jin0_table-94921146229	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94921146229 - CO2 Emissions (in grams): 0.1008 ## Validation Metrics - Loss: 0.892 - Accuracy: 0.727 - Macro F1: 0.672 - Micro F1: 0.727 - Weighted F1: 0.715 - Macro Precision: 0.682 - Micro Precision: 0.727 - Weighted Precision: 0.709 - Macro Recall: 0.675 - Micro Recall: 0.727 - Weighted Recall: 0.727	[ "classicantique", "frenchprovence", "vintageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]
galbitang/autotrain-jeongmi_chair-94919146230	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94919146230 - CO2 Emissions (in grams): 0.0582 ## Validation Metrics - Loss: 0.714 - Accuracy: 0.735 - Macro F1: 0.622 - Micro F1: 0.735 - Weighted F1: 0.731 - Macro Precision: 0.678 - Micro Precision: 0.735 - Weighted Precision: 0.745 - Macro Recall: 0.597 - Micro Recall: 0.735 - Weighted Recall: 0.735	[ "classsicantique", "frenchprovence", "vintatageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]

bdpc/resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5

# resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6065 - Accuracy: 0.7915 - Brier Loss: 0.3054 - Nll: 1.9957 - F1 Micro: 0.7915 - F1 Macro: 0.7910 - Ece: 0.0453 - Aurc: 0.0607 ## 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: 64 - eval_batch_size: 64 - 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 | 250 | 4.1565 | 0.1378 | 0.9318 | 7.9039 | 0.1378 | 0.1073 | 0.0673 | 0.8326 | | 4.1485 | 2.0 | 500 | 3.6932 | 0.3235 | 0.8832 | 5.1525 | 0.3235 | 0.2725 | 0.2044 | 0.5507 | | 4.1485 | 3.0 | 750 | 2.3374 | 0.4725 | 0.6611 | 3.3127 | 0.4725 | 0.4311 | 0.0839 | 0.2921 | | 2.392 | 4.0 | 1000 | 1.6516 | 0.588 | 0.5470 | 2.8681 | 0.588 | 0.5789 | 0.0620 | 0.1929 | | 2.392 | 5.0 | 1250 | 1.3260 | 0.6488 | 0.4782 | 2.6378 | 0.6488 | 0.6444 | 0.0486 | 0.1458 | | 1.1422 | 6.0 | 1500 | 1.0390 | 0.702 | 0.4156 | 2.4086 | 0.702 | 0.7029 | 0.0576 | 0.1097 | | 1.1422 | 7.0 | 1750 | 0.8420 | 0.7288 | 0.3738 | 2.2222 | 0.7288 | 0.7300 | 0.0553 | 0.0888 | | 0.708 | 8.0 | 2000 | 0.7753 | 0.7398 | 0.3586 | 2.1518 | 0.7398 | 0.7396 | 0.0587 | 0.0826 | | 0.708 | 9.0 | 2250 | 0.7797 | 0.7462 | 0.3580 | 2.1095 | 0.7462 | 0.7457 | 0.0581 | 0.0820 | | 0.5195 | 10.0 | 2500 | 0.7101 | 0.7602 | 0.3404 | 2.0711 | 0.7602 | 0.7612 | 0.0473 | 0.0733 | | 0.5195 | 11.0 | 2750 | 0.6971 | 0.7645 | 0.3338 | 2.0649 | 0.7645 | 0.7653 | 0.0541 | 0.0715 | | 0.4176 | 12.0 | 3000 | 0.6936 | 0.7712 | 0.3302 | 2.0265 | 0.7712 | 0.7708 | 0.0515 | 0.0702 | | 0.4176 | 13.0 | 3250 | 0.6991 | 0.7662 | 0.3346 | 2.0582 | 0.7663 | 0.7657 | 0.0581 | 0.0723 | | 0.3573 | 14.0 | 3500 | 0.6672 | 0.7722 | 0.3246 | 2.0053 | 0.7722 | 0.7723 | 0.0551 | 0.0683 | | 0.3573 | 15.0 | 3750 | 0.6735 | 0.777 | 0.3244 | 2.0387 | 0.777 | 0.7782 | 0.0488 | 0.0671 | | 0.3193 | 16.0 | 4000 | 0.6567 | 0.776 | 0.3216 | 2.0256 | 0.776 | 0.7773 | 0.0499 | 0.0678 | | 0.3193 | 17.0 | 4250 | 0.6498 | 0.78 | 0.3184 | 1.9865 | 0.78 | 0.7802 | 0.0477 | 0.0662 | | 0.2893 | 18.0 | 4500 | 0.6763 | 0.7755 | 0.3264 | 2.0844 | 0.7755 | 0.7755 | 0.0531 | 0.0697 | | 0.2893 | 19.0 | 4750 | 0.6519 | 0.7815 | 0.3183 | 2.0458 | 0.7815 | 0.7817 | 0.0513 | 0.0658 | | 0.271 | 20.0 | 5000 | 0.6432 | 0.7823 | 0.3147 | 2.0291 | 0.7823 | 0.7827 | 0.0440 | 0.0645 | | 0.271 | 21.0 | 5250 | 0.6456 | 0.781 | 0.3156 | 2.0493 | 0.7810 | 0.7813 | 0.0487 | 0.0652 | | 0.2516 | 22.0 | 5500 | 0.6336 | 0.7823 | 0.3144 | 1.9829 | 0.7823 | 0.7822 | 0.0522 | 0.0642 | | 0.2516 | 23.0 | 5750 | 0.6333 | 0.7837 | 0.3128 | 2.0196 | 0.7837 | 0.7836 | 0.0492 | 0.0641 | | 0.2397 | 24.0 | 6000 | 0.6337 | 0.7817 | 0.3147 | 2.0180 | 0.7817 | 0.7815 | 0.0494 | 0.0644 | | 0.2397 | 25.0 | 6250 | 0.6347 | 0.7857 | 0.3145 | 2.0187 | 0.7857 | 0.7856 | 0.0510 | 0.0641 | | 0.23 | 26.0 | 6500 | 0.6311 | 0.7815 | 0.3129 | 2.0132 | 0.7815 | 0.7819 | 0.0495 | 0.0637 | | 0.23 | 27.0 | 6750 | 0.6329 | 0.7853 | 0.3125 | 2.0708 | 0.7853 | 0.7852 | 0.0502 | 0.0635 | | 0.2191 | 28.0 | 7000 | 0.6222 | 0.786 | 0.3109 | 2.0022 | 0.786 | 0.7856 | 0.0483 | 0.0638 | | 0.2191 | 29.0 | 7250 | 0.6195 | 0.7863 | 0.3096 | 2.0028 | 0.7863 | 0.7859 | 0.0550 | 0.0620 | | 0.2155 | 30.0 | 7500 | 0.6196 | 0.7883 | 0.3090 | 1.9972 | 0.7883 | 0.7883 | 0.0486 | 0.0624 | | 0.2155 | 31.0 | 7750 | 0.6167 | 0.787 | 0.3080 | 2.0173 | 0.787 | 0.7871 | 0.0443 | 0.0623 | | 0.2074 | 32.0 | 8000 | 0.6143 | 0.7897 | 0.3073 | 2.0223 | 0.7897 | 0.7893 | 0.0443 | 0.0614 | | 0.2074 | 33.0 | 8250 | 0.6123 | 0.787 | 0.3078 | 1.9869 | 0.787 | 0.7866 | 0.0458 | 0.0619 | | 0.2028 | 34.0 | 8500 | 0.6137 | 0.7873 | 0.3070 | 1.9883 | 0.7873 | 0.7868 | 0.0457 | 0.0623 | | 0.2028 | 35.0 | 8750 | 0.6152 | 0.786 | 0.3085 | 2.0108 | 0.786 | 0.7863 | 0.0497 | 0.0626 | | 0.1982 | 36.0 | 9000 | 0.6133 | 0.7863 | 0.3077 | 2.0205 | 0.7863 | 0.7862 | 0.0515 | 0.0615 | | 0.1982 | 37.0 | 9250 | 0.6145 | 0.7877 | 0.3081 | 1.9930 | 0.7877 | 0.7879 | 0.0444 | 0.0621 | | 0.1948 | 38.0 | 9500 | 0.6116 | 0.7857 | 0.3078 | 2.0072 | 0.7857 | 0.7854 | 0.0508 | 0.0619 | | 0.1948 | 39.0 | 9750 | 0.6090 | 0.788 | 0.3059 | 1.9954 | 0.788 | 0.7882 | 0.0430 | 0.0614 | | 0.1933 | 40.0 | 10000 | 0.6143 | 0.7897 | 0.3072 | 1.9943 | 0.7897 | 0.7899 | 0.0462 | 0.0618 | | 0.1933 | 41.0 | 10250 | 0.6061 | 0.7887 | 0.3041 | 1.9900 | 0.7887 | 0.7889 | 0.0439 | 0.0606 | | 0.1882 | 42.0 | 10500 | 0.6070 | 0.7865 | 0.3058 | 1.9907 | 0.7865 | 0.7868 | 0.0438 | 0.0607 | | 0.1882 | 43.0 | 10750 | 0.6083 | 0.788 | 0.3054 | 2.0095 | 0.788 | 0.7877 | 0.0489 | 0.0608 | | 0.1871 | 44.0 | 11000 | 0.6083 | 0.787 | 0.3054 | 1.9828 | 0.787 | 0.7872 | 0.0469 | 0.0607 | | 0.1871 | 45.0 | 11250 | 0.6092 | 0.7893 | 0.3057 | 2.0140 | 0.7893 | 0.7891 | 0.0483 | 0.0608 | | 0.1862 | 46.0 | 11500 | 0.6057 | 0.7893 | 0.3053 | 2.0064 | 0.7893 | 0.7890 | 0.0450 | 0.0609 | | 0.1862 | 47.0 | 11750 | 0.6042 | 0.79 | 0.3044 | 1.9691 | 0.79 | 0.7899 | 0.0435 | 0.0607 | | 0.1845 | 48.0 | 12000 | 0.6068 | 0.79 | 0.3053 | 2.0052 | 0.79 | 0.7899 | 0.0438 | 0.0608 | | 0.1845 | 49.0 | 12250 | 0.6081 | 0.7893 | 0.3062 | 2.0117 | 0.7893 | 0.7890 | 0.0485 | 0.0612 | | 0.1836 | 50.0 | 12500 | 0.6065 | 0.7915 | 0.3054 | 1.9957 | 0.7915 | 0.7910 | 0.0453 | 0.0607 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

hansin91/activity_classification

# activity_classification 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: - Loss: 0.7631 - Accuracy: 0.7710 ## 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: 3e-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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1235 | 1.0 | 315 | 1.3182 | 0.7099 | | 1.0404 | 2.0 | 630 | 0.9831 | 0.7278 | | 0.7899 | 3.0 | 945 | 0.9509 | 0.7175 | | 0.6961 | 4.0 | 1260 | 0.8258 | 0.7460 | | 0.615 | 5.0 | 1575 | 0.7890 | 0.7667 | | 0.5534 | 6.0 | 1890 | 0.7876 | 0.7591 | | 0.524 | 7.0 | 2205 | 0.7627 | 0.7663 | | 0.4588 | 8.0 | 2520 | 0.8256 | 0.7468 | | 0.4407 | 9.0 | 2835 | 0.8041 | 0.7615 | | 0.4039 | 10.0 | 3150 | 0.8367 | 0.7540 | | 0.3966 | 11.0 | 3465 | 0.8708 | 0.7492 | | 0.366 | 12.0 | 3780 | 0.8410 | 0.7544 | | 0.3522 | 13.0 | 4095 | 0.9019 | 0.7365 | | 0.3495 | 14.0 | 4410 | 0.8240 | 0.7567 | | 0.3206 | 15.0 | 4725 | 0.8428 | 0.7607 | | 0.3172 | 16.0 | 5040 | 0.8626 | 0.7607 | | 0.2931 | 17.0 | 5355 | 1.0311 | 0.7306 | | 0.2943 | 18.0 | 5670 | 0.9393 | 0.7544 | | 0.2886 | 19.0 | 5985 | 0.9379 | 0.7472 | | 0.2785 | 20.0 | 6300 | 0.8911 | 0.7552 | | 0.274 | 21.0 | 6615 | 0.9730 | 0.7484 | | 0.2716 | 22.0 | 6930 | 0.9546 | 0.7504 | | 0.2686 | 23.0 | 7245 | 0.8939 | 0.7651 | | 0.2489 | 24.0 | 7560 | 0.9397 | 0.7480 | | 0.257 | 25.0 | 7875 | 0.9298 | 0.7552 | | 0.244 | 26.0 | 8190 | 0.9977 | 0.7437 | | 0.2333 | 27.0 | 8505 | 0.9967 | 0.75 | | 0.2376 | 28.0 | 8820 | 1.0012 | 0.7508 | | 0.2428 | 29.0 | 9135 | 0.9674 | 0.7421 | | 0.224 | 30.0 | 9450 | 1.0239 | 0.7361 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]

anggtpd/snacks_classifier

# snacks_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 the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0090 - Accuracy: 0.8908

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

purabp1249/swin-tiny-patch4-window7-224-finetuned-herbify_plants

# swin-tiny-patch4-window7-224-finetuned-herbify_plants 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: 0.0703 - Accuracy: 0.9868 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7849 | 0.99 | 21 | 0.4281 | 0.8553 | | 0.2498 | 1.98 | 42 | 0.1295 | 0.9737 | | 0.1387 | 2.96 | 63 | 0.0703 | 0.9868 | | 0.1039 | 3.95 | 84 | 0.0741 | 0.9737 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cpu - Datasets 2.14.5 - Tokenizers 0.13.3

[ "aloevera", "amruta_balli", "avacado", "bamboo", "betel" ]

grahmatagung/flower_classifier

# flower_classifier This model is a fine-tuned version of [jonathanfernandes/vit-base-patch16-224-finetuned-flower](https://huggingface.co/jonathanfernandes/vit-base-patch16-224-finetuned-flower) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2238 - Accuracy: 0.9878 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0568 | 1.0 | 102 | 0.9734 | 0.9322 | | 0.3193 | 2.0 | 205 | 0.3169 | 0.9805 | | 0.1862 | 2.99 | 306 | 0.2250 | 0.9853 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "1", "10", "16", "98", "99", "17", "18", "19", "2", "20", "21", "22", "23", "24", "100", "25", "26", "27", "28", "29", "3", "30", "31", "32", "33", "101", "34", "35", "36", "37", "38", "39", "4", "40", "41", "42", "102", "43", "44", "45", "46", "47", "48", "49", "5", "50", "51", "11", "52", "53", "54", "55", "56", "57", "58", "59", "6", "60", "12", "61", "62", "63", "64", "65", "66", "67", "68", "69", "7", "13", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "14", "8", "80", "81", "82", "83", "84", "85", "86", "87", "88", "15", "89", "9", "90", "91", "92", "93", "94", "95", "96", "97" ]

LucyintheSky/pose-estimation-front-side-back

# Pose Estimation: front,side,back ## Model description This model predicts the person's body position relative to the camera: **front, side, back**. It was trained on [Lucy in the Sky](https://www.lucyinthesky.com/shop) images. This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). ## Training and evaluation data It achieves the following results on the evaluation set: - Loss: 0.2524 - Accuracy: 0.9355 ### 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: 20 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "back", "front", "side" ]

stevanojs/pokemon_classification

# pokemon_classification 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 None dataset. It achieves the following results on the evaluation set: - Loss: 1.0586 - Accuracy: 0.9071 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.3925 | 1.0 | 350 | 4.0653 | 0.6705 | | 3.2005 | 2.0 | 700 | 3.1602 | 0.8227 | | 2.3615 | 3.0 | 1050 | 2.4281 | 0.8656 | | 1.5369 | 4.0 | 1400 | 1.8786 | 0.8821 | | 1.0741 | 5.0 | 1750 | 1.4818 | 0.9014 | | 0.7094 | 6.0 | 2100 | 1.2335 | 0.9014 | | 0.544 | 7.0 | 2450 | 1.0976 | 0.9042 | | 0.4622 | 8.0 | 2800 | 1.0586 | 0.9071 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "golbat", "machoke", "raichu", "dragonite", "fearow", "slowpoke", "weezing", "beedrill", "weedle", "cloyster", "vaporeon", "gyarados", "golduck", "zapdos", "machamp", "hitmonlee", "primeape", "cubone", "sandslash", "scyther", "haunter", "metapod", "tentacruel", "aerodactyl", "raticate", "kabutops", "ninetales", "zubat", "rhydon", "mew", "pinsir", "ditto", "victreebel", "omanyte", "horsea", "magnemite", "pikachu", "blastoise", "venomoth", "charizard", "seadra", "muk", "spearow", "bulbasaur", "bellsprout", "electrode", "ivysaur", "gloom", "poliwhirl", "flareon", "seaking", "hypno", "wartortle", "mankey", "tentacool", "exeggcute", "meowth", "growlithe", "tangela", "drowzee", "rapidash", "venonat", "omastar", "pidgeot", "nidorino", "porygon", "lickitung", "rattata", "machop", "charmeleon", "slowbro", "parasect", "eevee", "diglett", "starmie", "staryu", "psyduck", "dragonair", "magikarp", "vileplume", "marowak", "pidgeotto", "shellder", "mewtwo", "lapras", "farfetchd", "kingler", "seel", "kakuna", "doduo", "electabuzz", "charmander", "rhyhorn", "tauros", "dugtrio", "kabuto", "poliwrath", "gengar", "exeggutor", "dewgong", "jigglypuff", "geodude", "kadabra", "nidorina", "sandshrew", "grimer", "persian", "mrmime", "pidgey", "koffing", "ekans", "alolan sandslash", "venusaur", "snorlax", "paras", "jynx", "chansey", "weepinbell", "hitmonchan", "gastly", "kangaskhan", "oddish", "wigglytuff", "graveler", "arcanine", "clefairy", "articuno", "poliwag", "golem", "abra", "squirtle", "voltorb", "ponyta", "moltres", "nidoqueen", "magmar", "onix", "vulpix", "butterfree", "dodrio", "krabby", "arbok", "clefable", "goldeen", "magneton", "dratini", "caterpie", "jolteon", "nidoking", "alakazam" ]

bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5837 - Accuracy: 0.7867 - Brier Loss: 0.3013 - Nll: 1.9882 - F1 Micro: 0.7868 - F1 Macro: 0.7860 - Ece: 0.0529 - Aurc: 0.0581 ## 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: 64 - eval_batch_size: 64 - 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 | 250 | 4.1958 | 0.1035 | 0.9350 | 9.1004 | 0.1035 | 0.0792 | 0.0472 | 0.9013 | | 4.2322 | 2.0 | 500 | 4.0778 | 0.173 | 0.9251 | 6.5742 | 0.173 | 0.1393 | 0.0993 | 0.7501 | | 4.2322 | 3.0 | 750 | 3.6484 | 0.339 | 0.8778 | 4.9108 | 0.339 | 0.2957 | 0.2172 | 0.5305 | | 3.5256 | 4.0 | 1000 | 2.5967 | 0.4592 | 0.6991 | 3.3640 | 0.4592 | 0.4220 | 0.1274 | 0.3285 | | 3.5256 | 5.0 | 1250 | 2.0345 | 0.5417 | 0.6078 | 3.0118 | 0.5417 | 0.5180 | 0.0976 | 0.2447 | | 1.9172 | 6.0 | 1500 | 1.4417 | 0.625 | 0.5029 | 2.7890 | 0.625 | 0.6123 | 0.0549 | 0.1623 | | 1.9172 | 7.0 | 1750 | 1.3298 | 0.639 | 0.4852 | 2.6110 | 0.639 | 0.6320 | 0.0558 | 0.1501 | | 1.1801 | 8.0 | 2000 | 1.1697 | 0.674 | 0.4473 | 2.4787 | 0.674 | 0.6712 | 0.0466 | 0.1283 | | 1.1801 | 9.0 | 2250 | 0.9625 | 0.7093 | 0.4020 | 2.3242 | 0.7093 | 0.7085 | 0.0526 | 0.1017 | | 0.8029 | 10.0 | 2500 | 0.9477 | 0.7215 | 0.3893 | 2.3193 | 0.7215 | 0.7228 | 0.0515 | 0.0971 | | 0.8029 | 11.0 | 2750 | 0.8527 | 0.7375 | 0.3692 | 2.2785 | 0.7375 | 0.7377 | 0.0490 | 0.0870 | | 0.5717 | 12.0 | 3000 | 0.7377 | 0.7515 | 0.3470 | 2.1475 | 0.7515 | 0.7529 | 0.0552 | 0.0757 | | 0.5717 | 13.0 | 3250 | 0.7309 | 0.7498 | 0.3469 | 2.1250 | 0.7498 | 0.7494 | 0.0589 | 0.0758 | | 0.4414 | 14.0 | 3500 | 0.7165 | 0.7558 | 0.3427 | 2.1045 | 0.7558 | 0.7576 | 0.0582 | 0.0721 | | 0.4414 | 15.0 | 3750 | 0.6865 | 0.7678 | 0.3319 | 2.0457 | 0.7678 | 0.7688 | 0.0551 | 0.0697 | | 0.3691 | 16.0 | 4000 | 0.7002 | 0.7662 | 0.3348 | 2.1280 | 0.7663 | 0.7664 | 0.0567 | 0.0698 | | 0.3691 | 17.0 | 4250 | 0.6896 | 0.7628 | 0.3326 | 2.0750 | 0.7628 | 0.7631 | 0.0608 | 0.0691 | | 0.3214 | 18.0 | 4500 | 0.6666 | 0.7715 | 0.3258 | 2.0468 | 0.7715 | 0.7707 | 0.0544 | 0.0680 | | 0.3214 | 19.0 | 4750 | 0.6735 | 0.7702 | 0.3277 | 2.0544 | 0.7702 | 0.7700 | 0.0571 | 0.0681 | | 0.2914 | 20.0 | 5000 | 0.6607 | 0.772 | 0.3241 | 2.0364 | 0.772 | 0.7729 | 0.0525 | 0.0659 | | 0.2914 | 21.0 | 5250 | 0.6625 | 0.7688 | 0.3217 | 2.0387 | 0.7688 | 0.7703 | 0.0455 | 0.0664 | | 0.2653 | 22.0 | 5500 | 0.6543 | 0.775 | 0.3200 | 2.0560 | 0.775 | 0.7752 | 0.0507 | 0.0647 | | 0.2653 | 23.0 | 5750 | 0.6409 | 0.7725 | 0.3188 | 2.0091 | 0.7725 | 0.7733 | 0.0554 | 0.0647 | | 0.2482 | 24.0 | 6000 | 0.6452 | 0.7758 | 0.3191 | 2.0256 | 0.7758 | 0.7756 | 0.0502 | 0.0655 | | 0.2482 | 25.0 | 6250 | 0.6401 | 0.7742 | 0.3196 | 2.0668 | 0.7742 | 0.7745 | 0.0528 | 0.0648 | | 0.2354 | 26.0 | 6500 | 0.6316 | 0.775 | 0.3171 | 2.0150 | 0.775 | 0.7755 | 0.0555 | 0.0634 | | 0.2354 | 27.0 | 6750 | 0.6257 | 0.7808 | 0.3147 | 2.0129 | 0.7808 | 0.7808 | 0.0503 | 0.0624 | | 0.2229 | 28.0 | 7000 | 0.6343 | 0.7778 | 0.3144 | 2.0910 | 0.7778 | 0.7776 | 0.0510 | 0.0624 | | 0.2229 | 29.0 | 7250 | 0.6206 | 0.781 | 0.3115 | 2.0399 | 0.7810 | 0.7798 | 0.0555 | 0.0606 | | 0.2147 | 30.0 | 7500 | 0.6262 | 0.777 | 0.3124 | 2.0603 | 0.777 | 0.7772 | 0.0539 | 0.0616 | | 0.2147 | 31.0 | 7750 | 0.6265 | 0.7788 | 0.3137 | 2.0833 | 0.7788 | 0.7777 | 0.0532 | 0.0614 | | 0.2058 | 32.0 | 8000 | 0.6134 | 0.7815 | 0.3119 | 2.0369 | 0.7815 | 0.7815 | 0.0514 | 0.0615 | | 0.2058 | 33.0 | 8250 | 0.6153 | 0.7772 | 0.3133 | 2.0513 | 0.7773 | 0.7772 | 0.0534 | 0.0623 | | 0.1994 | 34.0 | 8500 | 0.6143 | 0.7853 | 0.3098 | 2.0188 | 0.7853 | 0.7857 | 0.0508 | 0.0611 | | 0.1994 | 35.0 | 8750 | 0.6096 | 0.7827 | 0.3086 | 2.0134 | 0.7828 | 0.7828 | 0.0512 | 0.0606 | | 0.1932 | 36.0 | 9000 | 0.6094 | 0.784 | 0.3067 | 2.0151 | 0.7840 | 0.7847 | 0.0471 | 0.0602 | | 0.1932 | 37.0 | 9250 | 0.6142 | 0.7833 | 0.3111 | 2.0213 | 0.7833 | 0.7829 | 0.0542 | 0.0608 | | 0.1895 | 38.0 | 9500 | 0.6103 | 0.7812 | 0.3094 | 2.0594 | 0.7812 | 0.7799 | 0.0529 | 0.0603 | | 0.1895 | 39.0 | 9750 | 0.6059 | 0.781 | 0.3078 | 2.0386 | 0.7810 | 0.7806 | 0.0545 | 0.0607 | | 0.1848 | 40.0 | 10000 | 0.6042 | 0.782 | 0.3072 | 2.0133 | 0.782 | 0.7824 | 0.0527 | 0.0603 | | 0.1848 | 41.0 | 10250 | 0.5991 | 0.785 | 0.3043 | 2.0124 | 0.785 | 0.7853 | 0.0496 | 0.0594 | | 0.1793 | 42.0 | 10500 | 0.6034 | 0.784 | 0.3058 | 2.0607 | 0.7840 | 0.7838 | 0.0490 | 0.0599 | | 0.1793 | 43.0 | 10750 | 0.6047 | 0.7827 | 0.3068 | 2.0139 | 0.7828 | 0.7819 | 0.0492 | 0.0595 | | 0.1768 | 44.0 | 11000 | 0.5982 | 0.785 | 0.3057 | 2.0303 | 0.785 | 0.7843 | 0.0473 | 0.0596 | | 0.1768 | 45.0 | 11250 | 0.6036 | 0.7795 | 0.3087 | 2.0173 | 0.7795 | 0.7788 | 0.0549 | 0.0607 | | 0.1743 | 46.0 | 11500 | 0.5974 | 0.785 | 0.3060 | 2.0026 | 0.785 | 0.7839 | 0.0478 | 0.0596 | | 0.1743 | 47.0 | 11750 | 0.5996 | 0.782 | 0.3068 | 2.0144 | 0.782 | 0.7825 | 0.0480 | 0.0598 | | 0.1707 | 48.0 | 12000 | 0.5958 | 0.7833 | 0.3079 | 2.0344 | 0.7833 | 0.7827 | 0.0500 | 0.0598 | | 0.1707 | 49.0 | 12250 | 0.5969 | 0.782 | 0.3060 | 2.0162 | 0.782 | 0.7820 | 0.0482 | 0.0597 | | 0.1683 | 50.0 | 12500 | 0.5933 | 0.784 | 0.3043 | 1.9897 | 0.7840 | 0.7836 | 0.0496 | 0.0589 | | 0.1683 | 51.0 | 12750 | 0.5935 | 0.7833 | 0.3042 | 2.0142 | 0.7833 | 0.7829 | 0.0501 | 0.0586 | | 0.1649 | 52.0 | 13000 | 0.5950 | 0.7847 | 0.3050 | 2.0125 | 0.7847 | 0.7851 | 0.0475 | 0.0591 | | 0.1649 | 53.0 | 13250 | 0.5904 | 0.7837 | 0.3020 | 1.9830 | 0.7837 | 0.7837 | 0.0504 | 0.0584 | | 0.1636 | 54.0 | 13500 | 0.5926 | 0.785 | 0.3042 | 2.0006 | 0.785 | 0.7845 | 0.0493 | 0.0588 | | 0.1636 | 55.0 | 13750 | 0.5885 | 0.7847 | 0.3029 | 2.0025 | 0.7847 | 0.7843 | 0.0505 | 0.0585 | | 0.1616 | 56.0 | 14000 | 0.5920 | 0.788 | 0.3041 | 2.0174 | 0.788 | 0.7878 | 0.0520 | 0.0591 | | 0.1616 | 57.0 | 14250 | 0.5927 | 0.7863 | 0.3033 | 2.0321 | 0.7863 | 0.7858 | 0.0521 | 0.0588 | | 0.1592 | 58.0 | 14500 | 0.5878 | 0.787 | 0.3017 | 1.9751 | 0.787 | 0.7874 | 0.0461 | 0.0584 | | 0.1592 | 59.0 | 14750 | 0.5888 | 0.7867 | 0.3030 | 1.9996 | 0.7868 | 0.7864 | 0.0494 | 0.0582 | | 0.1585 | 60.0 | 15000 | 0.5929 | 0.786 | 0.3052 | 2.0237 | 0.786 | 0.7857 | 0.0512 | 0.0584 | | 0.1585 | 61.0 | 15250 | 0.5894 | 0.7865 | 0.3026 | 1.9895 | 0.7865 | 0.7864 | 0.0548 | 0.0585 | | 0.1562 | 62.0 | 15500 | 0.5903 | 0.7873 | 0.3033 | 1.9670 | 0.7873 | 0.7870 | 0.0481 | 0.0584 | | 0.1562 | 63.0 | 15750 | 0.5896 | 0.7853 | 0.3023 | 1.9681 | 0.7853 | 0.7850 | 0.0520 | 0.0587 | | 0.1548 | 64.0 | 16000 | 0.5903 | 0.7847 | 0.3027 | 1.9865 | 0.7847 | 0.7846 | 0.0506 | 0.0587 | | 0.1548 | 65.0 | 16250 | 0.5910 | 0.7853 | 0.3039 | 2.0009 | 0.7853 | 0.7849 | 0.0515 | 0.0593 | | 0.1537 | 66.0 | 16500 | 0.5866 | 0.7883 | 0.3012 | 1.9561 | 0.7883 | 0.7881 | 0.0447 | 0.0581 | | 0.1537 | 67.0 | 16750 | 0.5858 | 0.7867 | 0.3009 | 1.9868 | 0.7868 | 0.7861 | 0.0486 | 0.0577 | | 0.1526 | 68.0 | 17000 | 0.5886 | 0.7867 | 0.3024 | 2.0009 | 0.7868 | 0.7862 | 0.0530 | 0.0587 | | 0.1526 | 69.0 | 17250 | 0.5850 | 0.7863 | 0.3010 | 2.0095 | 0.7863 | 0.7860 | 0.0510 | 0.0581 | | 0.1508 | 70.0 | 17500 | 0.5867 | 0.7865 | 0.3019 | 2.0304 | 0.7865 | 0.7861 | 0.0525 | 0.0583 | | 0.1508 | 71.0 | 17750 | 0.5895 | 0.7857 | 0.3038 | 2.0013 | 0.7857 | 0.7853 | 0.0478 | 0.0586 | | 0.15 | 72.0 | 18000 | 0.5894 | 0.7847 | 0.3025 | 2.0051 | 0.7847 | 0.7845 | 0.0500 | 0.0586 | | 0.15 | 73.0 | 18250 | 0.5867 | 0.7865 | 0.3022 | 1.9634 | 0.7865 | 0.7860 | 0.0489 | 0.0582 | | 0.149 | 74.0 | 18500 | 0.5888 | 0.7857 | 0.3026 | 1.9817 | 0.7857 | 0.7851 | 0.0497 | 0.0584 | | 0.149 | 75.0 | 18750 | 0.5823 | 0.7885 | 0.2994 | 1.9873 | 0.7885 | 0.7880 | 0.0476 | 0.0577 | | 0.1483 | 76.0 | 19000 | 0.5866 | 0.7853 | 0.3025 | 1.9870 | 0.7853 | 0.7849 | 0.0531 | 0.0583 | | 0.1483 | 77.0 | 19250 | 0.5866 | 0.7867 | 0.3013 | 1.9933 | 0.7868 | 0.7862 | 0.0498 | 0.0577 | | 0.1478 | 78.0 | 19500 | 0.5844 | 0.787 | 0.3010 | 1.9793 | 0.787 | 0.7868 | 0.0465 | 0.0579 | | 0.1478 | 79.0 | 19750 | 0.5850 | 0.7857 | 0.3005 | 1.9856 | 0.7857 | 0.7855 | 0.0489 | 0.0580 | | 0.1463 | 80.0 | 20000 | 0.5829 | 0.7893 | 0.2999 | 2.0003 | 0.7893 | 0.7890 | 0.0543 | 0.0578 | | 0.1463 | 81.0 | 20250 | 0.5845 | 0.7867 | 0.3011 | 2.0178 | 0.7868 | 0.7864 | 0.0494 | 0.0580 | | 0.1457 | 82.0 | 20500 | 0.5878 | 0.7865 | 0.3022 | 2.0108 | 0.7865 | 0.7861 | 0.0507 | 0.0583 | | 0.1457 | 83.0 | 20750 | 0.5862 | 0.7865 | 0.3016 | 1.9996 | 0.7865 | 0.7865 | 0.0505 | 0.0585 | | 0.1452 | 84.0 | 21000 | 0.5851 | 0.7863 | 0.3011 | 2.0002 | 0.7863 | 0.7859 | 0.0481 | 0.0582 | | 0.1452 | 85.0 | 21250 | 0.5850 | 0.787 | 0.3013 | 1.9659 | 0.787 | 0.7867 | 0.0524 | 0.0582 | | 0.1449 | 86.0 | 21500 | 0.5878 | 0.7867 | 0.3023 | 1.9837 | 0.7868 | 0.7866 | 0.0526 | 0.0581 | | 0.1449 | 87.0 | 21750 | 0.5844 | 0.7873 | 0.3010 | 1.9807 | 0.7873 | 0.7865 | 0.0522 | 0.0577 | | 0.1437 | 88.0 | 22000 | 0.5846 | 0.7877 | 0.3012 | 1.9947 | 0.7877 | 0.7869 | 0.0464 | 0.0580 | | 0.1437 | 89.0 | 22250 | 0.5859 | 0.787 | 0.3016 | 2.0002 | 0.787 | 0.7867 | 0.0503 | 0.0581 | | 0.143 | 90.0 | 22500 | 0.5838 | 0.7865 | 0.3010 | 1.9996 | 0.7865 | 0.7859 | 0.0496 | 0.0576 | | 0.143 | 91.0 | 22750 | 0.5843 | 0.7837 | 0.3011 | 1.9683 | 0.7837 | 0.7834 | 0.0501 | 0.0583 | | 0.1426 | 92.0 | 23000 | 0.5843 | 0.7873 | 0.3010 | 1.9960 | 0.7873 | 0.7870 | 0.0524 | 0.0578 | | 0.1426 | 93.0 | 23250 | 0.5827 | 0.7847 | 0.3005 | 1.9719 | 0.7847 | 0.7844 | 0.0506 | 0.0579 | | 0.1428 | 94.0 | 23500 | 0.5831 | 0.7865 | 0.3009 | 1.9781 | 0.7865 | 0.7862 | 0.0517 | 0.0579 | | 0.1428 | 95.0 | 23750 | 0.5821 | 0.784 | 0.3001 | 1.9641 | 0.7840 | 0.7838 | 0.0505 | 0.0579 | | 0.1424 | 96.0 | 24000 | 0.5850 | 0.7845 | 0.3020 | 1.9667 | 0.7845 | 0.7842 | 0.0526 | 0.0584 | | 0.1424 | 97.0 | 24250 | 0.5850 | 0.7847 | 0.3012 | 1.9776 | 0.7847 | 0.7844 | 0.0508 | 0.0579 | | 0.142 | 98.0 | 24500 | 0.5845 | 0.7877 | 0.3011 | 1.9745 | 0.7877 | 0.7870 | 0.0491 | 0.0579 | | 0.142 | 99.0 | 24750 | 0.5834 | 0.7853 | 0.3010 | 1.9679 | 0.7853 | 0.7852 | 0.0506 | 0.0581 | | 0.1416 | 100.0 | 25000 | 0.5837 | 0.7867 | 0.3013 | 1.9882 | 0.7868 | 0.7860 | 0.0529 | 0.0581 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7429 - Accuracy: 0.7853 - Brier Loss: 0.3044 - Nll: 2.0364 - F1 Micro: 0.7853 - F1 Macro: 0.7862 - Ece: 0.0430 - Aurc: 0.0599 ## 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: 64 - eval_batch_size: 64 - 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 | 250 | 9.5443 | 0.0765 | 0.9365 | 3.7373 | 0.0765 | 0.0522 | 0.0360 | 0.9336 | | 9.4095 | 2.0 | 500 | 7.4542 | 0.0757 | 0.9312 | 2.8468 | 0.0757 | 0.0316 | 0.0425 | 0.8840 | | 9.4095 | 3.0 | 750 | 5.8933 | 0.0975 | 0.9356 | 3.2058 | 0.0975 | 0.0408 | 0.0798 | 0.8593 | | 5.9994 | 4.0 | 1000 | 4.3665 | 0.2125 | 0.8700 | 5.3759 | 0.2125 | 0.1290 | 0.0743 | 0.7029 | | 5.9994 | 5.0 | 1250 | 3.0367 | 0.4415 | 0.6924 | 4.9073 | 0.4415 | 0.4283 | 0.0806 | 0.3570 | | 3.2184 | 6.0 | 1500 | 2.1589 | 0.579 | 0.5587 | 3.7412 | 0.579 | 0.5771 | 0.0572 | 0.2172 | | 3.2184 | 7.0 | 1750 | 1.5582 | 0.652 | 0.4673 | 3.0701 | 0.652 | 0.6456 | 0.0517 | 0.1478 | | 1.6737 | 8.0 | 2000 | 1.3502 | 0.6893 | 0.4266 | 2.8575 | 0.6893 | 0.6860 | 0.0544 | 0.1175 | | 1.6737 | 9.0 | 2250 | 1.1389 | 0.7188 | 0.3914 | 2.5937 | 0.7188 | 0.7195 | 0.0544 | 0.1006 | | 1.0789 | 10.0 | 2500 | 1.0563 | 0.7302 | 0.3742 | 2.5043 | 0.7302 | 0.7305 | 0.0618 | 0.0912 | | 1.0789 | 11.0 | 2750 | 1.0035 | 0.7428 | 0.3604 | 2.4375 | 0.7428 | 0.7441 | 0.0587 | 0.0823 | | 0.7934 | 12.0 | 3000 | 0.9169 | 0.7548 | 0.3472 | 2.2921 | 0.7548 | 0.7555 | 0.0547 | 0.0762 | | 0.7934 | 13.0 | 3250 | 0.8628 | 0.7598 | 0.3386 | 2.2849 | 0.7598 | 0.7600 | 0.0550 | 0.0739 | | 0.6268 | 14.0 | 3500 | 0.8773 | 0.7675 | 0.3362 | 2.2170 | 0.7675 | 0.7692 | 0.0490 | 0.0718 | | 0.6268 | 15.0 | 3750 | 0.8263 | 0.7682 | 0.3306 | 2.1617 | 0.7682 | 0.7702 | 0.0534 | 0.0704 | | 0.5269 | 16.0 | 4000 | 0.8422 | 0.7708 | 0.3289 | 2.1907 | 0.7707 | 0.7717 | 0.0524 | 0.0687 | | 0.5269 | 17.0 | 4250 | 0.8100 | 0.7745 | 0.3241 | 2.1664 | 0.7745 | 0.7761 | 0.0509 | 0.0667 | | 0.4516 | 18.0 | 4500 | 0.8013 | 0.7778 | 0.3215 | 2.1216 | 0.7778 | 0.7790 | 0.0473 | 0.0669 | | 0.4516 | 19.0 | 4750 | 0.7911 | 0.7802 | 0.3183 | 2.1224 | 0.7802 | 0.7812 | 0.0476 | 0.0648 | | 0.4039 | 20.0 | 5000 | 0.7900 | 0.7775 | 0.3197 | 2.0969 | 0.7775 | 0.7797 | 0.0473 | 0.0647 | | 0.4039 | 21.0 | 5250 | 0.7919 | 0.7792 | 0.3191 | 2.1445 | 0.7792 | 0.7810 | 0.0531 | 0.0652 | | 0.3563 | 22.0 | 5500 | 0.7960 | 0.7802 | 0.3166 | 2.0849 | 0.7802 | 0.7818 | 0.0478 | 0.0649 | | 0.3563 | 23.0 | 5750 | 0.7615 | 0.7825 | 0.3128 | 2.0834 | 0.7825 | 0.7833 | 0.0478 | 0.0638 | | 0.3251 | 24.0 | 6000 | 0.7840 | 0.7792 | 0.3151 | 2.0841 | 0.7792 | 0.7800 | 0.0513 | 0.0648 | | 0.3251 | 25.0 | 6250 | 0.7837 | 0.7792 | 0.3159 | 2.0889 | 0.7792 | 0.7808 | 0.0485 | 0.0643 | | 0.2949 | 26.0 | 6500 | 0.7827 | 0.7802 | 0.3158 | 2.0416 | 0.7802 | 0.7819 | 0.0548 | 0.0648 | | 0.2949 | 27.0 | 6750 | 0.7650 | 0.78 | 0.3130 | 2.0411 | 0.78 | 0.7807 | 0.0506 | 0.0629 | | 0.2669 | 28.0 | 7000 | 0.7787 | 0.7802 | 0.3133 | 2.0843 | 0.7802 | 0.7810 | 0.0454 | 0.0627 | | 0.2669 | 29.0 | 7250 | 0.7892 | 0.782 | 0.3163 | 2.0953 | 0.782 | 0.7826 | 0.0508 | 0.0635 | | 0.2512 | 30.0 | 7500 | 0.7775 | 0.7825 | 0.3126 | 2.0904 | 0.7825 | 0.7837 | 0.0451 | 0.0633 | | 0.2512 | 31.0 | 7750 | 0.7601 | 0.7817 | 0.3124 | 2.0251 | 0.7817 | 0.7827 | 0.0485 | 0.0627 | | 0.231 | 32.0 | 8000 | 0.7669 | 0.7833 | 0.3120 | 2.0685 | 0.7833 | 0.7842 | 0.0472 | 0.0629 | | 0.231 | 33.0 | 8250 | 0.7652 | 0.7847 | 0.3116 | 2.0661 | 0.7847 | 0.7858 | 0.0519 | 0.0625 | | 0.2172 | 34.0 | 8500 | 0.7637 | 0.7837 | 0.3107 | 2.0264 | 0.7837 | 0.7852 | 0.0487 | 0.0628 | | 0.2172 | 35.0 | 8750 | 0.7691 | 0.783 | 0.3120 | 2.0535 | 0.7830 | 0.7844 | 0.0438 | 0.0634 | | 0.2032 | 36.0 | 9000 | 0.7647 | 0.7845 | 0.3093 | 2.0480 | 0.7845 | 0.7852 | 0.0471 | 0.0620 | | 0.2032 | 37.0 | 9250 | 0.7727 | 0.782 | 0.3122 | 2.0610 | 0.782 | 0.7830 | 0.0493 | 0.0628 | | 0.1925 | 38.0 | 9500 | 0.7563 | 0.7843 | 0.3085 | 2.0267 | 0.7843 | 0.7849 | 0.0459 | 0.0608 | | 0.1925 | 39.0 | 9750 | 0.7597 | 0.7835 | 0.3087 | 2.0062 | 0.7835 | 0.7845 | 0.0485 | 0.0614 | | 0.1823 | 40.0 | 10000 | 0.7611 | 0.7833 | 0.3107 | 2.0007 | 0.7833 | 0.7853 | 0.0479 | 0.0625 | | 0.1823 | 41.0 | 10250 | 0.7608 | 0.7843 | 0.3076 | 2.0335 | 0.7843 | 0.7854 | 0.0486 | 0.0602 | | 0.17 | 42.0 | 10500 | 0.7535 | 0.7833 | 0.3096 | 2.0121 | 0.7833 | 0.7844 | 0.0505 | 0.0613 | | 0.17 | 43.0 | 10750 | 0.7524 | 0.7845 | 0.3066 | 2.0425 | 0.7845 | 0.7856 | 0.0476 | 0.0605 | | 0.1639 | 44.0 | 11000 | 0.7608 | 0.7808 | 0.3108 | 2.0739 | 0.7808 | 0.7816 | 0.0503 | 0.0618 | | 0.1639 | 45.0 | 11250 | 0.7560 | 0.786 | 0.3063 | 1.9876 | 0.786 | 0.7868 | 0.0496 | 0.0607 | | 0.1575 | 46.0 | 11500 | 0.7494 | 0.784 | 0.3063 | 2.0311 | 0.7840 | 0.7846 | 0.0416 | 0.0601 | | 0.1575 | 47.0 | 11750 | 0.7515 | 0.7857 | 0.3069 | 2.0539 | 0.7857 | 0.7866 | 0.0456 | 0.0609 | | 0.1493 | 48.0 | 12000 | 0.7511 | 0.7843 | 0.3086 | 2.0325 | 0.7843 | 0.7852 | 0.0552 | 0.0612 | | 0.1493 | 49.0 | 12250 | 0.7495 | 0.787 | 0.3067 | 2.0231 | 0.787 | 0.7880 | 0.0475 | 0.0605 | | 0.1425 | 50.0 | 12500 | 0.7538 | 0.7867 | 0.3052 | 2.0267 | 0.7868 | 0.7870 | 0.0507 | 0.0603 | | 0.1425 | 51.0 | 12750 | 0.7529 | 0.7847 | 0.3081 | 2.0592 | 0.7847 | 0.7859 | 0.0467 | 0.0604 | | 0.1356 | 52.0 | 13000 | 0.7527 | 0.7808 | 0.3071 | 2.0349 | 0.7808 | 0.7818 | 0.0473 | 0.0607 | | 0.1356 | 53.0 | 13250 | 0.7451 | 0.7865 | 0.3049 | 2.0368 | 0.7865 | 0.7879 | 0.0484 | 0.0595 | | 0.1325 | 54.0 | 13500 | 0.7481 | 0.7857 | 0.3056 | 2.0223 | 0.7857 | 0.7869 | 0.0468 | 0.0603 | | 0.1325 | 55.0 | 13750 | 0.7470 | 0.7835 | 0.3057 | 2.0306 | 0.7835 | 0.7844 | 0.0492 | 0.0601 | | 0.1264 | 56.0 | 14000 | 0.7471 | 0.7873 | 0.3053 | 2.0336 | 0.7873 | 0.7880 | 0.0519 | 0.0601 | | 0.1264 | 57.0 | 14250 | 0.7429 | 0.7895 | 0.3032 | 2.0149 | 0.7895 | 0.7903 | 0.0468 | 0.0595 | | 0.1208 | 58.0 | 14500 | 0.7399 | 0.7885 | 0.3035 | 2.0147 | 0.7885 | 0.7895 | 0.0433 | 0.0596 | | 0.1208 | 59.0 | 14750 | 0.7518 | 0.786 | 0.3076 | 2.0481 | 0.786 | 0.7873 | 0.0403 | 0.0607 | | 0.119 | 60.0 | 15000 | 0.7483 | 0.7903 | 0.3058 | 2.0138 | 0.7903 | 0.7914 | 0.0471 | 0.0601 | | 0.119 | 61.0 | 15250 | 0.7463 | 0.7845 | 0.3043 | 2.0617 | 0.7845 | 0.7855 | 0.0458 | 0.0599 | | 0.1128 | 62.0 | 15500 | 0.7478 | 0.7875 | 0.3056 | 2.0187 | 0.7875 | 0.7888 | 0.0452 | 0.0604 | | 0.1128 | 63.0 | 15750 | 0.7510 | 0.784 | 0.3061 | 2.0204 | 0.7840 | 0.7850 | 0.0495 | 0.0605 | | 0.1109 | 64.0 | 16000 | 0.7424 | 0.786 | 0.3053 | 2.0167 | 0.786 | 0.7871 | 0.0449 | 0.0603 | | 0.1109 | 65.0 | 16250 | 0.7473 | 0.7885 | 0.3054 | 2.0200 | 0.7885 | 0.7893 | 0.0471 | 0.0600 | | 0.1078 | 66.0 | 16500 | 0.7467 | 0.7873 | 0.3054 | 2.0224 | 0.7873 | 0.7883 | 0.0482 | 0.0599 | | 0.1078 | 67.0 | 16750 | 0.7445 | 0.7893 | 0.3039 | 2.0082 | 0.7893 | 0.7895 | 0.0456 | 0.0593 | | 0.1051 | 68.0 | 17000 | 0.7490 | 0.7873 | 0.3063 | 2.0152 | 0.7873 | 0.7883 | 0.0505 | 0.0602 | | 0.1051 | 69.0 | 17250 | 0.7490 | 0.785 | 0.3061 | 2.0103 | 0.785 | 0.7861 | 0.0465 | 0.0602 | | 0.1009 | 70.0 | 17500 | 0.7445 | 0.7875 | 0.3049 | 2.0308 | 0.7875 | 0.7884 | 0.0483 | 0.0598 | | 0.1009 | 71.0 | 17750 | 0.7490 | 0.7863 | 0.3068 | 2.0260 | 0.7863 | 0.7875 | 0.0495 | 0.0604 | | 0.0984 | 72.0 | 18000 | 0.7465 | 0.7893 | 0.3059 | 2.0161 | 0.7893 | 0.7906 | 0.0427 | 0.0601 | | 0.0984 | 73.0 | 18250 | 0.7451 | 0.7873 | 0.3058 | 2.0204 | 0.7873 | 0.7882 | 0.0511 | 0.0605 | | 0.0966 | 74.0 | 18500 | 0.7445 | 0.7875 | 0.3042 | 2.0227 | 0.7875 | 0.7886 | 0.0495 | 0.0599 | | 0.0966 | 75.0 | 18750 | 0.7443 | 0.7863 | 0.3040 | 2.0138 | 0.7863 | 0.7872 | 0.0442 | 0.0598 | | 0.0947 | 76.0 | 19000 | 0.7448 | 0.7865 | 0.3054 | 2.0234 | 0.7865 | 0.7873 | 0.0457 | 0.0598 | | 0.0947 | 77.0 | 19250 | 0.7448 | 0.7865 | 0.3041 | 2.0110 | 0.7865 | 0.7875 | 0.0508 | 0.0596 | | 0.0931 | 78.0 | 19500 | 0.7460 | 0.7883 | 0.3040 | 2.0125 | 0.7883 | 0.7895 | 0.0467 | 0.0595 | | 0.0931 | 79.0 | 19750 | 0.7456 | 0.7883 | 0.3038 | 2.0302 | 0.7883 | 0.7894 | 0.0455 | 0.0596 | | 0.0899 | 80.0 | 20000 | 0.7469 | 0.788 | 0.3040 | 2.0188 | 0.788 | 0.7892 | 0.0487 | 0.0597 | | 0.0899 | 81.0 | 20250 | 0.7421 | 0.788 | 0.3041 | 2.0359 | 0.788 | 0.7888 | 0.0427 | 0.0595 | | 0.0882 | 82.0 | 20500 | 0.7444 | 0.7865 | 0.3051 | 2.0219 | 0.7865 | 0.7875 | 0.0479 | 0.0600 | | 0.0882 | 83.0 | 20750 | 0.7439 | 0.788 | 0.3039 | 2.0197 | 0.788 | 0.7894 | 0.0439 | 0.0597 | | 0.0871 | 84.0 | 21000 | 0.7421 | 0.7865 | 0.3040 | 1.9910 | 0.7865 | 0.7876 | 0.0445 | 0.0598 | | 0.0871 | 85.0 | 21250 | 0.7429 | 0.7887 | 0.3043 | 2.0253 | 0.7887 | 0.7898 | 0.0426 | 0.0597 | | 0.0869 | 86.0 | 21500 | 0.7442 | 0.7873 | 0.3041 | 2.0156 | 0.7873 | 0.7885 | 0.0488 | 0.0596 | | 0.0869 | 87.0 | 21750 | 0.7439 | 0.7857 | 0.3051 | 2.0099 | 0.7857 | 0.7867 | 0.0465 | 0.0599 | | 0.084 | 88.0 | 22000 | 0.7434 | 0.786 | 0.3040 | 1.9926 | 0.786 | 0.7869 | 0.0469 | 0.0598 | | 0.084 | 89.0 | 22250 | 0.7431 | 0.7873 | 0.3048 | 2.0028 | 0.7873 | 0.7880 | 0.0442 | 0.0599 | | 0.0821 | 90.0 | 22500 | 0.7447 | 0.7867 | 0.3040 | 2.0349 | 0.7868 | 0.7876 | 0.0477 | 0.0596 | | 0.0821 | 91.0 | 22750 | 0.7443 | 0.7877 | 0.3051 | 2.0356 | 0.7877 | 0.7887 | 0.0486 | 0.0601 | | 0.0813 | 92.0 | 23000 | 0.7500 | 0.7873 | 0.3053 | 2.0465 | 0.7873 | 0.7880 | 0.0484 | 0.0601 | | 0.0813 | 93.0 | 23250 | 0.7449 | 0.788 | 0.3037 | 1.9966 | 0.788 | 0.7890 | 0.0441 | 0.0594 | | 0.0811 | 94.0 | 23500 | 0.7466 | 0.7897 | 0.3048 | 2.0297 | 0.7897 | 0.7907 | 0.0429 | 0.0600 | | 0.0811 | 95.0 | 23750 | 0.7482 | 0.7875 | 0.3058 | 2.0319 | 0.7875 | 0.7885 | 0.0464 | 0.0601 | | 0.0808 | 96.0 | 24000 | 0.7473 | 0.7863 | 0.3055 | 2.0219 | 0.7863 | 0.7874 | 0.0477 | 0.0603 | | 0.0808 | 97.0 | 24250 | 0.7451 | 0.7855 | 0.3044 | 2.0356 | 0.7855 | 0.7865 | 0.0481 | 0.0594 | | 0.08 | 98.0 | 24500 | 0.7442 | 0.7857 | 0.3042 | 2.0213 | 0.7857 | 0.7868 | 0.0475 | 0.0595 | | 0.08 | 99.0 | 24750 | 0.7462 | 0.7863 | 0.3053 | 2.0354 | 0.7863 | 0.7874 | 0.0425 | 0.0599 | | 0.079 | 100.0 | 25000 | 0.7429 | 0.7853 | 0.3044 | 2.0364 | 0.7853 | 0.7862 | 0.0430 | 0.0599 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9013 - Accuracy: 0.7933 - Brier Loss: 0.3080 - Nll: 1.8102 - F1 Micro: 0.7932 - F1 Macro: 0.7937 - Ece: 0.0719 - Aurc: 0.0635 ## 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: 64 - eval_batch_size: 64 - 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 | 250 | 6.0054 | 0.098 | 0.9327 | 9.3196 | 0.0980 | 0.0481 | 0.0462 | 0.8670 | | 6.0141 | 2.0 | 500 | 5.4713 | 0.2195 | 0.8933 | 5.2235 | 0.2195 | 0.1452 | 0.1046 | 0.7129 | | 6.0141 | 3.0 | 750 | 4.4006 | 0.4535 | 0.7034 | 3.0178 | 0.4535 | 0.4351 | 0.1373 | 0.3334 | | 4.5079 | 4.0 | 1000 | 3.8431 | 0.59 | 0.5686 | 2.5843 | 0.59 | 0.5822 | 0.1309 | 0.2072 | | 4.5079 | 5.0 | 1250 | 3.5315 | 0.6552 | 0.4864 | 2.4330 | 0.6552 | 0.6537 | 0.1048 | 0.1504 | | 3.5028 | 6.0 | 1500 | 3.2850 | 0.707 | 0.4163 | 2.2375 | 0.707 | 0.7082 | 0.0790 | 0.1111 | | 3.5028 | 7.0 | 1750 | 3.0974 | 0.7312 | 0.3721 | 2.0933 | 0.7312 | 0.7328 | 0.0452 | 0.0899 | | 3.0599 | 8.0 | 2000 | 3.0385 | 0.7455 | 0.3561 | 2.0148 | 0.7455 | 0.7456 | 0.0432 | 0.0838 | | 3.0599 | 9.0 | 2250 | 2.9978 | 0.7565 | 0.3432 | 1.9780 | 0.7565 | 0.7572 | 0.0437 | 0.0777 | | 2.8562 | 10.0 | 2500 | 2.9853 | 0.7622 | 0.3397 | 1.9176 | 0.7622 | 0.7619 | 0.0495 | 0.0751 | | 2.8562 | 11.0 | 2750 | 2.9803 | 0.7615 | 0.3385 | 1.9327 | 0.7615 | 0.7627 | 0.0547 | 0.0760 | | 2.7414 | 12.0 | 3000 | 2.9711 | 0.7658 | 0.3322 | 1.9439 | 0.7658 | 0.7661 | 0.0495 | 0.0740 | | 2.7414 | 13.0 | 3250 | 2.9618 | 0.771 | 0.3276 | 1.8599 | 0.771 | 0.7718 | 0.0548 | 0.0704 | | 2.6658 | 14.0 | 3500 | 2.9534 | 0.7762 | 0.3252 | 1.8935 | 0.7762 | 0.7770 | 0.0581 | 0.0699 | | 2.6658 | 15.0 | 3750 | 2.9568 | 0.776 | 0.3248 | 1.8836 | 0.776 | 0.7776 | 0.0588 | 0.0699 | | 2.6197 | 16.0 | 4000 | 2.9196 | 0.7812 | 0.3169 | 1.8338 | 0.7812 | 0.7814 | 0.0601 | 0.0655 | | 2.6197 | 17.0 | 4250 | 2.9267 | 0.7785 | 0.3202 | 1.8430 | 0.7785 | 0.7783 | 0.0647 | 0.0677 | | 2.5794 | 18.0 | 4500 | 2.9189 | 0.779 | 0.3155 | 1.8279 | 0.779 | 0.7794 | 0.0631 | 0.0661 | | 2.5794 | 19.0 | 4750 | 2.9324 | 0.7823 | 0.3177 | 1.8508 | 0.7823 | 0.7823 | 0.0665 | 0.0669 | | 2.5553 | 20.0 | 5000 | 2.9192 | 0.7837 | 0.3146 | 1.8312 | 0.7837 | 0.7840 | 0.0641 | 0.0654 | | 2.5553 | 21.0 | 5250 | 2.9160 | 0.7817 | 0.3140 | 1.8366 | 0.7817 | 0.7828 | 0.0682 | 0.0658 | | 2.53 | 22.0 | 5500 | 2.9172 | 0.7837 | 0.3139 | 1.8138 | 0.7837 | 0.7842 | 0.0602 | 0.0652 | | 2.53 | 23.0 | 5750 | 2.9132 | 0.7875 | 0.3134 | 1.8254 | 0.7875 | 0.7877 | 0.0656 | 0.0646 | | 2.5127 | 24.0 | 6000 | 2.9108 | 0.7875 | 0.3132 | 1.8367 | 0.7875 | 0.7869 | 0.0669 | 0.0652 | | 2.5127 | 25.0 | 6250 | 2.9272 | 0.7837 | 0.3139 | 1.8551 | 0.7837 | 0.7843 | 0.0632 | 0.0653 | | 2.4979 | 26.0 | 6500 | 2.9157 | 0.7867 | 0.3128 | 1.8101 | 0.7868 | 0.7876 | 0.0655 | 0.0647 | | 2.4979 | 27.0 | 6750 | 2.9031 | 0.785 | 0.3112 | 1.8089 | 0.785 | 0.7856 | 0.0688 | 0.0639 | | 2.4814 | 28.0 | 7000 | 2.9094 | 0.7875 | 0.3110 | 1.8594 | 0.7875 | 0.7880 | 0.0677 | 0.0646 | | 2.4814 | 29.0 | 7250 | 2.9110 | 0.7885 | 0.3116 | 1.8150 | 0.7885 | 0.7891 | 0.0696 | 0.0639 | | 2.4741 | 30.0 | 7500 | 2.9039 | 0.7877 | 0.3091 | 1.8471 | 0.7877 | 0.7887 | 0.0694 | 0.0632 | | 2.4741 | 31.0 | 7750 | 2.9029 | 0.7907 | 0.3087 | 1.7604 | 0.7907 | 0.7917 | 0.0691 | 0.0633 | | 2.4626 | 32.0 | 8000 | 2.8983 | 0.7877 | 0.3094 | 1.8191 | 0.7877 | 0.7884 | 0.0677 | 0.0625 | | 2.4626 | 33.0 | 8250 | 2.9024 | 0.7897 | 0.3088 | 1.8025 | 0.7897 | 0.7905 | 0.0720 | 0.0635 | | 2.4558 | 34.0 | 8500 | 2.9055 | 0.792 | 0.3070 | 1.7869 | 0.792 | 0.7920 | 0.0667 | 0.0628 | | 2.4558 | 35.0 | 8750 | 2.9055 | 0.788 | 0.3104 | 1.8349 | 0.788 | 0.7883 | 0.0733 | 0.0645 | | 2.4481 | 36.0 | 9000 | 2.9061 | 0.7887 | 0.3078 | 1.7840 | 0.7887 | 0.7898 | 0.0676 | 0.0642 | | 2.4481 | 37.0 | 9250 | 2.9086 | 0.7917 | 0.3102 | 1.7942 | 0.7917 | 0.7923 | 0.0716 | 0.0644 | | 2.4422 | 38.0 | 9500 | 2.9067 | 0.7897 | 0.3084 | 1.7915 | 0.7897 | 0.7900 | 0.0704 | 0.0637 | | 2.4422 | 39.0 | 9750 | 2.9080 | 0.7927 | 0.3092 | 1.7951 | 0.7927 | 0.7930 | 0.0709 | 0.0631 | | 2.4386 | 40.0 | 10000 | 2.9064 | 0.7943 | 0.3084 | 1.8079 | 0.7943 | 0.7949 | 0.0734 | 0.0635 | | 2.4386 | 41.0 | 10250 | 2.8990 | 0.792 | 0.3056 | 1.7918 | 0.792 | 0.7924 | 0.0699 | 0.0623 | | 2.4312 | 42.0 | 10500 | 2.9057 | 0.7893 | 0.3090 | 1.7892 | 0.7893 | 0.7901 | 0.0735 | 0.0641 | | 2.4312 | 43.0 | 10750 | 2.8998 | 0.7923 | 0.3079 | 1.7909 | 0.7923 | 0.7932 | 0.0707 | 0.0630 | | 2.4294 | 44.0 | 11000 | 2.9108 | 0.7905 | 0.3090 | 1.8220 | 0.7905 | 0.7916 | 0.0773 | 0.0636 | | 2.4294 | 45.0 | 11250 | 2.9030 | 0.7927 | 0.3086 | 1.8126 | 0.7927 | 0.7932 | 0.0710 | 0.0631 | | 2.4282 | 46.0 | 11500 | 2.9033 | 0.7915 | 0.3077 | 1.8234 | 0.7915 | 0.7920 | 0.0712 | 0.0631 | | 2.4282 | 47.0 | 11750 | 2.8975 | 0.7957 | 0.3063 | 1.8070 | 0.7957 | 0.7968 | 0.0702 | 0.0630 | | 2.4246 | 48.0 | 12000 | 2.9049 | 0.7935 | 0.3085 | 1.8090 | 0.7935 | 0.7944 | 0.0722 | 0.0635 | | 2.4246 | 49.0 | 12250 | 2.9020 | 0.792 | 0.3075 | 1.8233 | 0.792 | 0.7927 | 0.0700 | 0.0638 | | 2.4227 | 50.0 | 12500 | 2.9013 | 0.7933 | 0.3080 | 1.8102 | 0.7932 | 0.7937 | 0.0719 | 0.0635 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.5

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 - Accuracy: 0.69 - Brier Loss: 0.4919 - Nll: 2.4969 - F1 Micro: 0.69 - F1 Macro: 0.6317 - Ece: 0.3029 - Aurc: 0.1260 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.4796 | 0.165 | 0.8965 | 8.4885 | 0.165 | 0.1123 | 0.2151 | 0.8341 | | No log | 2.0 | 26 | 1.4679 | 0.165 | 0.8954 | 8.3391 | 0.165 | 0.1066 | 0.2136 | 0.8332 | | No log | 3.0 | 39 | 1.4170 | 0.21 | 0.8858 | 6.1941 | 0.2100 | 0.0969 | 0.2433 | 0.7991 | | No log | 4.0 | 52 | 1.3472 | 0.21 | 0.8711 | 6.0602 | 0.2100 | 0.0728 | 0.2320 | 0.7271 | | No log | 5.0 | 65 | 1.2776 | 0.19 | 0.8572 | 6.1293 | 0.19 | 0.0537 | 0.2422 | 0.7473 | | No log | 6.0 | 78 | 1.1840 | 0.245 | 0.8353 | 6.2405 | 0.245 | 0.1060 | 0.2810 | 0.6690 | | No log | 7.0 | 91 | 1.0740 | 0.365 | 0.7936 | 6.3617 | 0.3650 | 0.1739 | 0.3136 | 0.3646 | | No log | 8.0 | 104 | 1.1102 | 0.345 | 0.8081 | 5.8896 | 0.345 | 0.1812 | 0.3046 | 0.4292 | | No log | 9.0 | 117 | 1.0735 | 0.34 | 0.7963 | 5.9970 | 0.34 | 0.1842 | 0.3028 | 0.4286 | | No log | 10.0 | 130 | 1.1145 | 0.265 | 0.8110 | 5.9054 | 0.265 | 0.1300 | 0.2511 | 0.6350 | | No log | 11.0 | 143 | 0.9981 | 0.325 | 0.7659 | 5.3834 | 0.325 | 0.1655 | 0.2790 | 0.4860 | | No log | 12.0 | 156 | 1.0500 | 0.285 | 0.7898 | 4.9696 | 0.285 | 0.1594 | 0.2604 | 0.6636 | | No log | 13.0 | 169 | 0.8764 | 0.445 | 0.6976 | 4.6456 | 0.445 | 0.2647 | 0.2779 | 0.3020 | | No log | 14.0 | 182 | 0.9147 | 0.48 | 0.7108 | 4.4793 | 0.48 | 0.2942 | 0.3262 | 0.2862 | | No log | 15.0 | 195 | 0.9776 | 0.38 | 0.7434 | 4.4065 | 0.38 | 0.2269 | 0.2938 | 0.5297 | | No log | 16.0 | 208 | 0.8066 | 0.47 | 0.6494 | 3.9671 | 0.47 | 0.2966 | 0.2791 | 0.2907 | | No log | 17.0 | 221 | 0.7766 | 0.535 | 0.6305 | 3.5250 | 0.535 | 0.3866 | 0.3003 | 0.2424 | | No log | 18.0 | 234 | 0.8186 | 0.535 | 0.6458 | 3.3670 | 0.535 | 0.3792 | 0.3005 | 0.2311 | | No log | 19.0 | 247 | 0.8156 | 0.52 | 0.6430 | 3.1633 | 0.52 | 0.3675 | 0.3072 | 0.2667 | | No log | 20.0 | 260 | 0.8386 | 0.55 | 0.6462 | 3.2549 | 0.55 | 0.4251 | 0.3103 | 0.2703 | | No log | 21.0 | 273 | 0.7996 | 0.515 | 0.6342 | 3.1396 | 0.515 | 0.3969 | 0.3177 | 0.2867 | | No log | 22.0 | 286 | 0.8605 | 0.6 | 0.6472 | 3.2563 | 0.6 | 0.4717 | 0.3810 | 0.2113 | | No log | 23.0 | 299 | 0.7138 | 0.595 | 0.5713 | 3.1171 | 0.595 | 0.4657 | 0.2773 | 0.2034 | | No log | 24.0 | 312 | 0.7212 | 0.665 | 0.5740 | 2.9688 | 0.665 | 0.5474 | 0.3366 | 0.1754 | | No log | 25.0 | 325 | 0.7463 | 0.63 | 0.5843 | 2.8998 | 0.63 | 0.5502 | 0.3432 | 0.2072 | | No log | 26.0 | 338 | 0.7231 | 0.67 | 0.5626 | 3.1334 | 0.67 | 0.5564 | 0.3160 | 0.1521 | | No log | 27.0 | 351 | 0.6913 | 0.68 | 0.5427 | 2.8906 | 0.68 | 0.5702 | 0.3354 | 0.1406 | | No log | 28.0 | 364 | 0.6825 | 0.66 | 0.5342 | 2.8619 | 0.66 | 0.5615 | 0.2902 | 0.1625 | | No log | 29.0 | 377 | 0.7015 | 0.665 | 0.5549 | 2.7315 | 0.665 | 0.5741 | 0.3305 | 0.1769 | | No log | 30.0 | 390 | 0.6939 | 0.67 | 0.5406 | 2.7114 | 0.67 | 0.5720 | 0.3353 | 0.1420 | | No log | 31.0 | 403 | 0.6836 | 0.69 | 0.5265 | 2.7567 | 0.69 | 0.5982 | 0.3216 | 0.1455 | | No log | 32.0 | 416 | 0.6728 | 0.69 | 0.5211 | 2.6858 | 0.69 | 0.6056 | 0.3124 | 0.1453 | | No log | 33.0 | 429 | 0.6926 | 0.675 | 0.5403 | 2.5815 | 0.675 | 0.6095 | 0.3258 | 0.1683 | | No log | 34.0 | 442 | 0.6673 | 0.66 | 0.5090 | 2.5591 | 0.66 | 0.5722 | 0.2950 | 0.1385 | | No log | 35.0 | 455 | 0.6811 | 0.675 | 0.5207 | 2.5813 | 0.675 | 0.5841 | 0.3324 | 0.1273 | | No log | 36.0 | 468 | 0.6648 | 0.69 | 0.5119 | 2.5745 | 0.69 | 0.6225 | 0.3433 | 0.1320 | | No log | 37.0 | 481 | 0.6623 | 0.67 | 0.5092 | 2.6134 | 0.67 | 0.6129 | 0.3204 | 0.1471 | | No log | 38.0 | 494 | 0.6635 | 0.69 | 0.5088 | 2.3862 | 0.69 | 0.6192 | 0.3201 | 0.1311 | | 0.7628 | 39.0 | 507 | 0.6554 | 0.685 | 0.5008 | 2.5849 | 0.685 | 0.6210 | 0.3179 | 0.1377 | | 0.7628 | 40.0 | 520 | 0.6567 | 0.685 | 0.5022 | 2.6498 | 0.685 | 0.6310 | 0.3127 | 0.1414 | | 0.7628 | 41.0 | 533 | 0.6558 | 0.695 | 0.4996 | 2.5917 | 0.695 | 0.6347 | 0.3115 | 0.1321 | | 0.7628 | 42.0 | 546 | 0.6578 | 0.695 | 0.5021 | 2.4864 | 0.695 | 0.6259 | 0.3098 | 0.1306 | | 0.7628 | 43.0 | 559 | 0.6544 | 0.685 | 0.4969 | 2.5757 | 0.685 | 0.6175 | 0.2955 | 0.1342 | | 0.7628 | 44.0 | 572 | 0.6507 | 0.685 | 0.4944 | 2.5057 | 0.685 | 0.6257 | 0.3144 | 0.1304 | | 0.7628 | 45.0 | 585 | 0.6501 | 0.675 | 0.4937 | 2.4903 | 0.675 | 0.6208 | 0.3091 | 0.1301 | | 0.7628 | 46.0 | 598 | 0.6518 | 0.685 | 0.4949 | 2.4732 | 0.685 | 0.6254 | 0.3164 | 0.1235 | | 0.7628 | 47.0 | 611 | 0.6499 | 0.685 | 0.4936 | 2.4924 | 0.685 | 0.6273 | 0.3124 | 0.1323 | | 0.7628 | 48.0 | 624 | 0.6490 | 0.7 | 0.4925 | 2.4999 | 0.7 | 0.6353 | 0.3147 | 0.1243 | | 0.7628 | 49.0 | 637 | 0.6510 | 0.685 | 0.4933 | 2.5758 | 0.685 | 0.6242 | 0.3206 | 0.1281 | | 0.7628 | 50.0 | 650 | 0.6481 | 0.69 | 0.4919 | 2.4969 | 0.69 | 0.6317 | 0.3029 | 0.1260 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

# resnet101-base_tobacco-cnn_tobacco3482_kd_MSE This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0899 - Accuracy: 0.395 - Brier Loss: 0.6867 - Nll: 4.7352 - F1 Micro: 0.395 - F1 Macro: 0.2347 - Ece: 0.2366 - Aurc: 0.3626 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.1202 | 0.17 | 0.8964 | 8.4790 | 0.17 | 0.1089 | 0.2136 | 0.8244 | | No log | 2.0 | 26 | 1.0772 | 0.165 | 0.8950 | 8.2397 | 0.165 | 0.0929 | 0.2120 | 0.8534 | | No log | 3.0 | 39 | 0.9427 | 0.2 | 0.8847 | 7.1036 | 0.2000 | 0.0796 | 0.2384 | 0.7748 | | No log | 4.0 | 52 | 0.7947 | 0.21 | 0.8720 | 6.5481 | 0.2100 | 0.0649 | 0.2432 | 0.7270 | | No log | 5.0 | 65 | 0.5378 | 0.205 | 0.8432 | 6.3064 | 0.205 | 0.0544 | 0.2367 | 0.6763 | | No log | 6.0 | 78 | 0.4557 | 0.18 | 0.8402 | 6.3878 | 0.18 | 0.0308 | 0.2384 | 0.7467 | | No log | 7.0 | 91 | 0.4326 | 0.18 | 0.8383 | 6.3386 | 0.18 | 0.0308 | 0.2385 | 0.7234 | | No log | 8.0 | 104 | 0.2832 | 0.265 | 0.8085 | 6.3561 | 0.265 | 0.1012 | 0.2570 | 0.6272 | | No log | 9.0 | 117 | 0.2672 | 0.255 | 0.8124 | 6.2296 | 0.255 | 0.0981 | 0.2569 | 0.6567 | | No log | 10.0 | 130 | 0.2452 | 0.29 | 0.7953 | 6.3199 | 0.29 | 0.1153 | 0.2717 | 0.5884 | | No log | 11.0 | 143 | 0.2155 | 0.31 | 0.7764 | 6.3618 | 0.31 | 0.1231 | 0.2728 | 0.4803 | | No log | 12.0 | 156 | 0.1315 | 0.31 | 0.7371 | 6.2610 | 0.31 | 0.1231 | 0.2343 | 0.4419 | | No log | 13.0 | 169 | 0.1803 | 0.3 | 0.7665 | 6.1189 | 0.3 | 0.1187 | 0.2587 | 0.4579 | | No log | 14.0 | 182 | 0.1426 | 0.31 | 0.7386 | 6.1115 | 0.31 | 0.1236 | 0.2502 | 0.4341 | | No log | 15.0 | 195 | 0.1431 | 0.31 | 0.7334 | 5.9353 | 0.31 | 0.1274 | 0.2624 | 0.4233 | | No log | 16.0 | 208 | 0.1540 | 0.32 | 0.7318 | 5.7102 | 0.32 | 0.1432 | 0.2493 | 0.4322 | | No log | 17.0 | 221 | 0.2603 | 0.305 | 0.7784 | 5.6776 | 0.305 | 0.1361 | 0.2751 | 0.5118 | | No log | 18.0 | 234 | 0.1000 | 0.35 | 0.7074 | 5.4636 | 0.35 | 0.1574 | 0.2420 | 0.4027 | | No log | 19.0 | 247 | 0.1014 | 0.33 | 0.7131 | 5.5297 | 0.33 | 0.1413 | 0.2439 | 0.4245 | | No log | 20.0 | 260 | 0.2862 | 0.265 | 0.8013 | 5.5041 | 0.265 | 0.1126 | 0.2762 | 0.6324 | | No log | 21.0 | 273 | 0.1224 | 0.34 | 0.7183 | 5.2027 | 0.34 | 0.1544 | 0.2673 | 0.4222 | | No log | 22.0 | 286 | 0.1406 | 0.345 | 0.7173 | 5.1426 | 0.345 | 0.1612 | 0.2710 | 0.4019 | | No log | 23.0 | 299 | 0.1509 | 0.34 | 0.7270 | 5.0281 | 0.34 | 0.1565 | 0.2641 | 0.4178 | | No log | 24.0 | 312 | 0.0994 | 0.37 | 0.6996 | 5.1278 | 0.37 | 0.1771 | 0.2390 | 0.3930 | | No log | 25.0 | 325 | 0.1965 | 0.35 | 0.7474 | 5.0356 | 0.35 | 0.1707 | 0.2774 | 0.4503 | | No log | 26.0 | 338 | 0.1104 | 0.37 | 0.7085 | 5.0275 | 0.37 | 0.1984 | 0.2663 | 0.3927 | | No log | 27.0 | 351 | 0.1674 | 0.34 | 0.7299 | 4.9200 | 0.34 | 0.1739 | 0.2787 | 0.4257 | | No log | 28.0 | 364 | 0.2424 | 0.335 | 0.7626 | 5.0286 | 0.335 | 0.1693 | 0.2905 | 0.5297 | | No log | 29.0 | 377 | 0.1261 | 0.345 | 0.7185 | 5.0591 | 0.345 | 0.1730 | 0.2892 | 0.4142 | | No log | 30.0 | 390 | 0.1574 | 0.365 | 0.7213 | 4.8809 | 0.3650 | 0.1951 | 0.2983 | 0.4062 | | No log | 31.0 | 403 | 0.1227 | 0.365 | 0.7098 | 4.8152 | 0.3650 | 0.1996 | 0.2802 | 0.3992 | | No log | 32.0 | 416 | 0.1114 | 0.355 | 0.7010 | 4.8224 | 0.3550 | 0.1915 | 0.2657 | 0.3958 | | No log | 33.0 | 429 | 0.1027 | 0.39 | 0.6934 | 4.7755 | 0.39 | 0.2245 | 0.2653 | 0.3695 | | No log | 34.0 | 442 | 0.0959 | 0.385 | 0.6875 | 4.8715 | 0.3850 | 0.2299 | 0.2591 | 0.3699 | | No log | 35.0 | 455 | 0.0905 | 0.395 | 0.6897 | 4.8649 | 0.395 | 0.2367 | 0.2519 | 0.3627 | | No log | 36.0 | 468 | 0.0879 | 0.365 | 0.6911 | 4.8472 | 0.3650 | 0.2132 | 0.2437 | 0.3910 | | No log | 37.0 | 481 | 0.0867 | 0.39 | 0.6881 | 4.7379 | 0.39 | 0.2335 | 0.2576 | 0.3680 | | No log | 38.0 | 494 | 0.0934 | 0.4 | 0.6916 | 4.6797 | 0.4000 | 0.2490 | 0.2578 | 0.3628 | | 0.2032 | 39.0 | 507 | 0.0928 | 0.38 | 0.6901 | 4.6734 | 0.38 | 0.2268 | 0.2432 | 0.3783 | | 0.2032 | 40.0 | 520 | 0.0995 | 0.39 | 0.6875 | 4.8180 | 0.39 | 0.2323 | 0.2647 | 0.3730 | | 0.2032 | 41.0 | 533 | 0.0944 | 0.37 | 0.6892 | 4.8193 | 0.37 | 0.2174 | 0.2536 | 0.3862 | | 0.2032 | 42.0 | 546 | 0.0904 | 0.415 | 0.6885 | 4.5644 | 0.415 | 0.2556 | 0.2729 | 0.3573 | | 0.2032 | 43.0 | 559 | 0.0951 | 0.39 | 0.6899 | 4.6549 | 0.39 | 0.2417 | 0.2525 | 0.3692 | | 0.2032 | 44.0 | 572 | 0.0884 | 0.4 | 0.6860 | 4.6572 | 0.4000 | 0.2402 | 0.2587 | 0.3557 | | 0.2032 | 45.0 | 585 | 0.0867 | 0.38 | 0.6874 | 4.6558 | 0.38 | 0.2278 | 0.2526 | 0.3738 | | 0.2032 | 46.0 | 598 | 0.0861 | 0.405 | 0.6844 | 4.5777 | 0.405 | 0.2537 | 0.2548 | 0.3628 | | 0.2032 | 47.0 | 611 | 0.0874 | 0.385 | 0.6853 | 4.4946 | 0.3850 | 0.2380 | 0.2570 | 0.3743 | | 0.2032 | 48.0 | 624 | 0.0880 | 0.405 | 0.6857 | 4.5605 | 0.405 | 0.2500 | 0.2489 | 0.3555 | | 0.2032 | 49.0 | 637 | 0.0884 | 0.4 | 0.6853 | 4.6057 | 0.4000 | 0.2481 | 0.2401 | 0.3616 | | 0.2032 | 50.0 | 650 | 0.0899 | 0.395 | 0.6867 | 4.7352 | 0.395 | 0.2347 | 0.2366 | 0.3626 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6500 - Accuracy: 0.69 - Brier Loss: 0.5003 - Nll: 2.5629 - F1 Micro: 0.69 - F1 Macro: 0.6350 - Ece: 0.3098 - Aurc: 0.1329 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.4712 | 0.165 | 0.8966 | 8.4652 | 0.165 | 0.1101 | 0.2129 | 0.8342 | | No log | 2.0 | 26 | 1.4590 | 0.165 | 0.8951 | 8.1097 | 0.165 | 0.1059 | 0.2034 | 0.8021 | | No log | 3.0 | 39 | 1.4178 | 0.175 | 0.8873 | 6.8095 | 0.175 | 0.0813 | 0.2150 | 0.7994 | | No log | 4.0 | 52 | 1.3342 | 0.18 | 0.8702 | 6.4137 | 0.18 | 0.0475 | 0.2314 | 0.7558 | | No log | 5.0 | 65 | 1.2828 | 0.2 | 0.8587 | 6.1547 | 0.2000 | 0.0642 | 0.2429 | 0.7009 | | No log | 6.0 | 78 | 1.2675 | 0.205 | 0.8548 | 6.1395 | 0.205 | 0.0612 | 0.2348 | 0.7022 | | No log | 7.0 | 91 | 1.0716 | 0.31 | 0.7962 | 6.4589 | 0.31 | 0.1241 | 0.2787 | 0.4433 | | No log | 8.0 | 104 | 1.1184 | 0.29 | 0.8126 | 6.2585 | 0.29 | 0.1394 | 0.2863 | 0.5819 | | No log | 9.0 | 117 | 1.1021 | 0.31 | 0.8075 | 6.0370 | 0.31 | 0.1697 | 0.2834 | 0.5458 | | No log | 10.0 | 130 | 1.0268 | 0.33 | 0.7815 | 6.1370 | 0.33 | 0.1921 | 0.2856 | 0.5395 | | No log | 11.0 | 143 | 1.0290 | 0.355 | 0.7759 | 5.3640 | 0.3550 | 0.2143 | 0.2795 | 0.4697 | | No log | 12.0 | 156 | 0.9169 | 0.36 | 0.7262 | 5.2997 | 0.36 | 0.1995 | 0.2761 | 0.4070 | | No log | 13.0 | 169 | 0.9903 | 0.36 | 0.7586 | 4.9404 | 0.36 | 0.2200 | 0.2832 | 0.5343 | | No log | 14.0 | 182 | 0.9128 | 0.425 | 0.7082 | 4.5862 | 0.425 | 0.2706 | 0.2834 | 0.3542 | | No log | 15.0 | 195 | 1.0046 | 0.405 | 0.7441 | 3.9763 | 0.405 | 0.2759 | 0.3142 | 0.4602 | | No log | 16.0 | 208 | 0.9277 | 0.41 | 0.7146 | 4.3670 | 0.41 | 0.2763 | 0.2695 | 0.4409 | | No log | 17.0 | 221 | 0.9726 | 0.505 | 0.7208 | 3.5350 | 0.505 | 0.3736 | 0.3332 | 0.3469 | | No log | 18.0 | 234 | 0.7717 | 0.505 | 0.6280 | 3.4386 | 0.505 | 0.3412 | 0.2564 | 0.2567 | | No log | 19.0 | 247 | 0.7723 | 0.58 | 0.6143 | 3.6207 | 0.58 | 0.4125 | 0.3178 | 0.1847 | | No log | 20.0 | 260 | 0.8182 | 0.57 | 0.6419 | 3.1633 | 0.57 | 0.4855 | 0.3517 | 0.2530 | | No log | 21.0 | 273 | 0.7333 | 0.58 | 0.5891 | 3.3014 | 0.58 | 0.4512 | 0.2718 | 0.2137 | | No log | 22.0 | 286 | 0.7374 | 0.665 | 0.5856 | 3.0299 | 0.665 | 0.5432 | 0.3459 | 0.1657 | | No log | 23.0 | 299 | 0.7083 | 0.645 | 0.5564 | 3.0874 | 0.645 | 0.5180 | 0.3112 | 0.1608 | | No log | 24.0 | 312 | 0.7480 | 0.64 | 0.5901 | 3.0218 | 0.64 | 0.5410 | 0.3701 | 0.1976 | | No log | 25.0 | 325 | 0.7547 | 0.68 | 0.5894 | 2.9002 | 0.68 | 0.5801 | 0.3817 | 0.1559 | | No log | 26.0 | 338 | 0.6998 | 0.65 | 0.5474 | 2.9402 | 0.65 | 0.5468 | 0.2875 | 0.1707 | | No log | 27.0 | 351 | 0.6967 | 0.66 | 0.5506 | 2.8344 | 0.66 | 0.5578 | 0.3105 | 0.1707 | | No log | 28.0 | 364 | 0.6733 | 0.655 | 0.5332 | 2.6492 | 0.655 | 0.5719 | 0.2935 | 0.1554 | | No log | 29.0 | 377 | 0.7162 | 0.67 | 0.5596 | 2.7250 | 0.67 | 0.5721 | 0.3388 | 0.1423 | | No log | 30.0 | 390 | 0.6826 | 0.665 | 0.5291 | 2.7460 | 0.665 | 0.5797 | 0.3353 | 0.1469 | | No log | 31.0 | 403 | 0.6761 | 0.665 | 0.5195 | 2.7938 | 0.665 | 0.5647 | 0.3096 | 0.1485 | | No log | 32.0 | 416 | 0.6745 | 0.695 | 0.5295 | 2.6172 | 0.695 | 0.6160 | 0.3171 | 0.1636 | | No log | 33.0 | 429 | 0.6785 | 0.695 | 0.5242 | 2.5816 | 0.695 | 0.6115 | 0.3475 | 0.1349 | | No log | 34.0 | 442 | 0.6688 | 0.665 | 0.5174 | 2.6401 | 0.665 | 0.5833 | 0.2988 | 0.1427 | | No log | 35.0 | 455 | 0.6767 | 0.675 | 0.5275 | 2.6364 | 0.675 | 0.6027 | 0.3285 | 0.1483 | | No log | 36.0 | 468 | 0.6605 | 0.695 | 0.5076 | 2.6483 | 0.695 | 0.6252 | 0.3127 | 0.1372 | | No log | 37.0 | 481 | 0.6538 | 0.705 | 0.5029 | 2.6284 | 0.705 | 0.6340 | 0.3173 | 0.1220 | | No log | 38.0 | 494 | 0.6610 | 0.695 | 0.5102 | 2.5052 | 0.695 | 0.6375 | 0.3128 | 0.1298 | | 0.7532 | 39.0 | 507 | 0.6618 | 0.695 | 0.5110 | 2.5663 | 0.695 | 0.6268 | 0.3297 | 0.1367 | | 0.7532 | 40.0 | 520 | 0.6749 | 0.69 | 0.5235 | 2.5343 | 0.69 | 0.6341 | 0.3256 | 0.1332 | | 0.7532 | 41.0 | 533 | 0.6574 | 0.695 | 0.5062 | 2.4223 | 0.695 | 0.6338 | 0.3292 | 0.1469 | | 0.7532 | 42.0 | 546 | 0.6530 | 0.695 | 0.5026 | 2.6189 | 0.695 | 0.6390 | 0.2950 | 0.1391 | | 0.7532 | 43.0 | 559 | 0.6509 | 0.685 | 0.5003 | 2.5417 | 0.685 | 0.6299 | 0.3150 | 0.1368 | | 0.7532 | 44.0 | 572 | 0.6520 | 0.71 | 0.5030 | 2.4796 | 0.7100 | 0.6453 | 0.3251 | 0.1286 | | 0.7532 | 45.0 | 585 | 0.6494 | 0.69 | 0.4994 | 2.5431 | 0.69 | 0.6327 | 0.3138 | 0.1279 | | 0.7532 | 46.0 | 598 | 0.6515 | 0.71 | 0.5007 | 2.5295 | 0.7100 | 0.6541 | 0.3307 | 0.1208 | | 0.7532 | 47.0 | 611 | 0.6477 | 0.69 | 0.4979 | 2.5971 | 0.69 | 0.6323 | 0.3263 | 0.1281 | | 0.7532 | 48.0 | 624 | 0.6495 | 0.7 | 0.5007 | 2.6162 | 0.7 | 0.6395 | 0.3412 | 0.1272 | | 0.7532 | 49.0 | 637 | 0.6478 | 0.7 | 0.4968 | 2.4946 | 0.7 | 0.6386 | 0.3191 | 0.1309 | | 0.7532 | 50.0 | 650 | 0.6500 | 0.69 | 0.5003 | 2.5629 | 0.69 | 0.6350 | 0.3098 | 0.1329 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.7

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8009 - Accuracy: 0.695 - Brier Loss: 0.4518 - Nll: 2.3840 - F1 Micro: 0.695 - F1 Macro: 0.6406 - Ece: 0.2661 - Aurc: 0.1211 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.7971 | 0.17 | 0.8966 | 8.4593 | 0.17 | 0.1148 | 0.2202 | 0.8308 | | No log | 2.0 | 26 | 1.7887 | 0.13 | 0.8956 | 8.3211 | 0.13 | 0.0772 | 0.2024 | 0.8359 | | No log | 3.0 | 39 | 1.7450 | 0.225 | 0.8868 | 6.4554 | 0.225 | 0.1165 | 0.2502 | 0.7572 | | No log | 4.0 | 52 | 1.6811 | 0.24 | 0.8733 | 5.9510 | 0.24 | 0.0953 | 0.2651 | 0.6944 | | No log | 5.0 | 65 | 1.6411 | 0.19 | 0.8649 | 6.0993 | 0.19 | 0.0493 | 0.2422 | 0.7783 | | No log | 6.0 | 78 | 1.5475 | 0.195 | 0.8429 | 6.2065 | 0.195 | 0.0630 | 0.2472 | 0.7110 | | No log | 7.0 | 91 | 1.4688 | 0.3 | 0.8197 | 6.0345 | 0.3 | 0.1481 | 0.2936 | 0.5379 | | No log | 8.0 | 104 | 1.5036 | 0.285 | 0.8294 | 5.6660 | 0.285 | 0.1428 | 0.2869 | 0.6535 | | No log | 9.0 | 117 | 1.3901 | 0.34 | 0.7934 | 5.9107 | 0.34 | 0.1853 | 0.2894 | 0.5277 | | No log | 10.0 | 130 | 1.3484 | 0.37 | 0.7760 | 5.6441 | 0.37 | 0.2175 | 0.3177 | 0.5266 | | No log | 11.0 | 143 | 1.3375 | 0.34 | 0.7734 | 5.0872 | 0.34 | 0.2083 | 0.2902 | 0.5557 | | No log | 12.0 | 156 | 1.3639 | 0.305 | 0.7834 | 4.5070 | 0.305 | 0.1885 | 0.2674 | 0.6177 | | No log | 13.0 | 169 | 1.2321 | 0.415 | 0.7225 | 4.3464 | 0.415 | 0.2751 | 0.2943 | 0.3825 | | No log | 14.0 | 182 | 1.1453 | 0.44 | 0.6767 | 4.4158 | 0.44 | 0.2864 | 0.2617 | 0.3413 | | No log | 15.0 | 195 | 1.1830 | 0.43 | 0.6965 | 3.8251 | 0.4300 | 0.2972 | 0.2912 | 0.4239 | | No log | 16.0 | 208 | 1.0572 | 0.535 | 0.6230 | 3.5943 | 0.535 | 0.3758 | 0.2861 | 0.2291 | | No log | 17.0 | 221 | 1.0532 | 0.585 | 0.6151 | 3.3834 | 0.585 | 0.4331 | 0.3278 | 0.1879 | | No log | 18.0 | 234 | 1.0940 | 0.565 | 0.6374 | 3.2290 | 0.565 | 0.4431 | 0.3313 | 0.2415 | | No log | 19.0 | 247 | 0.9877 | 0.585 | 0.5886 | 3.1068 | 0.585 | 0.4564 | 0.2896 | 0.2110 | | No log | 20.0 | 260 | 1.0405 | 0.61 | 0.6056 | 3.1786 | 0.61 | 0.5038 | 0.3428 | 0.1962 | | No log | 21.0 | 273 | 0.9728 | 0.635 | 0.5634 | 2.9133 | 0.635 | 0.5293 | 0.3333 | 0.1664 | | No log | 22.0 | 286 | 0.9425 | 0.635 | 0.5527 | 2.8909 | 0.635 | 0.5237 | 0.3131 | 0.1796 | | No log | 23.0 | 299 | 0.9549 | 0.65 | 0.5605 | 2.8074 | 0.65 | 0.5539 | 0.3283 | 0.1914 | | No log | 24.0 | 312 | 1.0085 | 0.67 | 0.5733 | 2.8377 | 0.67 | 0.5543 | 0.3525 | 0.1571 | | No log | 25.0 | 325 | 0.9140 | 0.655 | 0.5257 | 2.5878 | 0.655 | 0.5603 | 0.3171 | 0.1495 | | No log | 26.0 | 338 | 0.8979 | 0.65 | 0.5249 | 2.7723 | 0.65 | 0.5563 | 0.2843 | 0.1646 | | No log | 27.0 | 351 | 0.8912 | 0.675 | 0.5082 | 2.6562 | 0.675 | 0.5837 | 0.2871 | 0.1380 | | No log | 28.0 | 364 | 0.8966 | 0.66 | 0.5242 | 2.3150 | 0.66 | 0.5890 | 0.3180 | 0.1777 | | No log | 29.0 | 377 | 0.8602 | 0.67 | 0.4959 | 2.5813 | 0.67 | 0.5866 | 0.3023 | 0.1319 | | No log | 30.0 | 390 | 0.8434 | 0.69 | 0.4779 | 2.5451 | 0.69 | 0.6130 | 0.3061 | 0.1188 | | No log | 31.0 | 403 | 0.8406 | 0.715 | 0.4782 | 2.3339 | 0.715 | 0.6438 | 0.3241 | 0.1092 | | No log | 32.0 | 416 | 0.8294 | 0.71 | 0.4726 | 2.5394 | 0.7100 | 0.6308 | 0.2922 | 0.1218 | | No log | 33.0 | 429 | 0.8329 | 0.68 | 0.4763 | 2.4520 | 0.68 | 0.6166 | 0.2592 | 0.1396 | | No log | 34.0 | 442 | 0.8937 | 0.69 | 0.5015 | 2.5649 | 0.69 | 0.6357 | 0.3293 | 0.1279 | | No log | 35.0 | 455 | 0.8358 | 0.665 | 0.4807 | 2.4437 | 0.665 | 0.6178 | 0.2380 | 0.1473 | | No log | 36.0 | 468 | 0.8283 | 0.685 | 0.4747 | 2.5408 | 0.685 | 0.6304 | 0.3126 | 0.1361 | | No log | 37.0 | 481 | 0.8235 | 0.685 | 0.4707 | 2.4620 | 0.685 | 0.6300 | 0.2757 | 0.1343 | | No log | 38.0 | 494 | 0.8289 | 0.68 | 0.4778 | 2.5443 | 0.68 | 0.6305 | 0.2935 | 0.1469 | | 0.9462 | 39.0 | 507 | 0.8373 | 0.69 | 0.4728 | 2.5775 | 0.69 | 0.6281 | 0.3028 | 0.1149 | | 0.9462 | 40.0 | 520 | 0.8062 | 0.715 | 0.4548 | 2.3673 | 0.715 | 0.6587 | 0.2776 | 0.1133 | | 0.9462 | 41.0 | 533 | 0.7990 | 0.705 | 0.4517 | 2.3284 | 0.705 | 0.6463 | 0.2716 | 0.1185 | | 0.9462 | 42.0 | 546 | 0.8210 | 0.7 | 0.4650 | 2.5646 | 0.7 | 0.6432 | 0.2690 | 0.1199 | | 0.9462 | 43.0 | 559 | 0.8102 | 0.695 | 0.4558 | 2.5651 | 0.695 | 0.6442 | 0.2656 | 0.1184 | | 0.9462 | 44.0 | 572 | 0.8061 | 0.69 | 0.4566 | 2.5154 | 0.69 | 0.6356 | 0.2816 | 0.1267 | | 0.9462 | 45.0 | 585 | 0.8018 | 0.7 | 0.4531 | 2.4982 | 0.7 | 0.6419 | 0.2696 | 0.1192 | | 0.9462 | 46.0 | 598 | 0.8040 | 0.7 | 0.4521 | 2.5309 | 0.7 | 0.6448 | 0.2797 | 0.1166 | | 0.9462 | 47.0 | 611 | 0.8062 | 0.68 | 0.4560 | 2.5452 | 0.68 | 0.6370 | 0.2744 | 0.1217 | | 0.9462 | 48.0 | 624 | 0.8011 | 0.69 | 0.4529 | 2.4281 | 0.69 | 0.6402 | 0.2594 | 0.1224 | | 0.9462 | 49.0 | 637 | 0.8017 | 0.69 | 0.4532 | 2.4239 | 0.69 | 0.6400 | 0.2613 | 0.1261 | | 0.9462 | 50.0 | 650 | 0.8009 | 0.695 | 0.4518 | 2.3840 | 0.695 | 0.6406 | 0.2661 | 0.1211 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8831 - Accuracy: 0.695 - Brier Loss: 0.4126 - Nll: 2.4628 - F1 Micro: 0.695 - F1 Macro: 0.6387 - Ece: 0.2426 - Aurc: 0.1068 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 2.1233 | 0.16 | 0.8967 | 8.5697 | 0.16 | 0.1066 | 0.2078 | 0.8316 | | No log | 2.0 | 26 | 2.1188 | 0.14 | 0.8961 | 8.2960 | 0.14 | 0.0886 | 0.1947 | 0.8419 | | No log | 3.0 | 39 | 2.0764 | 0.195 | 0.8873 | 6.4713 | 0.195 | 0.1159 | 0.2335 | 0.7665 | | No log | 4.0 | 52 | 2.0365 | 0.21 | 0.8787 | 5.7752 | 0.2100 | 0.0930 | 0.2376 | 0.7548 | | No log | 5.0 | 65 | 1.9888 | 0.2 | 0.8682 | 5.8737 | 0.2000 | 0.0775 | 0.2417 | 0.7314 | | No log | 6.0 | 78 | 1.8998 | 0.215 | 0.8465 | 5.8553 | 0.2150 | 0.0970 | 0.2586 | 0.7063 | | No log | 7.0 | 91 | 1.8351 | 0.33 | 0.8289 | 5.7781 | 0.33 | 0.1904 | 0.3089 | 0.6103 | | No log | 8.0 | 104 | 1.7342 | 0.4 | 0.7968 | 5.5366 | 0.4000 | 0.2476 | 0.3457 | 0.4276 | | No log | 9.0 | 117 | 1.6787 | 0.36 | 0.7757 | 5.7414 | 0.36 | 0.2148 | 0.3062 | 0.4324 | | No log | 10.0 | 130 | 1.6942 | 0.4 | 0.7870 | 5.2615 | 0.4000 | 0.2831 | 0.3168 | 0.5227 | | No log | 11.0 | 143 | 1.5992 | 0.4 | 0.7489 | 4.7833 | 0.4000 | 0.2649 | 0.3053 | 0.4679 | | No log | 12.0 | 156 | 1.6071 | 0.425 | 0.7532 | 4.2803 | 0.425 | 0.2906 | 0.3196 | 0.4646 | | No log | 13.0 | 169 | 1.4727 | 0.48 | 0.6925 | 4.1911 | 0.48 | 0.3239 | 0.2957 | 0.3081 | | No log | 14.0 | 182 | 1.4275 | 0.515 | 0.6705 | 3.7980 | 0.515 | 0.3569 | 0.3211 | 0.2626 | | No log | 15.0 | 195 | 1.3282 | 0.56 | 0.6200 | 3.6359 | 0.56 | 0.4163 | 0.2990 | 0.2213 | | No log | 16.0 | 208 | 1.3280 | 0.565 | 0.6263 | 3.4960 | 0.565 | 0.4177 | 0.3217 | 0.2346 | | No log | 17.0 | 221 | 1.3220 | 0.595 | 0.6196 | 3.2202 | 0.595 | 0.4639 | 0.3322 | 0.1992 | | No log | 18.0 | 234 | 1.2359 | 0.595 | 0.5840 | 3.3332 | 0.595 | 0.4780 | 0.3042 | 0.2011 | | No log | 19.0 | 247 | 1.1690 | 0.625 | 0.5531 | 3.2423 | 0.625 | 0.5233 | 0.2940 | 0.1807 | | No log | 20.0 | 260 | 1.1644 | 0.64 | 0.5532 | 3.0542 | 0.64 | 0.5429 | 0.3019 | 0.1821 | | No log | 21.0 | 273 | 1.1611 | 0.62 | 0.5516 | 2.9412 | 0.62 | 0.5193 | 0.2865 | 0.2160 | | No log | 22.0 | 286 | 1.3427 | 0.585 | 0.6361 | 3.0936 | 0.585 | 0.5089 | 0.3442 | 0.2922 | | No log | 23.0 | 299 | 1.1238 | 0.62 | 0.5440 | 2.7924 | 0.62 | 0.5458 | 0.2654 | 0.2088 | | No log | 24.0 | 312 | 1.2008 | 0.685 | 0.5615 | 2.5918 | 0.685 | 0.5890 | 0.3907 | 0.1516 | | No log | 25.0 | 325 | 1.0764 | 0.695 | 0.5000 | 2.6354 | 0.695 | 0.6107 | 0.3126 | 0.1397 | | No log | 26.0 | 338 | 1.0268 | 0.675 | 0.4822 | 2.4798 | 0.675 | 0.5992 | 0.2775 | 0.1229 | | No log | 27.0 | 351 | 1.0340 | 0.67 | 0.4893 | 2.4316 | 0.67 | 0.5997 | 0.2763 | 0.1638 | | No log | 28.0 | 364 | 1.0154 | 0.665 | 0.4769 | 2.6487 | 0.665 | 0.6034 | 0.2590 | 0.1487 | | No log | 29.0 | 377 | 1.0013 | 0.64 | 0.4814 | 2.5899 | 0.64 | 0.5771 | 0.2429 | 0.1593 | | No log | 30.0 | 390 | 1.0173 | 0.685 | 0.4714 | 2.6922 | 0.685 | 0.6178 | 0.2898 | 0.1423 | | No log | 31.0 | 403 | 0.9630 | 0.695 | 0.4509 | 2.6349 | 0.695 | 0.6206 | 0.2746 | 0.1248 | | No log | 32.0 | 416 | 0.9950 | 0.68 | 0.4648 | 2.4144 | 0.68 | 0.6362 | 0.3020 | 0.1725 | | No log | 33.0 | 429 | 0.9711 | 0.72 | 0.4502 | 2.6651 | 0.72 | 0.6571 | 0.2892 | 0.1268 | | No log | 34.0 | 442 | 0.9491 | 0.705 | 0.4425 | 2.7169 | 0.705 | 0.6425 | 0.2541 | 0.1145 | | No log | 35.0 | 455 | 0.9213 | 0.685 | 0.4309 | 2.5736 | 0.685 | 0.6174 | 0.2380 | 0.1161 | | No log | 36.0 | 468 | 0.9144 | 0.695 | 0.4297 | 2.4141 | 0.695 | 0.6308 | 0.2502 | 0.1154 | | No log | 37.0 | 481 | 0.9242 | 0.715 | 0.4264 | 2.7191 | 0.715 | 0.6429 | 0.2386 | 0.1030 | | No log | 38.0 | 494 | 0.9290 | 0.695 | 0.4346 | 2.6515 | 0.695 | 0.6367 | 0.2432 | 0.1189 | | 1.0953 | 39.0 | 507 | 0.9110 | 0.69 | 0.4262 | 2.6615 | 0.69 | 0.6328 | 0.2368 | 0.1112 | | 1.0953 | 40.0 | 520 | 0.9000 | 0.695 | 0.4186 | 2.4590 | 0.695 | 0.6417 | 0.2453 | 0.1070 | | 1.0953 | 41.0 | 533 | 0.8961 | 0.69 | 0.4189 | 2.4170 | 0.69 | 0.6368 | 0.2349 | 0.1090 | | 1.0953 | 42.0 | 546 | 0.9103 | 0.675 | 0.4286 | 2.6129 | 0.675 | 0.6193 | 0.2318 | 0.1190 | | 1.0953 | 43.0 | 559 | 0.8858 | 0.715 | 0.4131 | 2.5243 | 0.715 | 0.6517 | 0.2462 | 0.1053 | | 1.0953 | 44.0 | 572 | 0.8872 | 0.705 | 0.4135 | 2.3272 | 0.705 | 0.6542 | 0.2596 | 0.1051 | | 1.0953 | 45.0 | 585 | 0.8897 | 0.715 | 0.4136 | 2.3788 | 0.715 | 0.6532 | 0.2560 | 0.1035 | | 1.0953 | 46.0 | 598 | 0.8842 | 0.7 | 0.4117 | 2.5325 | 0.7 | 0.6446 | 0.2327 | 0.1075 | | 1.0953 | 47.0 | 611 | 0.8857 | 0.675 | 0.4141 | 2.5451 | 0.675 | 0.6203 | 0.2473 | 0.1125 | | 1.0953 | 48.0 | 624 | 0.8875 | 0.69 | 0.4164 | 2.4696 | 0.69 | 0.6352 | 0.2542 | 0.1109 | | 1.0953 | 49.0 | 637 | 0.8842 | 0.69 | 0.4153 | 2.5338 | 0.69 | 0.6358 | 0.2302 | 0.1112 | | 1.0953 | 50.0 | 650 | 0.8831 | 0.695 | 0.4126 | 2.4628 | 0.695 | 0.6387 | 0.2426 | 0.1068 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7844 - Accuracy: 0.742 - Brier Loss: 0.4405 - Nll: 2.8680 - F1 Micro: 0.7420 - F1 Macro: 0.7411 - Ece: 0.1946 - Aurc: 0.1002 ## 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: 64 - eval_batch_size: 64 - 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 | 250 | 2.7345 | 0.153 | 0.9327 | 8.3371 | 0.153 | 0.1246 | 0.0866 | 0.7933 | | 2.6983 | 2.0 | 500 | 2.4500 | 0.4213 | 0.8816 | 4.7062 | 0.4213 | 0.3924 | 0.3073 | 0.4444 | | 2.6983 | 3.0 | 750 | 1.7959 | 0.5012 | 0.7003 | 3.3576 | 0.5012 | 0.4758 | 0.1869 | 0.3051 | | 1.7341 | 4.0 | 1000 | 1.3637 | 0.5985 | 0.5511 | 2.8818 | 0.5985 | 0.5868 | 0.1005 | 0.1935 | | 1.7341 | 5.0 | 1250 | 1.1978 | 0.6498 | 0.4862 | 2.7546 | 0.6498 | 0.6471 | 0.0826 | 0.1500 | | 1.0818 | 6.0 | 1500 | 1.0812 | 0.6853 | 0.4364 | 2.6325 | 0.6853 | 0.6845 | 0.0522 | 0.1217 | | 1.0818 | 7.0 | 1750 | 1.0276 | 0.7013 | 0.4149 | 2.5542 | 0.7013 | 0.7003 | 0.0397 | 0.1108 | | 0.7498 | 8.0 | 2000 | 0.9724 | 0.7133 | 0.3944 | 2.4773 | 0.7133 | 0.7129 | 0.0505 | 0.1040 | | 0.7498 | 9.0 | 2250 | 0.9777 | 0.7248 | 0.3924 | 2.4916 | 0.7248 | 0.7242 | 0.0628 | 0.0992 | | 0.5034 | 10.0 | 2500 | 1.0027 | 0.724 | 0.3976 | 2.4974 | 0.724 | 0.7250 | 0.0751 | 0.1032 | | 0.5034 | 11.0 | 2750 | 0.9979 | 0.729 | 0.3913 | 2.5344 | 0.729 | 0.7295 | 0.0805 | 0.0988 | | 0.3237 | 12.0 | 3000 | 1.0553 | 0.7192 | 0.4075 | 2.6242 | 0.7192 | 0.7193 | 0.0963 | 0.1072 | | 0.3237 | 13.0 | 3250 | 1.1162 | 0.7175 | 0.4139 | 2.6543 | 0.7175 | 0.7185 | 0.1295 | 0.1093 | | 0.2023 | 14.0 | 3500 | 1.1259 | 0.725 | 0.4140 | 2.6758 | 0.7250 | 0.7246 | 0.1237 | 0.1055 | | 0.2023 | 15.0 | 3750 | 1.2728 | 0.7115 | 0.4381 | 2.8308 | 0.7115 | 0.7147 | 0.1464 | 0.1168 | | 0.1264 | 16.0 | 4000 | 1.2664 | 0.7222 | 0.4296 | 2.8434 | 0.7223 | 0.7236 | 0.1523 | 0.1107 | | 0.1264 | 17.0 | 4250 | 1.2620 | 0.724 | 0.4252 | 2.7990 | 0.724 | 0.7252 | 0.1563 | 0.1066 | | 0.0802 | 18.0 | 4500 | 1.3362 | 0.727 | 0.4293 | 2.8642 | 0.7270 | 0.7267 | 0.1653 | 0.1090 | | 0.0802 | 19.0 | 4750 | 1.3608 | 0.7302 | 0.4288 | 2.7893 | 0.7302 | 0.7318 | 0.1637 | 0.1059 | | 0.0553 | 20.0 | 5000 | 1.3757 | 0.7308 | 0.4303 | 2.8861 | 0.7308 | 0.7300 | 0.1670 | 0.1073 | | 0.0553 | 21.0 | 5250 | 1.4947 | 0.7295 | 0.4420 | 2.8306 | 0.7295 | 0.7300 | 0.1770 | 0.1128 | | 0.0329 | 22.0 | 5500 | 1.5338 | 0.7265 | 0.4416 | 2.8729 | 0.7265 | 0.7273 | 0.1808 | 0.1097 | | 0.0329 | 23.0 | 5750 | 1.5127 | 0.7355 | 0.4362 | 2.8574 | 0.7355 | 0.7366 | 0.1774 | 0.1045 | | 0.0258 | 24.0 | 6000 | 1.5189 | 0.7352 | 0.4360 | 2.8435 | 0.7353 | 0.7344 | 0.1784 | 0.1030 | | 0.0258 | 25.0 | 6250 | 1.5802 | 0.7362 | 0.4404 | 2.8399 | 0.7362 | 0.7362 | 0.1847 | 0.1013 | | 0.0193 | 26.0 | 6500 | 1.5869 | 0.737 | 0.4378 | 2.8237 | 0.737 | 0.7362 | 0.1846 | 0.1022 | | 0.0193 | 27.0 | 6750 | 1.6160 | 0.7365 | 0.4373 | 2.7928 | 0.7365 | 0.7360 | 0.1864 | 0.1049 | | 0.014 | 28.0 | 7000 | 1.6775 | 0.7372 | 0.4426 | 2.9236 | 0.7372 | 0.7373 | 0.1909 | 0.1039 | | 0.014 | 29.0 | 7250 | 1.6391 | 0.736 | 0.4370 | 2.8717 | 0.736 | 0.7358 | 0.1905 | 0.0999 | | 0.0132 | 30.0 | 7500 | 1.6804 | 0.7355 | 0.4434 | 2.8397 | 0.7355 | 0.7360 | 0.1903 | 0.1067 | | 0.0132 | 31.0 | 7750 | 1.6809 | 0.738 | 0.4386 | 2.8853 | 0.738 | 0.7387 | 0.1920 | 0.1015 | | 0.0121 | 32.0 | 8000 | 1.6953 | 0.734 | 0.4443 | 2.8451 | 0.734 | 0.7342 | 0.1961 | 0.1013 | | 0.0121 | 33.0 | 8250 | 1.7184 | 0.7425 | 0.4344 | 2.8180 | 0.7425 | 0.7423 | 0.1910 | 0.1014 | | 0.0098 | 34.0 | 8500 | 1.7151 | 0.735 | 0.4445 | 2.8532 | 0.735 | 0.7337 | 0.1952 | 0.1000 | | 0.0098 | 35.0 | 8750 | 1.7781 | 0.7338 | 0.4484 | 2.8133 | 0.7338 | 0.7351 | 0.1999 | 0.1052 | | 0.0086 | 36.0 | 9000 | 1.7540 | 0.7372 | 0.4443 | 2.8388 | 0.7372 | 0.7388 | 0.1954 | 0.1039 | | 0.0086 | 37.0 | 9250 | 1.7744 | 0.738 | 0.4474 | 2.8600 | 0.738 | 0.7390 | 0.1953 | 0.1057 | | 0.0079 | 38.0 | 9500 | 1.7446 | 0.7368 | 0.4417 | 2.8485 | 0.7367 | 0.7374 | 0.1972 | 0.1016 | | 0.0079 | 39.0 | 9750 | 1.7700 | 0.739 | 0.4398 | 2.8826 | 0.739 | 0.7395 | 0.1970 | 0.1023 | | 0.0076 | 40.0 | 10000 | 1.7896 | 0.7368 | 0.4442 | 2.8449 | 0.7367 | 0.7376 | 0.1988 | 0.1033 | | 0.0076 | 41.0 | 10250 | 1.7435 | 0.7402 | 0.4387 | 2.8390 | 0.7402 | 0.7405 | 0.1926 | 0.1031 | | 0.0074 | 42.0 | 10500 | 1.7837 | 0.7338 | 0.4470 | 2.8191 | 0.7338 | 0.7339 | 0.2018 | 0.1035 | | 0.0074 | 43.0 | 10750 | 1.8015 | 0.7392 | 0.4427 | 2.8093 | 0.7392 | 0.7401 | 0.1981 | 0.1017 | | 0.0061 | 44.0 | 11000 | 1.8155 | 0.739 | 0.4449 | 2.8333 | 0.739 | 0.7406 | 0.1983 | 0.1022 | | 0.0061 | 45.0 | 11250 | 1.7958 | 0.7392 | 0.4426 | 2.8161 | 0.7392 | 0.7385 | 0.1963 | 0.1039 | | 0.0059 | 46.0 | 11500 | 1.8089 | 0.7422 | 0.4411 | 2.8174 | 0.7422 | 0.7422 | 0.1955 | 0.1011 | | 0.0059 | 47.0 | 11750 | 1.8125 | 0.743 | 0.4386 | 2.8184 | 0.743 | 0.7435 | 0.1939 | 0.1012 | | 0.0053 | 48.0 | 12000 | 1.8004 | 0.7372 | 0.4432 | 2.8413 | 0.7372 | 0.7371 | 0.1995 | 0.1023 | | 0.0053 | 49.0 | 12250 | 1.8075 | 0.7405 | 0.4392 | 2.8569 | 0.7405 | 0.7397 | 0.1962 | 0.1015 | | 0.0055 | 50.0 | 12500 | 1.7844 | 0.742 | 0.4405 | 2.8680 | 0.7420 | 0.7411 | 0.1946 | 0.1002 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8012 - Accuracy: 0.7 - Brier Loss: 0.4467 - Nll: 2.5682 - F1 Micro: 0.7 - F1 Macro: 0.6313 - Ece: 0.2684 - Aurc: 0.1170 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.8024 | 0.16 | 0.8966 | 8.5001 | 0.16 | 0.1073 | 0.2079 | 0.8334 | | No log | 2.0 | 26 | 1.7941 | 0.145 | 0.8957 | 8.3207 | 0.145 | 0.0843 | 0.2022 | 0.8435 | | No log | 3.0 | 39 | 1.7486 | 0.2 | 0.8868 | 6.2015 | 0.2000 | 0.1007 | 0.2209 | 0.7900 | | No log | 4.0 | 52 | 1.6854 | 0.205 | 0.8738 | 6.0142 | 0.205 | 0.0707 | 0.2453 | 0.7584 | | No log | 5.0 | 65 | 1.6162 | 0.2 | 0.8594 | 6.2364 | 0.2000 | 0.0552 | 0.2466 | 0.7717 | | No log | 6.0 | 78 | 1.5412 | 0.235 | 0.8416 | 6.0423 | 0.235 | 0.0902 | 0.2589 | 0.7006 | | No log | 7.0 | 91 | 1.5011 | 0.295 | 0.8304 | 6.1420 | 0.295 | 0.1272 | 0.2803 | 0.6124 | | No log | 8.0 | 104 | 1.4415 | 0.3 | 0.8114 | 6.0440 | 0.3 | 0.1296 | 0.2870 | 0.5641 | | No log | 9.0 | 117 | 1.3257 | 0.38 | 0.7625 | 5.6923 | 0.38 | 0.2198 | 0.3136 | 0.3675 | | No log | 10.0 | 130 | 1.3748 | 0.33 | 0.7905 | 5.5276 | 0.33 | 0.1870 | 0.2947 | 0.5985 | | No log | 11.0 | 143 | 1.3294 | 0.39 | 0.7683 | 4.9632 | 0.39 | 0.2573 | 0.2940 | 0.4639 | | No log | 12.0 | 156 | 1.2444 | 0.385 | 0.7297 | 4.8431 | 0.3850 | 0.2330 | 0.2849 | 0.4173 | | No log | 13.0 | 169 | 1.2212 | 0.45 | 0.7153 | 4.5819 | 0.45 | 0.3051 | 0.3143 | 0.3379 | | No log | 14.0 | 182 | 1.1835 | 0.495 | 0.6888 | 3.6108 | 0.495 | 0.3412 | 0.3316 | 0.2873 | | No log | 15.0 | 195 | 1.1203 | 0.47 | 0.6559 | 3.6500 | 0.47 | 0.3348 | 0.2935 | 0.3061 | | No log | 16.0 | 208 | 1.1520 | 0.495 | 0.6707 | 3.8106 | 0.495 | 0.3632 | 0.2938 | 0.3604 | | No log | 17.0 | 221 | 1.0261 | 0.565 | 0.6021 | 3.3382 | 0.565 | 0.4214 | 0.2840 | 0.2047 | | No log | 18.0 | 234 | 1.0080 | 0.61 | 0.5914 | 3.2936 | 0.61 | 0.4748 | 0.3240 | 0.1806 | | No log | 19.0 | 247 | 1.0696 | 0.58 | 0.6253 | 3.2354 | 0.58 | 0.4686 | 0.3152 | 0.2626 | | No log | 20.0 | 260 | 0.9733 | 0.615 | 0.5722 | 3.1019 | 0.615 | 0.4968 | 0.3259 | 0.2066 | | No log | 21.0 | 273 | 0.9266 | 0.625 | 0.5423 | 3.0239 | 0.625 | 0.5202 | 0.2834 | 0.1782 | | No log | 22.0 | 286 | 0.9364 | 0.66 | 0.5461 | 2.9031 | 0.66 | 0.5461 | 0.3128 | 0.1601 | | No log | 23.0 | 299 | 0.9181 | 0.675 | 0.5307 | 2.8416 | 0.675 | 0.5584 | 0.3106 | 0.1462 | | No log | 24.0 | 312 | 0.9739 | 0.665 | 0.5539 | 2.8798 | 0.665 | 0.5634 | 0.3325 | 0.1610 | | No log | 25.0 | 325 | 0.8851 | 0.69 | 0.5099 | 2.7336 | 0.69 | 0.6013 | 0.3064 | 0.1437 | | No log | 26.0 | 338 | 0.8755 | 0.71 | 0.4979 | 2.7400 | 0.7100 | 0.6032 | 0.3162 | 0.1211 | | No log | 27.0 | 351 | 0.8653 | 0.675 | 0.4964 | 2.8339 | 0.675 | 0.5705 | 0.2977 | 0.1386 | | No log | 28.0 | 364 | 0.8838 | 0.675 | 0.5055 | 2.7456 | 0.675 | 0.5816 | 0.2969 | 0.1524 | | No log | 29.0 | 377 | 0.8805 | 0.68 | 0.5025 | 2.6942 | 0.68 | 0.5855 | 0.3099 | 0.1380 | | No log | 30.0 | 390 | 0.8585 | 0.665 | 0.4891 | 2.7511 | 0.665 | 0.5737 | 0.2627 | 0.1370 | | No log | 31.0 | 403 | 0.8410 | 0.675 | 0.4736 | 2.6431 | 0.675 | 0.5985 | 0.2670 | 0.1335 | | No log | 32.0 | 416 | 0.8378 | 0.71 | 0.4724 | 2.7320 | 0.7100 | 0.6236 | 0.2885 | 0.1153 | | No log | 33.0 | 429 | 0.8421 | 0.705 | 0.4718 | 2.6331 | 0.705 | 0.6326 | 0.2644 | 0.1147 | | No log | 34.0 | 442 | 0.8350 | 0.685 | 0.4697 | 2.8035 | 0.685 | 0.6062 | 0.2831 | 0.1291 | | No log | 35.0 | 455 | 0.8377 | 0.7 | 0.4708 | 2.4611 | 0.7 | 0.6376 | 0.3173 | 0.1195 | | No log | 36.0 | 468 | 0.8126 | 0.69 | 0.4562 | 2.3909 | 0.69 | 0.6154 | 0.2433 | 0.1177 | | No log | 37.0 | 481 | 0.8299 | 0.685 | 0.4673 | 2.5695 | 0.685 | 0.6080 | 0.2802 | 0.1261 | | No log | 38.0 | 494 | 0.8197 | 0.685 | 0.4597 | 2.6388 | 0.685 | 0.6187 | 0.2690 | 0.1229 | | 0.9314 | 39.0 | 507 | 0.8137 | 0.695 | 0.4547 | 2.7263 | 0.695 | 0.6332 | 0.2581 | 0.1207 | | 0.9314 | 40.0 | 520 | 0.8168 | 0.69 | 0.4583 | 2.6230 | 0.69 | 0.6267 | 0.2696 | 0.1161 | | 0.9314 | 41.0 | 533 | 0.8090 | 0.7 | 0.4529 | 2.6449 | 0.7 | 0.6236 | 0.2445 | 0.1187 | | 0.9314 | 42.0 | 546 | 0.8168 | 0.68 | 0.4586 | 2.5516 | 0.68 | 0.6162 | 0.2722 | 0.1275 | | 0.9314 | 43.0 | 559 | 0.8100 | 0.7 | 0.4523 | 2.5565 | 0.7 | 0.6347 | 0.2869 | 0.1192 | | 0.9314 | 44.0 | 572 | 0.8078 | 0.7 | 0.4514 | 2.5734 | 0.7 | 0.6344 | 0.2583 | 0.1172 | | 0.9314 | 45.0 | 585 | 0.8022 | 0.715 | 0.4472 | 2.4971 | 0.715 | 0.6534 | 0.2890 | 0.1165 | | 0.9314 | 46.0 | 598 | 0.8049 | 0.695 | 0.4484 | 2.4891 | 0.695 | 0.6423 | 0.2722 | 0.1189 | | 0.9314 | 47.0 | 611 | 0.8025 | 0.705 | 0.4481 | 2.4929 | 0.705 | 0.6393 | 0.2650 | 0.1124 | | 0.9314 | 48.0 | 624 | 0.7973 | 0.7 | 0.4439 | 2.5000 | 0.7 | 0.6292 | 0.2718 | 0.1142 | | 0.9314 | 49.0 | 637 | 0.8011 | 0.7 | 0.4464 | 2.5713 | 0.7 | 0.6303 | 0.2400 | 0.1183 | | 0.9314 | 50.0 | 650 | 0.8012 | 0.7 | 0.4467 | 2.5682 | 0.7 | 0.6313 | 0.2684 | 0.1170 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8672 - Accuracy: 0.71 - Brier Loss: 0.4047 - Nll: 2.1924 - F1 Micro: 0.7100 - F1 Macro: 0.6463 - Ece: 0.2420 - Aurc: 0.1050 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 2.1239 | 0.16 | 0.8967 | 8.4233 | 0.16 | 0.1062 | 0.2101 | 0.8304 | | No log | 2.0 | 26 | 2.1201 | 0.14 | 0.8961 | 8.2220 | 0.14 | 0.0876 | 0.1970 | 0.8491 | | No log | 3.0 | 39 | 2.0724 | 0.215 | 0.8865 | 6.2039 | 0.2150 | 0.1169 | 0.2432 | 0.7837 | | No log | 4.0 | 52 | 2.0291 | 0.185 | 0.8773 | 5.6169 | 0.185 | 0.0792 | 0.2329 | 0.7651 | | No log | 5.0 | 65 | 1.9592 | 0.215 | 0.8614 | 6.0237 | 0.2150 | 0.0835 | 0.2493 | 0.7373 | | No log | 6.0 | 78 | 1.9039 | 0.205 | 0.8483 | 5.9575 | 0.205 | 0.0619 | 0.2493 | 0.7526 | | No log | 7.0 | 91 | 1.8651 | 0.26 | 0.8381 | 5.6215 | 0.26 | 0.1490 | 0.2663 | 0.6747 | | No log | 8.0 | 104 | 1.8342 | 0.225 | 0.8311 | 5.7631 | 0.225 | 0.1071 | 0.2425 | 0.6919 | | No log | 9.0 | 117 | 1.8057 | 0.31 | 0.8218 | 5.2969 | 0.31 | 0.2118 | 0.2795 | 0.6489 | | No log | 10.0 | 130 | 1.5737 | 0.46 | 0.7277 | 5.1748 | 0.46 | 0.2853 | 0.3279 | 0.2977 | | No log | 11.0 | 143 | 1.5629 | 0.415 | 0.7331 | 4.8259 | 0.415 | 0.2846 | 0.2924 | 0.3880 | | No log | 12.0 | 156 | 1.5283 | 0.45 | 0.7135 | 4.0012 | 0.45 | 0.3122 | 0.3298 | 0.3197 | | No log | 13.0 | 169 | 1.4200 | 0.51 | 0.6674 | 3.9849 | 0.51 | 0.3400 | 0.3259 | 0.2549 | | No log | 14.0 | 182 | 1.4334 | 0.535 | 0.6710 | 3.7006 | 0.535 | 0.3840 | 0.3291 | 0.2584 | | No log | 15.0 | 195 | 1.4306 | 0.45 | 0.6854 | 3.8260 | 0.45 | 0.3120 | 0.3055 | 0.4297 | | No log | 16.0 | 208 | 1.3175 | 0.585 | 0.6174 | 3.3484 | 0.585 | 0.4401 | 0.3406 | 0.1916 | | No log | 17.0 | 221 | 1.2680 | 0.57 | 0.5998 | 3.1408 | 0.57 | 0.4356 | 0.2903 | 0.2136 | | No log | 18.0 | 234 | 1.2605 | 0.58 | 0.6020 | 3.2085 | 0.58 | 0.4711 | 0.2915 | 0.2355 | | No log | 19.0 | 247 | 1.2292 | 0.61 | 0.5791 | 3.0633 | 0.61 | 0.5021 | 0.2929 | 0.2082 | | No log | 20.0 | 260 | 1.3872 | 0.54 | 0.6604 | 3.2778 | 0.54 | 0.4604 | 0.3284 | 0.3506 | | No log | 21.0 | 273 | 1.1646 | 0.625 | 0.5520 | 2.8539 | 0.625 | 0.5193 | 0.2828 | 0.1885 | | No log | 22.0 | 286 | 1.1565 | 0.655 | 0.5438 | 2.6915 | 0.655 | 0.5437 | 0.3430 | 0.1549 | | No log | 23.0 | 299 | 1.1041 | 0.625 | 0.5298 | 2.9930 | 0.625 | 0.5241 | 0.2423 | 0.1906 | | No log | 24.0 | 312 | 1.0448 | 0.685 | 0.4895 | 2.8196 | 0.685 | 0.5846 | 0.2701 | 0.1411 | | No log | 25.0 | 325 | 1.0623 | 0.695 | 0.4904 | 2.6903 | 0.695 | 0.6086 | 0.2762 | 0.1435 | | No log | 26.0 | 338 | 0.9872 | 0.695 | 0.4607 | 2.6336 | 0.695 | 0.5953 | 0.2728 | 0.1180 | | No log | 27.0 | 351 | 0.9789 | 0.705 | 0.4580 | 2.6326 | 0.705 | 0.6127 | 0.2579 | 0.1171 | | No log | 28.0 | 364 | 1.0033 | 0.685 | 0.4707 | 2.5747 | 0.685 | 0.5906 | 0.2747 | 0.1291 | | No log | 29.0 | 377 | 1.0152 | 0.7 | 0.4789 | 2.4333 | 0.7 | 0.6260 | 0.2951 | 0.1739 | | No log | 30.0 | 390 | 1.0107 | 0.715 | 0.4684 | 2.5194 | 0.715 | 0.6401 | 0.3197 | 0.1389 | | No log | 31.0 | 403 | 0.9511 | 0.69 | 0.4445 | 2.5648 | 0.69 | 0.6131 | 0.2648 | 0.1298 | | No log | 32.0 | 416 | 0.9586 | 0.735 | 0.4448 | 2.3342 | 0.735 | 0.6578 | 0.2941 | 0.1275 | | No log | 33.0 | 429 | 1.0010 | 0.73 | 0.4625 | 2.4748 | 0.7300 | 0.6613 | 0.3307 | 0.1202 | | No log | 34.0 | 442 | 0.9481 | 0.71 | 0.4361 | 2.4986 | 0.7100 | 0.6456 | 0.2856 | 0.1228 | | No log | 35.0 | 455 | 0.9190 | 0.69 | 0.4323 | 2.6586 | 0.69 | 0.6265 | 0.2538 | 0.1250 | | No log | 36.0 | 468 | 0.9226 | 0.715 | 0.4350 | 2.2652 | 0.715 | 0.6507 | 0.2868 | 0.1328 | | No log | 37.0 | 481 | 0.9017 | 0.725 | 0.4182 | 2.5141 | 0.7250 | 0.6590 | 0.2547 | 0.1013 | | No log | 38.0 | 494 | 0.9092 | 0.72 | 0.4218 | 2.5171 | 0.72 | 0.6495 | 0.2677 | 0.1055 | | 1.0958 | 39.0 | 507 | 0.9093 | 0.71 | 0.4221 | 2.6479 | 0.7100 | 0.6456 | 0.2567 | 0.1185 | | 1.0958 | 40.0 | 520 | 0.8926 | 0.71 | 0.4204 | 2.3785 | 0.7100 | 0.6522 | 0.2396 | 0.1153 | | 1.0958 | 41.0 | 533 | 0.8928 | 0.715 | 0.4157 | 2.5719 | 0.715 | 0.6487 | 0.2708 | 0.1067 | | 1.0958 | 42.0 | 546 | 0.8967 | 0.715 | 0.4247 | 2.6422 | 0.715 | 0.6495 | 0.2525 | 0.1174 | | 1.0958 | 43.0 | 559 | 0.8773 | 0.695 | 0.4116 | 2.5548 | 0.695 | 0.6400 | 0.2491 | 0.1142 | | 1.0958 | 44.0 | 572 | 0.8660 | 0.71 | 0.4036 | 2.2950 | 0.7100 | 0.6535 | 0.2401 | 0.1009 | | 1.0958 | 45.0 | 585 | 0.8718 | 0.72 | 0.4057 | 2.4922 | 0.72 | 0.6551 | 0.2624 | 0.0998 | | 1.0958 | 46.0 | 598 | 0.8737 | 0.7 | 0.4070 | 2.4455 | 0.7 | 0.6416 | 0.2360 | 0.1052 | | 1.0958 | 47.0 | 611 | 0.8707 | 0.715 | 0.4094 | 2.3519 | 0.715 | 0.6494 | 0.2514 | 0.1086 | | 1.0958 | 48.0 | 624 | 0.8640 | 0.705 | 0.4039 | 2.3765 | 0.705 | 0.6430 | 0.2538 | 0.1041 | | 1.0958 | 49.0 | 637 | 0.8702 | 0.7 | 0.4066 | 2.5524 | 0.7 | 0.6423 | 0.2160 | 0.1080 | | 1.0958 | 50.0 | 650 | 0.8672 | 0.71 | 0.4047 | 2.1924 | 0.7100 | 0.6463 | 0.2420 | 0.1050 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.5

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6454 - Accuracy: 0.685 - Brier Loss: 0.4931 - Nll: 2.5040 - F1 Micro: 0.685 - F1 Macro: 0.6171 - Ece: 0.2996 - Aurc: 0.1499 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.4475 | 0.17 | 0.8966 | 8.4781 | 0.17 | 0.1126 | 0.2169 | 0.8299 | | No log | 2.0 | 26 | 1.4360 | 0.165 | 0.8955 | 8.4118 | 0.165 | 0.1097 | 0.2115 | 0.8359 | | No log | 3.0 | 39 | 1.3776 | 0.16 | 0.8842 | 6.1685 | 0.16 | 0.0633 | 0.2066 | 0.7780 | | No log | 4.0 | 52 | 1.3085 | 0.2 | 0.8701 | 6.0521 | 0.2000 | 0.0728 | 0.2332 | 0.7424 | | No log | 5.0 | 65 | 1.2551 | 0.18 | 0.8597 | 6.1887 | 0.18 | 0.0491 | 0.2265 | 0.7890 | | No log | 6.0 | 78 | 1.2118 | 0.2 | 0.8489 | 6.1706 | 0.2000 | 0.0631 | 0.2324 | 0.7179 | | No log | 7.0 | 91 | 1.1759 | 0.19 | 0.8418 | 6.1310 | 0.19 | 0.0428 | 0.2384 | 0.7431 | | No log | 8.0 | 104 | 1.1577 | 0.195 | 0.8339 | 5.7305 | 0.195 | 0.0535 | 0.2399 | 0.7126 | | No log | 9.0 | 117 | 0.9905 | 0.34 | 0.7692 | 6.1092 | 0.34 | 0.1567 | 0.2772 | 0.4320 | | No log | 10.0 | 130 | 0.9603 | 0.355 | 0.7541 | 5.7998 | 0.3550 | 0.1969 | 0.3021 | 0.4111 | | No log | 11.0 | 143 | 1.0839 | 0.255 | 0.8087 | 5.1464 | 0.255 | 0.1242 | 0.2389 | 0.6769 | | No log | 12.0 | 156 | 0.9374 | 0.39 | 0.7410 | 4.8415 | 0.39 | 0.2220 | 0.3037 | 0.4194 | | No log | 13.0 | 169 | 0.9974 | 0.33 | 0.7720 | 4.9023 | 0.33 | 0.1732 | 0.2863 | 0.6049 | | No log | 14.0 | 182 | 0.9393 | 0.435 | 0.7251 | 4.3102 | 0.435 | 0.2455 | 0.3276 | 0.3645 | | No log | 15.0 | 195 | 0.9554 | 0.39 | 0.7416 | 4.0361 | 0.39 | 0.2535 | 0.2721 | 0.5075 | | No log | 16.0 | 208 | 0.8012 | 0.465 | 0.6445 | 4.0129 | 0.465 | 0.2935 | 0.2551 | 0.2839 | | No log | 17.0 | 221 | 0.8033 | 0.53 | 0.6418 | 3.4959 | 0.53 | 0.3816 | 0.3242 | 0.2458 | | No log | 18.0 | 234 | 0.7740 | 0.57 | 0.6204 | 3.4062 | 0.57 | 0.4297 | 0.3139 | 0.2245 | | No log | 19.0 | 247 | 0.7736 | 0.6 | 0.6124 | 3.3460 | 0.6 | 0.4408 | 0.3017 | 0.1919 | | No log | 20.0 | 260 | 0.9105 | 0.555 | 0.6919 | 3.2115 | 0.555 | 0.4524 | 0.3604 | 0.3099 | | No log | 21.0 | 273 | 0.7416 | 0.61 | 0.5948 | 3.1349 | 0.61 | 0.5093 | 0.3176 | 0.2233 | | No log | 22.0 | 286 | 0.7318 | 0.655 | 0.5815 | 3.1259 | 0.655 | 0.5433 | 0.3478 | 0.1672 | | No log | 23.0 | 299 | 0.7799 | 0.59 | 0.6079 | 3.0590 | 0.59 | 0.4963 | 0.3340 | 0.2455 | | No log | 24.0 | 312 | 0.7886 | 0.665 | 0.6038 | 2.9965 | 0.665 | 0.5575 | 0.3773 | 0.1623 | | No log | 25.0 | 325 | 0.7083 | 0.66 | 0.5602 | 3.0752 | 0.66 | 0.5582 | 0.3283 | 0.1772 | | No log | 26.0 | 338 | 0.6882 | 0.63 | 0.5507 | 2.9022 | 0.63 | 0.5404 | 0.2963 | 0.1851 | | No log | 27.0 | 351 | 0.6774 | 0.66 | 0.5348 | 2.7876 | 0.66 | 0.5674 | 0.3095 | 0.1662 | | No log | 28.0 | 364 | 0.8111 | 0.675 | 0.6067 | 2.7578 | 0.675 | 0.5800 | 0.3905 | 0.1923 | | No log | 29.0 | 377 | 0.6803 | 0.645 | 0.5338 | 2.8666 | 0.645 | 0.5486 | 0.3054 | 0.1646 | | No log | 30.0 | 390 | 0.6835 | 0.685 | 0.5336 | 2.5944 | 0.685 | 0.5840 | 0.3119 | 0.1595 | | No log | 31.0 | 403 | 0.6810 | 0.655 | 0.5309 | 2.7112 | 0.655 | 0.5625 | 0.2879 | 0.1786 | | No log | 32.0 | 416 | 0.6848 | 0.685 | 0.5194 | 2.6456 | 0.685 | 0.5893 | 0.3314 | 0.1350 | | No log | 33.0 | 429 | 0.6631 | 0.695 | 0.5063 | 2.6286 | 0.695 | 0.5980 | 0.3198 | 0.1314 | | No log | 34.0 | 442 | 0.6639 | 0.69 | 0.5126 | 2.3890 | 0.69 | 0.5834 | 0.2990 | 0.1376 | | No log | 35.0 | 455 | 0.6736 | 0.675 | 0.5172 | 2.3291 | 0.675 | 0.6014 | 0.3148 | 0.1646 | | No log | 36.0 | 468 | 0.6648 | 0.68 | 0.5137 | 2.4549 | 0.68 | 0.6156 | 0.3316 | 0.1492 | | No log | 37.0 | 481 | 0.6543 | 0.7 | 0.5006 | 2.4275 | 0.7 | 0.6130 | 0.3041 | 0.1342 | | No log | 38.0 | 494 | 0.6514 | 0.675 | 0.5001 | 2.4064 | 0.675 | 0.5984 | 0.2963 | 0.1491 | | 0.7462 | 39.0 | 507 | 0.6498 | 0.71 | 0.4988 | 2.5772 | 0.7100 | 0.6405 | 0.2980 | 0.1335 | | 0.7462 | 40.0 | 520 | 0.6496 | 0.705 | 0.4964 | 2.5649 | 0.705 | 0.6386 | 0.3060 | 0.1380 | | 0.7462 | 41.0 | 533 | 0.6562 | 0.68 | 0.5027 | 2.5816 | 0.68 | 0.6026 | 0.3100 | 0.1467 | | 0.7462 | 42.0 | 546 | 0.6632 | 0.68 | 0.5089 | 2.4570 | 0.68 | 0.6112 | 0.2989 | 0.1500 | | 0.7462 | 43.0 | 559 | 0.6437 | 0.7 | 0.4885 | 2.3648 | 0.7 | 0.6331 | 0.2741 | 0.1427 | | 0.7462 | 44.0 | 572 | 0.6435 | 0.705 | 0.4894 | 2.4253 | 0.705 | 0.6370 | 0.3043 | 0.1390 | | 0.7462 | 45.0 | 585 | 0.6457 | 0.695 | 0.4929 | 2.3611 | 0.695 | 0.6314 | 0.3021 | 0.1449 | | 0.7462 | 46.0 | 598 | 0.6437 | 0.695 | 0.4912 | 2.3639 | 0.695 | 0.6370 | 0.2984 | 0.1436 | | 0.7462 | 47.0 | 611 | 0.6466 | 0.685 | 0.4933 | 2.4859 | 0.685 | 0.6306 | 0.2936 | 0.1474 | | 0.7462 | 48.0 | 624 | 0.6470 | 0.67 | 0.4950 | 2.3782 | 0.67 | 0.6070 | 0.3139 | 0.1547 | | 0.7462 | 49.0 | 637 | 0.6477 | 0.675 | 0.4945 | 2.4509 | 0.675 | 0.6092 | 0.2852 | 0.1527 | | 0.7462 | 50.0 | 650 | 0.6454 | 0.685 | 0.4931 | 2.5040 | 0.685 | 0.6171 | 0.2996 | 0.1499 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7861 - Accuracy: 0.705 - Brier Loss: 0.4410 - Nll: 2.6519 - F1 Micro: 0.705 - F1 Macro: 0.6403 - Ece: 0.2724 - Aurc: 0.1188 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 1.7831 | 0.165 | 0.8966 | 8.4414 | 0.165 | 0.1121 | 0.2151 | 0.8335 | | No log | 2.0 | 26 | 1.7753 | 0.145 | 0.8958 | 8.5715 | 0.145 | 0.0954 | 0.1998 | 0.8332 | | No log | 3.0 | 39 | 1.7334 | 0.175 | 0.8877 | 6.4682 | 0.175 | 0.0756 | 0.2069 | 0.7896 | | No log | 4.0 | 52 | 1.6604 | 0.185 | 0.8723 | 6.0351 | 0.185 | 0.0505 | 0.2328 | 0.7549 | | No log | 5.0 | 65 | 1.5874 | 0.18 | 0.8560 | 6.0732 | 0.18 | 0.0431 | 0.2285 | 0.7506 | | No log | 6.0 | 78 | 1.5223 | 0.185 | 0.8415 | 6.1638 | 0.185 | 0.0479 | 0.2419 | 0.7530 | | No log | 7.0 | 91 | 1.4642 | 0.35 | 0.8239 | 6.0328 | 0.35 | 0.1696 | 0.3081 | 0.5219 | | No log | 8.0 | 104 | 1.3599 | 0.35 | 0.7825 | 6.2102 | 0.35 | 0.1908 | 0.2977 | 0.4172 | | No log | 9.0 | 117 | 1.3083 | 0.385 | 0.7566 | 5.7128 | 0.3850 | 0.2203 | 0.3012 | 0.3842 | | No log | 10.0 | 130 | 1.3151 | 0.365 | 0.7670 | 5.1073 | 0.3650 | 0.2150 | 0.2923 | 0.4891 | | No log | 11.0 | 143 | 1.3736 | 0.295 | 0.7950 | 5.3584 | 0.295 | 0.1747 | 0.2716 | 0.6360 | | No log | 12.0 | 156 | 1.2655 | 0.425 | 0.7380 | 4.0312 | 0.425 | 0.2789 | 0.3273 | 0.3366 | | No log | 13.0 | 169 | 1.1696 | 0.475 | 0.6901 | 3.9627 | 0.4750 | 0.3083 | 0.3011 | 0.2825 | | No log | 14.0 | 182 | 1.2992 | 0.355 | 0.7473 | 3.9098 | 0.3550 | 0.2292 | 0.2675 | 0.4929 | | No log | 15.0 | 195 | 1.1698 | 0.51 | 0.6881 | 3.7143 | 0.51 | 0.3691 | 0.3333 | 0.3278 | | No log | 16.0 | 208 | 1.0624 | 0.515 | 0.6274 | 3.8387 | 0.515 | 0.3631 | 0.2821 | 0.2583 | | No log | 17.0 | 221 | 1.0970 | 0.565 | 0.6421 | 3.3302 | 0.565 | 0.4493 | 0.3362 | 0.2373 | | No log | 18.0 | 234 | 1.0029 | 0.625 | 0.5883 | 3.3820 | 0.625 | 0.4675 | 0.3005 | 0.1660 | | No log | 19.0 | 247 | 1.0384 | 0.605 | 0.6093 | 3.3183 | 0.605 | 0.4863 | 0.3252 | 0.2145 | | No log | 20.0 | 260 | 1.0686 | 0.62 | 0.6234 | 3.0246 | 0.62 | 0.5155 | 0.3625 | 0.2334 | | No log | 21.0 | 273 | 0.9641 | 0.62 | 0.5685 | 2.9225 | 0.62 | 0.5259 | 0.3103 | 0.2063 | | No log | 22.0 | 286 | 1.0054 | 0.665 | 0.5849 | 3.0792 | 0.665 | 0.5614 | 0.3636 | 0.1863 | | No log | 23.0 | 299 | 0.9959 | 0.675 | 0.5734 | 2.9829 | 0.675 | 0.5577 | 0.3619 | 0.1806 | | No log | 24.0 | 312 | 0.9044 | 0.675 | 0.5267 | 2.8952 | 0.675 | 0.5712 | 0.2989 | 0.1475 | | No log | 25.0 | 325 | 0.9803 | 0.655 | 0.5627 | 2.7501 | 0.655 | 0.5418 | 0.3415 | 0.1919 | | No log | 26.0 | 338 | 0.8814 | 0.65 | 0.5176 | 2.8421 | 0.65 | 0.5619 | 0.2665 | 0.1694 | | No log | 27.0 | 351 | 0.8555 | 0.69 | 0.4928 | 2.7870 | 0.69 | 0.5831 | 0.3091 | 0.1279 | | No log | 28.0 | 364 | 0.8290 | 0.69 | 0.4777 | 2.6377 | 0.69 | 0.5976 | 0.2551 | 0.1290 | | No log | 29.0 | 377 | 0.8593 | 0.685 | 0.4949 | 2.5880 | 0.685 | 0.5776 | 0.3083 | 0.1279 | | No log | 30.0 | 390 | 0.8226 | 0.685 | 0.4678 | 2.8938 | 0.685 | 0.5884 | 0.2820 | 0.1249 | | No log | 31.0 | 403 | 0.8578 | 0.69 | 0.4857 | 2.6150 | 0.69 | 0.6024 | 0.3109 | 0.1344 | | No log | 32.0 | 416 | 0.8330 | 0.685 | 0.4753 | 2.5999 | 0.685 | 0.6047 | 0.2688 | 0.1407 | | No log | 33.0 | 429 | 0.8268 | 0.7 | 0.4683 | 2.6138 | 0.7 | 0.6193 | 0.2913 | 0.1315 | | No log | 34.0 | 442 | 0.8535 | 0.715 | 0.4749 | 2.5059 | 0.715 | 0.6450 | 0.2931 | 0.1190 | | No log | 35.0 | 455 | 0.8334 | 0.665 | 0.4752 | 2.3839 | 0.665 | 0.5950 | 0.2762 | 0.1397 | | No log | 36.0 | 468 | 0.8025 | 0.71 | 0.4553 | 2.4803 | 0.7100 | 0.6302 | 0.2889 | 0.1178 | | No log | 37.0 | 481 | 0.8142 | 0.715 | 0.4563 | 2.6785 | 0.715 | 0.6426 | 0.2989 | 0.1048 | | No log | 38.0 | 494 | 0.8124 | 0.7 | 0.4538 | 2.5320 | 0.7 | 0.6332 | 0.2594 | 0.1132 | | 0.9303 | 39.0 | 507 | 0.7888 | 0.69 | 0.4452 | 2.6427 | 0.69 | 0.6269 | 0.2583 | 0.1224 | | 0.9303 | 40.0 | 520 | 0.7907 | 0.705 | 0.4458 | 2.6942 | 0.705 | 0.6367 | 0.2688 | 0.1155 | | 0.9303 | 41.0 | 533 | 0.7918 | 0.71 | 0.4442 | 2.4378 | 0.7100 | 0.6558 | 0.2816 | 0.1132 | | 0.9303 | 42.0 | 546 | 0.8005 | 0.725 | 0.4479 | 2.6088 | 0.7250 | 0.6576 | 0.2914 | 0.1049 | | 0.9303 | 43.0 | 559 | 0.7879 | 0.72 | 0.4421 | 2.7052 | 0.72 | 0.6592 | 0.2741 | 0.1122 | | 0.9303 | 44.0 | 572 | 0.7910 | 0.71 | 0.4461 | 2.6463 | 0.7100 | 0.6463 | 0.3119 | 0.1188 | | 0.9303 | 45.0 | 585 | 0.7922 | 0.705 | 0.4450 | 2.6453 | 0.705 | 0.6481 | 0.2753 | 0.1211 | | 0.9303 | 46.0 | 598 | 0.7915 | 0.715 | 0.4429 | 2.6970 | 0.715 | 0.6526 | 0.2741 | 0.1107 | | 0.9303 | 47.0 | 611 | 0.7809 | 0.705 | 0.4370 | 2.6841 | 0.705 | 0.6453 | 0.2734 | 0.1158 | | 0.9303 | 48.0 | 624 | 0.7771 | 0.705 | 0.4350 | 2.6168 | 0.705 | 0.6423 | 0.2652 | 0.1139 | | 0.9303 | 49.0 | 637 | 0.7826 | 0.705 | 0.4377 | 2.5091 | 0.705 | 0.6423 | 0.2758 | 0.1202 | | 0.9303 | 50.0 | 650 | 0.7861 | 0.705 | 0.4410 | 2.6519 | 0.705 | 0.6403 | 0.2724 | 0.1188 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

dima806/man_woman_face_image_detection

Returns with about 98.7% accuracy whether the face belongs to man or woman based on face image. See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/t9MgehgAAZEJAXOebhfjO.png) ``` Classification report: precision recall f1-score support man 0.9885 0.9857 0.9871 51062 woman 0.9857 0.9885 0.9871 51062 accuracy 0.9871 102124 macro avg 0.9871 0.9871 0.9871 102124 weighted avg 0.9871 0.9871 0.9871 102124 ```

[ "man", "woman" ]

bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8809 - Accuracy: 0.7 - Brier Loss: 0.4126 - Nll: 2.4279 - F1 Micro: 0.7 - F1 Macro: 0.6279 - Ece: 0.2569 - Aurc: 0.1111 ## 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: 64 - eval_batch_size: 64 - 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 | 13 | 2.1185 | 0.165 | 0.8967 | 8.5399 | 0.165 | 0.1130 | 0.2151 | 0.8331 | | No log | 2.0 | 26 | 2.1127 | 0.13 | 0.8958 | 8.1152 | 0.13 | 0.0842 | 0.1816 | 0.8392 | | No log | 3.0 | 39 | 2.0781 | 0.165 | 0.8888 | 6.8828 | 0.165 | 0.0878 | 0.2150 | 0.8082 | | No log | 4.0 | 52 | 2.0197 | 0.22 | 0.8762 | 5.7578 | 0.22 | 0.1155 | 0.2521 | 0.7521 | | No log | 5.0 | 65 | 1.9499 | 0.205 | 0.8601 | 6.0641 | 0.205 | 0.0951 | 0.2567 | 0.7355 | | No log | 6.0 | 78 | 1.9019 | 0.25 | 0.8483 | 5.8930 | 0.25 | 0.1178 | 0.2728 | 0.6862 | | No log | 7.0 | 91 | 1.8252 | 0.28 | 0.8301 | 5.8062 | 0.28 | 0.1660 | 0.2890 | 0.6982 | | No log | 8.0 | 104 | 1.8194 | 0.28 | 0.8275 | 5.2642 | 0.28 | 0.1625 | 0.2874 | 0.6935 | | No log | 9.0 | 117 | 1.7671 | 0.355 | 0.8109 | 5.1326 | 0.3550 | 0.2211 | 0.3018 | 0.5678 | | No log | 10.0 | 130 | 1.6582 | 0.355 | 0.7774 | 5.2226 | 0.3550 | 0.2200 | 0.2991 | 0.5305 | | No log | 11.0 | 143 | 1.5849 | 0.395 | 0.7422 | 5.0239 | 0.395 | 0.2436 | 0.2979 | 0.3974 | | No log | 12.0 | 156 | 1.4908 | 0.46 | 0.7001 | 4.2790 | 0.46 | 0.3169 | 0.3091 | 0.3003 | | No log | 13.0 | 169 | 1.6016 | 0.395 | 0.7496 | 4.2149 | 0.395 | 0.2793 | 0.2929 | 0.4640 | | No log | 14.0 | 182 | 1.4714 | 0.475 | 0.6971 | 4.0742 | 0.4750 | 0.3299 | 0.3177 | 0.3613 | | No log | 15.0 | 195 | 1.5007 | 0.46 | 0.7119 | 3.8252 | 0.46 | 0.3145 | 0.3111 | 0.3954 | | No log | 16.0 | 208 | 1.4352 | 0.515 | 0.6776 | 3.4028 | 0.515 | 0.3948 | 0.3376 | 0.2993 | | No log | 17.0 | 221 | 1.2890 | 0.575 | 0.6104 | 3.4453 | 0.575 | 0.4478 | 0.2940 | 0.2119 | | No log | 18.0 | 234 | 1.2190 | 0.595 | 0.5719 | 3.2413 | 0.595 | 0.4662 | 0.2608 | 0.1981 | | No log | 19.0 | 247 | 1.2287 | 0.59 | 0.5764 | 3.2303 | 0.59 | 0.4857 | 0.2811 | 0.2020 | | No log | 20.0 | 260 | 1.1726 | 0.64 | 0.5494 | 2.9544 | 0.64 | 0.5307 | 0.2993 | 0.1708 | | No log | 21.0 | 273 | 1.1305 | 0.61 | 0.5384 | 2.9557 | 0.61 | 0.5170 | 0.2771 | 0.1949 | | No log | 22.0 | 286 | 1.1256 | 0.645 | 0.5295 | 2.7934 | 0.645 | 0.5381 | 0.3181 | 0.1629 | | No log | 23.0 | 299 | 1.1209 | 0.645 | 0.5217 | 2.8697 | 0.645 | 0.5432 | 0.3055 | 0.1687 | | No log | 24.0 | 312 | 1.2513 | 0.685 | 0.5917 | 2.7262 | 0.685 | 0.5639 | 0.3779 | 0.1833 | | No log | 25.0 | 325 | 1.0321 | 0.695 | 0.4819 | 2.7202 | 0.695 | 0.5896 | 0.2810 | 0.1280 | | No log | 26.0 | 338 | 1.0405 | 0.645 | 0.4957 | 2.6116 | 0.645 | 0.5661 | 0.2515 | 0.1700 | | No log | 27.0 | 351 | 1.0580 | 0.695 | 0.4933 | 2.7436 | 0.695 | 0.5996 | 0.2967 | 0.1339 | | No log | 28.0 | 364 | 0.9740 | 0.65 | 0.4575 | 2.5682 | 0.65 | 0.5731 | 0.2513 | 0.1384 | | No log | 29.0 | 377 | 0.9934 | 0.695 | 0.4651 | 2.5753 | 0.695 | 0.6108 | 0.2775 | 0.1171 | | No log | 30.0 | 390 | 0.9900 | 0.645 | 0.4695 | 2.6280 | 0.645 | 0.5668 | 0.2459 | 0.1558 | | No log | 31.0 | 403 | 0.9671 | 0.695 | 0.4504 | 2.8174 | 0.695 | 0.6094 | 0.2505 | 0.1188 | | No log | 32.0 | 416 | 0.9327 | 0.715 | 0.4324 | 2.5285 | 0.715 | 0.6415 | 0.2565 | 0.1086 | | No log | 33.0 | 429 | 0.9628 | 0.71 | 0.4464 | 2.5876 | 0.7100 | 0.6435 | 0.2709 | 0.1152 | | No log | 34.0 | 442 | 0.9316 | 0.715 | 0.4353 | 2.7111 | 0.715 | 0.6334 | 0.2361 | 0.1078 | | No log | 35.0 | 455 | 0.9275 | 0.7 | 0.4364 | 2.5226 | 0.7 | 0.6251 | 0.2586 | 0.1207 | | No log | 36.0 | 468 | 0.9301 | 0.7 | 0.4346 | 2.6464 | 0.7 | 0.6232 | 0.2482 | 0.1142 | | No log | 37.0 | 481 | 0.9013 | 0.695 | 0.4194 | 2.5575 | 0.695 | 0.6197 | 0.2554 | 0.1098 | | No log | 38.0 | 494 | 0.9008 | 0.695 | 0.4196 | 2.6270 | 0.695 | 0.6156 | 0.2246 | 0.1063 | | 1.0903 | 39.0 | 507 | 0.9185 | 0.71 | 0.4311 | 2.6290 | 0.7100 | 0.6362 | 0.2626 | 0.1165 | | 1.0903 | 40.0 | 520 | 0.9053 | 0.685 | 0.4254 | 2.5057 | 0.685 | 0.6239 | 0.2210 | 0.1171 | | 1.0903 | 41.0 | 533 | 0.8955 | 0.7 | 0.4189 | 2.4823 | 0.7 | 0.6291 | 0.1995 | 0.1103 | | 1.0903 | 42.0 | 546 | 0.9012 | 0.69 | 0.4223 | 2.5377 | 0.69 | 0.6195 | 0.2486 | 0.1119 | | 1.0903 | 43.0 | 559 | 0.8894 | 0.71 | 0.4138 | 2.6167 | 0.7100 | 0.6382 | 0.2459 | 0.1022 | | 1.0903 | 44.0 | 572 | 0.8846 | 0.695 | 0.4132 | 2.5130 | 0.695 | 0.6265 | 0.2198 | 0.1093 | | 1.0903 | 45.0 | 585 | 0.8946 | 0.69 | 0.4190 | 2.6357 | 0.69 | 0.6230 | 0.2375 | 0.1145 | | 1.0903 | 46.0 | 598 | 0.8931 | 0.705 | 0.4168 | 2.6306 | 0.705 | 0.6342 | 0.2555 | 0.1102 | | 1.0903 | 47.0 | 611 | 0.8842 | 0.71 | 0.4160 | 2.3021 | 0.7100 | 0.6347 | 0.2096 | 0.1120 | | 1.0903 | 48.0 | 624 | 0.8805 | 0.695 | 0.4140 | 2.3447 | 0.695 | 0.6237 | 0.2181 | 0.1128 | | 1.0903 | 49.0 | 637 | 0.8816 | 0.7 | 0.4142 | 2.4358 | 0.7 | 0.6295 | 0.2550 | 0.1112 | | 1.0903 | 50.0 | 650 | 0.8809 | 0.7 | 0.4126 | 2.4279 | 0.7 | 0.6279 | 0.2569 | 0.1111 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3

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

yfh/food

# food This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6313 - eval_accuracy: 0.856 - eval_runtime: 739.9774 - eval_samples_per_second: 1.351 - eval_steps_per_second: 0.085 - epoch: 0.15 - step: 38 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "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" ]

zkdeng/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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2188 - Accuracy: 0.92 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6077 | 0.96 | 12 | 0.3408 | 0.895 | | 0.3469 | 2.0 | 25 | 0.2188 | 0.92 | | 0.2627 | 2.88 | 36 | 0.2183 | 0.915 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]

zkdeng/swin-tiny-patch4-window7-224-finetuned-black_widow

# swin-tiny-patch4-window7-224-finetuned-black_widow This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1422 - Accuracy: 0.945 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1728 | 0.96 | 12 | 0.1506 | 0.935 | | 0.1408 | 2.0 | 25 | 0.1422 | 0.945 | | 0.1669 | 2.96 | 37 | 0.1289 | 0.945 | | 0.1618 | 4.0 | 50 | 0.1126 | 0.945 | | 0.1383 | 4.8 | 60 | 0.1200 | 0.94 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]

aichoux/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: 0.3209 - Accuracy: 0.8902 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 8 | 2.7448 | 0.0314 | | 2.7716 | 2.0 | 16 | 2.5834 | 0.1765 | | 2.5974 | 3.0 | 24 | 2.3608 | 0.3020 | | 2.3426 | 4.0 | 32 | 2.1157 | 0.3333 | | 1.9747 | 5.0 | 40 | 1.7539 | 0.4627 | | 1.9747 | 6.0 | 48 | 1.3641 | 0.6078 | | 1.5182 | 7.0 | 56 | 1.0755 | 0.6471 | | 1.198 | 8.0 | 64 | 0.8743 | 0.7216 | | 1.0206 | 9.0 | 72 | 0.7666 | 0.7294 | | 0.8731 | 10.0 | 80 | 0.7035 | 0.7490 | | 0.8731 | 11.0 | 88 | 0.6122 | 0.7608 | | 0.7938 | 12.0 | 96 | 0.6508 | 0.7490 | | 0.7286 | 13.0 | 104 | 0.5081 | 0.7961 | | 0.659 | 14.0 | 112 | 0.5536 | 0.7961 | | 0.6232 | 15.0 | 120 | 0.5079 | 0.8 | | 0.6232 | 16.0 | 128 | 0.4483 | 0.8314 | | 0.6028 | 17.0 | 136 | 0.4096 | 0.8157 | | 0.5333 | 18.0 | 144 | 0.3710 | 0.8510 | | 0.5053 | 19.0 | 152 | 0.4810 | 0.8039 | | 0.4717 | 20.0 | 160 | 0.4121 | 0.8235 | | 0.4717 | 21.0 | 168 | 0.4021 | 0.8392 | | 0.4728 | 22.0 | 176 | 0.3780 | 0.8588 | | 0.4347 | 23.0 | 184 | 0.3374 | 0.8745 | | 0.4545 | 24.0 | 192 | 0.4056 | 0.8431 | | 0.3954 | 25.0 | 200 | 0.4088 | 0.8745 | | 0.3954 | 26.0 | 208 | 0.4169 | 0.8392 | | 0.4145 | 27.0 | 216 | 0.3262 | 0.8706 | | 0.3895 | 28.0 | 224 | 0.4235 | 0.8706 | | 0.4185 | 29.0 | 232 | 0.3482 | 0.8706 | | 0.3686 | 30.0 | 240 | 0.3088 | 0.8824 | | 0.3686 | 31.0 | 248 | 0.3230 | 0.8902 | | 0.3617 | 32.0 | 256 | 0.3473 | 0.8824 | | 0.3136 | 33.0 | 264 | 0.3793 | 0.8627 | | 0.3482 | 34.0 | 272 | 0.3477 | 0.8588 | | 0.3519 | 35.0 | 280 | 0.3692 | 0.8667 | | 0.3519 | 36.0 | 288 | 0.3611 | 0.8627 | | 0.3311 | 37.0 | 296 | 0.3233 | 0.8745 | | 0.3222 | 38.0 | 304 | 0.3416 | 0.8627 | | 0.3013 | 39.0 | 312 | 0.3198 | 0.8824 | | 0.2871 | 40.0 | 320 | 0.3308 | 0.8667 | | 0.2871 | 41.0 | 328 | 0.3246 | 0.8667 | | 0.3154 | 42.0 | 336 | 0.3943 | 0.8667 | | 0.2735 | 43.0 | 344 | 0.3186 | 0.8784 | | 0.2911 | 44.0 | 352 | 0.3132 | 0.8824 | | 0.266 | 45.0 | 360 | 0.3204 | 0.8980 | | 0.266 | 46.0 | 368 | 0.3097 | 0.8784 | | 0.2686 | 47.0 | 376 | 0.3075 | 0.8902 | | 0.2818 | 48.0 | 384 | 0.3192 | 0.8902 | | 0.2492 | 49.0 | 392 | 0.3434 | 0.8745 | | 0.276 | 50.0 | 400 | 0.3237 | 0.8824 | | 0.276 | 51.0 | 408 | 0.3450 | 0.8745 | | 0.245 | 52.0 | 416 | 0.3284 | 0.8706 | | 0.2292 | 53.0 | 424 | 0.3263 | 0.8902 | | 0.2252 | 54.0 | 432 | 0.3216 | 0.8745 | | 0.2483 | 55.0 | 440 | 0.3359 | 0.8863 | | 0.2483 | 56.0 | 448 | 0.3314 | 0.8902 | | 0.2549 | 57.0 | 456 | 0.3932 | 0.8745 | | 0.2247 | 58.0 | 464 | 0.3189 | 0.8745 | | 0.2344 | 59.0 | 472 | 0.3251 | 0.8745 | | 0.2315 | 60.0 | 480 | 0.3289 | 0.8824 | | 0.2315 | 61.0 | 488 | 0.3058 | 0.8745 | | 0.2109 | 62.0 | 496 | 0.2999 | 0.8863 | | 0.2325 | 63.0 | 504 | 0.3078 | 0.8980 | | 0.2126 | 64.0 | 512 | 0.3531 | 0.8784 | | 0.1975 | 65.0 | 520 | 0.3394 | 0.8902 | | 0.1975 | 66.0 | 528 | 0.3113 | 0.8902 | | 0.1998 | 67.0 | 536 | 0.3365 | 0.8941 | | 0.2208 | 68.0 | 544 | 0.2854 | 0.9020 | | 0.2126 | 69.0 | 552 | 0.3170 | 0.8941 | | 0.2352 | 70.0 | 560 | 0.3155 | 0.8824 | | 0.2352 | 71.0 | 568 | 0.3327 | 0.8824 | | 0.1724 | 72.0 | 576 | 0.3503 | 0.8902 | | 0.2038 | 73.0 | 584 | 0.3309 | 0.8824 | | 0.1919 | 74.0 | 592 | 0.3299 | 0.8902 | | 0.2199 | 75.0 | 600 | 0.3347 | 0.8863 | | 0.2199 | 76.0 | 608 | 0.3471 | 0.8824 | | 0.2075 | 77.0 | 616 | 0.3437 | 0.8863 | | 0.2206 | 78.0 | 624 | 0.3161 | 0.8824 | | 0.1655 | 79.0 | 632 | 0.3227 | 0.8784 | | 0.1765 | 80.0 | 640 | 0.3302 | 0.8784 | | 0.1765 | 81.0 | 648 | 0.3153 | 0.8745 | | 0.1832 | 82.0 | 656 | 0.3010 | 0.8745 | | 0.185 | 83.0 | 664 | 0.3266 | 0.8941 | | 0.1627 | 84.0 | 672 | 0.3192 | 0.8941 | | 0.176 | 85.0 | 680 | 0.3125 | 0.8863 | | 0.176 | 86.0 | 688 | 0.3241 | 0.8745 | | 0.1723 | 87.0 | 696 | 0.3124 | 0.8784 | | 0.1477 | 88.0 | 704 | 0.3109 | 0.8745 | | 0.1703 | 89.0 | 712 | 0.3196 | 0.8824 | | 0.1919 | 90.0 | 720 | 0.3186 | 0.8980 | | 0.1919 | 91.0 | 728 | 0.3178 | 0.8902 | | 0.1465 | 92.0 | 736 | 0.3241 | 0.8824 | | 0.155 | 93.0 | 744 | 0.3281 | 0.8784 | | 0.1829 | 94.0 | 752 | 0.3263 | 0.8824 | | 0.167 | 95.0 | 760 | 0.3282 | 0.8824 | | 0.167 | 96.0 | 768 | 0.3290 | 0.8824 | | 0.166 | 97.0 | 776 | 0.3253 | 0.8902 | | 0.1756 | 98.0 | 784 | 0.3231 | 0.8863 | | 0.157 | 99.0 | 792 | 0.3215 | 0.8902 | | 0.1492 | 100.0 | 800 | 0.3209 | 0.8902 | ### Framework versions - Transformers 4.33.3 - Pytorch 1.11.0+cu113 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "beijing", "biaoend", "biaomiddle", "biaoout", "biaoxie", "biaozheng", "dazhe", "kaishi", "loufeng", "panend", "panout", "panxie", "panzheng", "weibu" ]

GayatriC/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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

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

DamarJati/Face-Mask-Detection

# Face-Mask-Detection 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: 0.0051 - Accuracy: 0.9992 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0344 | 1.0 | 83 | 0.0051 | 0.9992 | | 0.0112 | 2.0 | 166 | 0.0052 | 0.9983 | | 0.0146 | 3.0 | 249 | 0.0045 | 0.9992 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "withmask", "withoutmask" ]

navradio/swin-tiny-patch4-window7-224-PE

# swin-tiny-patch4-window7-224-PE 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: 0.4489 - Accuracy: 0.7980 ## 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: 0.0025 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6872 | 1.0 | 11 | 0.6535 | 0.6061 | | 0.7287 | 2.0 | 22 | 0.6601 | 0.6397 | | 0.7212 | 3.0 | 33 | 0.6740 | 0.5657 | | 0.6947 | 4.0 | 44 | 0.6531 | 0.6532 | | 0.6783 | 5.0 | 55 | 0.6739 | 0.5724 | | 0.6816 | 6.0 | 66 | 0.6274 | 0.6599 | | 0.6428 | 7.0 | 77 | 0.6671 | 0.6330 | | 0.6928 | 8.0 | 88 | 0.6380 | 0.6498 | | 0.6767 | 9.0 | 99 | 0.6875 | 0.6061 | | 0.6918 | 10.0 | 110 | 0.6859 | 0.5690 | | 0.6845 | 11.0 | 121 | 0.6810 | 0.5657 | | 0.6826 | 12.0 | 132 | 0.6919 | 0.5185 | | 0.6877 | 13.0 | 143 | 0.6693 | 0.6061 | | 0.6709 | 14.0 | 154 | 0.6660 | 0.5690 | | 0.6707 | 15.0 | 165 | 0.6764 | 0.5690 | | 0.6703 | 16.0 | 176 | 0.6467 | 0.6296 | | 0.6629 | 17.0 | 187 | 0.6471 | 0.6431 | | 0.6557 | 18.0 | 198 | 0.6597 | 0.6229 | | 0.659 | 19.0 | 209 | 0.6451 | 0.6027 | | 0.65 | 20.0 | 220 | 0.6638 | 0.6094 | | 0.6453 | 21.0 | 231 | 0.6544 | 0.6162 | | 0.6426 | 22.0 | 242 | 0.6565 | 0.5825 | | 0.6339 | 23.0 | 253 | 0.6743 | 0.6296 | | 0.6236 | 24.0 | 264 | 0.6669 | 0.5960 | | 0.6427 | 25.0 | 275 | 0.6379 | 0.6532 | | 0.6439 | 26.0 | 286 | 0.6361 | 0.6263 | | 0.6212 | 27.0 | 297 | 0.6540 | 0.6465 | | 0.6186 | 28.0 | 308 | 0.5925 | 0.6700 | | 0.6162 | 29.0 | 319 | 0.6224 | 0.6734 | | 0.6237 | 30.0 | 330 | 0.6018 | 0.6667 | | 0.6061 | 31.0 | 341 | 0.5735 | 0.6801 | | 0.6138 | 32.0 | 352 | 0.6425 | 0.6566 | | 0.595 | 33.0 | 363 | 0.5827 | 0.6768 | | 0.5869 | 34.0 | 374 | 0.5956 | 0.7172 | | 0.577 | 35.0 | 385 | 0.5458 | 0.7003 | | 0.5766 | 36.0 | 396 | 0.5603 | 0.6869 | | 0.5726 | 37.0 | 407 | 0.5339 | 0.7340 | | 0.5702 | 38.0 | 418 | 0.5577 | 0.7138 | | 0.5762 | 39.0 | 429 | 0.5262 | 0.7374 | | 0.5543 | 40.0 | 440 | 0.5091 | 0.7441 | | 0.5339 | 41.0 | 451 | 0.5185 | 0.7542 | | 0.5428 | 42.0 | 462 | 0.5023 | 0.7542 | | 0.5349 | 43.0 | 473 | 0.5439 | 0.7306 | | 0.5319 | 44.0 | 484 | 0.4745 | 0.7811 | | 0.5294 | 45.0 | 495 | 0.5432 | 0.7172 | | 0.5314 | 46.0 | 506 | 0.4511 | 0.7912 | | 0.5073 | 47.0 | 517 | 0.4379 | 0.8047 | | 0.5028 | 48.0 | 528 | 0.4487 | 0.7980 | | 0.4985 | 49.0 | 539 | 0.4550 | 0.7946 | | 0.4826 | 50.0 | 550 | 0.4489 | 0.7980 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "cm", "non_cm" ]

fahmindra/activity_classification

# activity_classification 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: - Loss: 0.7087 - Accuracy: 0.8012 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7167 | 1.0 | 157 | 1.6188 | 0.6964 | | 1.0511 | 2.0 | 315 | 1.0981 | 0.7381 | | 0.9184 | 3.0 | 472 | 0.9225 | 0.7710 | | 0.7396 | 4.0 | 630 | 0.8333 | 0.7802 | | 0.6873 | 5.0 | 787 | 0.7917 | 0.7849 | | 0.6579 | 6.0 | 945 | 0.7510 | 0.7845 | | 0.5857 | 7.0 | 1102 | 0.7672 | 0.7845 | | 0.4968 | 8.0 | 1260 | 0.7467 | 0.7857 | | 0.513 | 9.0 | 1417 | 0.7156 | 0.7940 | | 0.4957 | 9.97 | 1570 | 0.7073 | 0.8024 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]

awrysfab/human_action_classification

# human_action_classification 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: - Loss: 3.3689 - Accuracy: 0.0728 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3354 | 1.0 | 197 | 2.9994 | 0.0717 | | 0.9519 | 2.0 | 394 | 3.3635 | 0.0778 | | 0.8178 | 3.0 | 591 | 3.5103 | 0.0763 | | 0.7122 | 4.0 | 788 | 3.7261 | 0.0683 | | 0.7532 | 5.0 | 985 | 3.7279 | 0.0661 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]

michaelsinanta/smoke_detector

# smoke_detector 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 smokedataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0187 - Accuracy: 0.9951 ## 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 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1404 | 1.0 | 716 | 0.0396 | 0.9902 | | 0.0493 | 2.0 | 1432 | 0.0337 | 0.9920 | | 0.0237 | 3.0 | 2148 | 0.0263 | 0.9934 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "cloud", "other", "smoke" ]

mmuratarat/kvasir-v2-classifier

This repo contains the artifacts of a model, using a pre-trained Visual Transformer model and fine-tuning it on a custom dataset. There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch 🤗 Hugging Face Transformers provides access to thousands of pretrained models for a wide range of tasks. When you use a pretrained model, you train it on a dataset specific to your task. This is known as fine-tuning, an incredibly powerful training technique. ## Dataset Here, we use Kvasir dataset v2. It consists of images, annotated and verified by medical doctors (experienced endoscopists), including several classes showing anatomical landmarks, phatological findings or endoscopic procedures in the gastrointestinal tract It is a multi-class dataset consisting of 1,000 images per class with a total of 8,000 images for eight different classes. These classes consist of pathological findings (esophagitis, polyps, ulcerative colitis), anatomical landmarks (z-line, pylorus, cecum), and normal and regular findings (normal colon mucosa, stool), and polyp removal cases (dyed and lifted polyps, dyed resection margins) The dataset can be download from [here](https://datasets.simula.no/kvasir/) which weights around ~2.3 GB. and is free for research and educational purposes only. ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/kvasir_v2_examples.png?raw=true) ## Model The [Hugging Face transformers](https://huggingface.co/docs/transformers/index) package is a very popular Python library which provides access to the HuggingFace Hub where we can find a lot of pretrained models and pipelines for a variety of tasks in domains such as Natural Language Processing (NLP), Computer Vision (CV) or Automatic Speech Recognition (ASR). A Vision Transformer-based model is used in this experiment. Vision Transformer (ViT) was introduced in June 2021 by a team of researchers at Google Brain (https://arxiv.org/abs/2010.11929). The Vision Transformer (ViT) model was proposed in "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. It’s the first paper that successfully trains a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/vit_architecture.jpg?raw=true) In this case, we'll be using [the google/vit-base-patch16-224-in21k model](https://huggingface.co/google/vit-base-patch16-224-in21k) from Hugging Face. ## Training hyperparameters The following hyperparameters were used during training: * learning_rate: 2e-5 * lr_scheduler_type: linear * warmup_steps = 500 * weight_decay = 0.01 * warmup_ratio=0.0 * train_batch_size: 16 * eval_batch_size: 32 * seed: 42 * num_epochs: 5 * optimizer: Adam * adam_beta1=0.9, * adam_beta2=0.999, * adam_epsilon=1e-08, ## Evaluation Metrics We have used usual evaluation metrics for this image classification task, that include weighted average precision, F1 score and recall, and overall accuracy. In multi-class classification problems, the weighted average method adjusts for class imbalance by assigning a weight proportional to the number of instances in each class. ## Training results | **Epoch** | **Training Loss** | **Validation Loss** | **Accuracy** | **F1** | **Precision** | **Recall** | |:---------: |:-----------------: |:-------------------: |:------------: |:-------: |:-------------: |:----------: | | 1 | 1.4341 | 0.62736 | 0.89417 | 0.89285 | 0.90208 | 0.89417 | | 2 | 0.4203 | 0.3135 | 0.92917 | 0.929 | 0.93058 | 0.92917 | | 3 | 0.2272 | 0.251 | 0.9375 | 0.93745 | 0.938 | 0.9375 | | 4 | 0.146 | 0.24937 | 0.93833 | 0.93814 | 0.94072 | 0.93833 | | 5 | 0.1034 | 0.2383 | 0.93917 | 0.9391 | 0.93992 | 0.93917 | ![](https://github.com/mmuratarat/turkish/blob/master/_posts/images/kvasir_vit_model_progress.png?raw=true) ## Training Metrics epoch = 5.0 total_flos = 2.634869332279296e+18 train_loss = 0.46618968290441176 train_runtime = 0:01:51.45 train_samples_per_second = 5.07 train_steps_per_second = 0.317 global_step = 2125 ## Fine-tuned model The pre-trained model has been pushed to Hugging Face Hub and can be found on https://huggingface.co/mmuratarat/kvasir-v2-classifier. You can make inferences by either using "Hosted Inference API" on the Hub or locally pulling the model from the Hub. ## How to use Here is how to use this pre-trained model to classify an image of GI tract: ```python from transformers import AutoModelForImageClassification, AutoFeatureExtractor from PIL import Image import requests # this is an image from "polyps" class url = 'https://github.com/mmuratarat/turkish/blob/master/_posts/images/example_polyps_image.jpg?raw=true' image = Image.open(requests.get(url, stream=True).raw) model = AutoModelForImageClassification.from_pretrained("mmuratarat/kvasir-v2-classifier") feature_extractor = AutoFeatureExtractor.from_pretrained("mmuratarat/kvasir-v2-classifier") inputs = feature_extractor(image, return_tensors="pt") id2label = {'0': 'dyed-lifted-polyps', '1': 'dyed-resection-margins', '2': 'esophagitis', '3': 'normal-cecum', '4': 'normal-pylorus', '5': 'normal-z-line', '6': 'polyps', '7': 'ulcerative-colitis'} logits = model(**inputs).logits predicted_label = logits.argmax(-1).item() predicted_class = id2label[str(predicted_label)] predicted_class ``` ## Framework versions * Transformers 4.34.0 * Pytorch 2.0.1+cu118 * Datasets 2.14.5 * Tokenizers 0.14.0 * scikit-learn 1.2.2 * scipy 1.11.3 * numpy 1.23.5 * accelerate 0.23.0 * pandas 1.5.3 ## Contact Please reach out to [email protected] if you have any questions or feedback. ## Source Code You can find the source code for obtaining this pre-trained model on [mmuratarat/kvasir-v2-ViT-classifier]( https://github.com/mmuratarat/kvasir-v2-ViT-classifier) repository of Github. Note that `Kvasir_ViT.ipynb` file contains Turkish commentary but the code itself is self-explanatory. ## Citation In all documents and papers that use or refer to this pre-trained model or report benchmarking results, a reference to this model have to be included.

[ "dyed-lifted-polyps", "dyed-resection-margins", "esophagitis", "normal-cecum", "normal-pylorus", "normal-z-line", "polyps", "ulcerative-colitis" ]

CHMD08/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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

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

dima806/top_15_anime_characters_image_detection

Returns anime character name given an image with about 98% accuracy. See https://www.kaggle.com/code/dima806/anime-character-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Killua 1.0000 1.0000 1.0000 57 Sakata Gintoki 1.0000 0.9655 0.9825 58 Eren Yeager 0.9649 0.9649 0.9649 57 Ichigo 0.9825 0.9825 0.9825 57 Lelouch Lamperouge 1.0000 1.0000 1.0000 58 Naruto 1.0000 1.0000 1.0000 58 Goku 0.9655 0.9825 0.9739 57 Vegeta 0.9649 0.9649 0.9649 57 Zoro 0.9355 1.0000 0.9667 58 Natsu Dragneel 1.0000 1.0000 1.0000 58 Gon 1.0000 0.9310 0.9643 58 Sasuke 0.9333 0.9655 0.9492 58 Elric Edward 1.0000 0.9825 0.9912 57 Light Yagami 0.9828 0.9828 0.9828 58 Luffy 1.0000 1.0000 1.0000 58 accuracy 0.9815 864 macro avg 0.9820 0.9815 0.9815 864 weighted avg 0.9820 0.9815 0.9815 864 ```

[ "killua", "sakata gintoki", "eren yeager", "ichigo", "lelouch lamperouge", "naruto", "goku", "vegeta", "zoro", "natsu dragneel", "gon", "sasuke", "elric edward", "light yagami", "luffy" ]

farhanyh/food_model

# 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: 1.6470 - Accuracy: 0.909 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.718 | 0.99 | 62 | 2.5596 | 0.842 | | 1.8555 | 2.0 | 125 | 1.8344 | 0.873 | | 1.6437 | 2.98 | 186 | 1.6470 | 0.909 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "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" ]

crangana/trained-race

# trained-race This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.9830 - Accuracy: 0.6258 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3923 | 0.18 | 1000 | 1.3550 | 0.4712 | | 1.1517 | 0.37 | 2000 | 1.1854 | 0.5429 | | 1.2405 | 0.55 | 3000 | 1.1001 | 0.5754 | | 1.0752 | 0.74 | 4000 | 1.0330 | 0.6018 | | 1.0986 | 0.92 | 5000 | 0.9973 | 0.6173 | | 1.0007 | 1.11 | 6000 | 0.9735 | 0.6279 | | 0.9851 | 1.29 | 7000 | 0.9830 | 0.6258 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "east asian", "indian", "black", "white", "middle eastern", "latino_hispanic", "southeast asian" ]

ahyar002/vit-pneumonia-classification

# vit-pneumonia-classification 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 chest-xray-classification dataset. It achieves the following results on the evaluation set: - Loss: 0.1301 - Accuracy: 0.9561 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4786 | 1.0 | 32 | 0.3081 | 0.8609 | | 0.213 | 2.0 | 64 | 0.1645 | 0.9399 | | 0.1724 | 3.0 | 96 | 0.1419 | 0.9502 | | 0.1438 | 4.0 | 128 | 0.0950 | 0.9734 | | 0.1267 | 5.0 | 160 | 0.1225 | 0.9579 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "normal", "pneumonia" ]

crangana/trained-age

# trained-age This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 1.1340 - Accuracy: 0.5164 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3347 | 0.18 | 1000 | 1.3819 | 0.4296 | | 1.3071 | 0.37 | 2000 | 1.2799 | 0.4642 | | 1.297 | 0.55 | 3000 | 1.2503 | 0.4721 | | 1.3121 | 0.74 | 4000 | 1.1661 | 0.4995 | | 1.1806 | 0.92 | 5000 | 1.1137 | 0.5240 | | 1.0839 | 1.11 | 6000 | 1.1340 | 0.5164 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "more than 70" ]

dima806/ball_types_image_detection

Returns ball type given an image. See https://www.kaggle.com/code/dima806/ball-types-image-detection for more details. ``` Classification report: precision recall f1-score support wiffle ball 1.0000 1.0000 1.0000 39 puffballs 1.0000 1.0000 1.0000 38 chrochet ball 1.0000 1.0000 1.0000 39 golf ball 1.0000 1.0000 1.0000 39 pokeman balls 1.0000 1.0000 1.0000 39 water polo ball 1.0000 1.0000 1.0000 39 football 1.0000 1.0000 1.0000 39 marble 1.0000 1.0000 1.0000 38 medicine ball 1.0000 1.0000 1.0000 39 tether ball 1.0000 1.0000 1.0000 38 billiard ball 1.0000 1.0000 1.0000 38 cannon ball 1.0000 1.0000 1.0000 39 crystal ball 1.0000 1.0000 1.0000 38 cricket ball 1.0000 1.0000 1.0000 39 sepak takraw ball 1.0000 1.0000 1.0000 39 tennis ball 1.0000 1.0000 1.0000 39 wrecking ball 1.0000 1.0000 1.0000 38 rubberband ball 1.0000 1.0000 1.0000 39 buckeyballs 1.0000 1.0000 1.0000 39 bowling ball 1.0000 1.0000 1.0000 38 eyeballs 1.0000 1.0000 1.0000 38 meat ball 1.0000 1.0000 1.0000 38 brass 1.0000 1.0000 1.0000 39 screwballs 1.0000 1.0000 1.0000 38 baseball 1.0000 1.0000 1.0000 38 beachballs 1.0000 1.0000 1.0000 39 soccer ball 1.0000 1.0000 1.0000 38 basketball 1.0000 1.0000 1.0000 39 volley ball 1.0000 1.0000 1.0000 39 paint balls 1.0000 1.0000 1.0000 39 accuracy 1.0000 1158 macro avg 1.0000 1.0000 1.0000 1158 weighted avg 1.0000 1.0000 1.0000 1158 ```

[ "wiffle ball", "puffballs", "chrochet ball", "golf ball", "pokeman balls", "water polo ball", "football", "marble", "medicine ball", "tether ball", "billiard ball", "cannon ball", "crystal ball", "cricket ball", "sepak takraw ball", "tennis ball", "wrecking ball", "rubberband ball", "buckeyballs", "bowling ball", "eyeballs", "meat ball", "brass", "screwballs", "baseball", "beachballs", "soccer ball", "basketball", "volley ball", "paint balls" ]

crangana/trained-gender

# trained-gender This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.2437 - Accuracy: 0.8986 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4277 | 0.18 | 1000 | 0.4054 | 0.8089 | | 0.315 | 0.37 | 2000 | 0.3487 | 0.8318 | | 0.3082 | 0.55 | 3000 | 0.3052 | 0.8633 | | 0.3235 | 0.74 | 4000 | 0.2899 | 0.8684 | | 0.2505 | 0.92 | 5000 | 0.2693 | 0.8785 | | 0.2484 | 1.11 | 6000 | 0.2547 | 0.8889 | | 0.1933 | 1.29 | 7000 | 0.2521 | 0.8901 | | 0.1497 | 1.48 | 8000 | 0.2443 | 0.8929 | | 0.326 | 1.66 | 9000 | 0.2406 | 0.8958 | | 0.215 | 1.84 | 10000 | 0.2381 | 0.9007 | | 0.2035 | 2.03 | 11000 | 0.2437 | 0.8986 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "male", "female" ]

Manixtox/swin-tiny-patch4-window7-224-finetuned-shotclass

# swin-tiny-patch4-window7-224-finetuned-shotclass 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: 0.2648 - Accuracy: 0.9275 ## 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 | 0.97 | 8 | 0.3702 | 0.8855 | | 0.3802 | 1.94 | 16 | 0.2648 | 0.9275 | | 0.2783 | 2.91 | 24 | 0.3685 | 0.8740 | | 0.2403 | 4.0 | 33 | 0.3235 | 0.8893 | | 0.1864 | 4.85 | 40 | 0.3339 | 0.8740 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "plano_entero", "plano_medio", "primer_plano", "xlongshots" ]

platzi/platzi-vit-model-gabriel-salazar

# platzi-vit-model-gabriel-salazar 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1267 - Accuracy: 0.9774 ## 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: 8 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0535 | 3.85 | 500 | 0.1267 | 0.9774 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "angular_leaf_spot", "bean_rust", "healthy" ]

dima806/shoe_types_image_detection

Return shoe type given an image. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/VRp8h5fKGtsdi_ydGM2Oj.png) See https://www.kaggle.com/code/dima806/shoe-type-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Clog 0.9748 0.9598 0.9672 1169 Brogue 0.9804 0.9812 0.9808 1170 Sneaker 0.9718 0.9735 0.9727 1170 Boat 0.9642 0.9658 0.9650 1170 Ballet Flat 0.9729 0.9837 0.9783 1169 accuracy 0.9728 5848 macro avg 0.9728 0.9728 0.9728 5848 weighted avg 0.9728 0.9728 0.9728 5848 ```

[ "clog", "brogue", "sneaker", "boat", "ballet flat" ]

flatmoon102/fruits_and_vegetables_image_classification

# fruits_and_vegetables_image_classification 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.3835 - Accuracy: 0.9159 ## 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: 8e-05 - train_batch_size: 32 - eval_batch_size: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 87 | 1.6751 | 0.8768 | | No log | 2.0 | 174 | 1.0260 | 0.8957 | | No log | 3.0 | 261 | 0.6767 | 0.8957 | | No log | 4.0 | 348 | 0.5445 | 0.8986 | | No log | 5.0 | 435 | 0.4685 | 0.9072 | | 0.8955 | 6.0 | 522 | 0.4328 | 0.9072 | | 0.8955 | 7.0 | 609 | 0.4028 | 0.9 | | 0.8955 | 8.0 | 696 | 0.3958 | 0.9145 | | 0.8955 | 9.0 | 783 | 0.3835 | 0.9159 | | 0.8955 | 10.0 | 870 | 0.3842 | 0.9145 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "pineapple", "lettuce", "potato", "bell pepper", "pomegranate", "chilli pepper", "eggplant", "mango", "cabbage", "lemon", "capsicum", "spinach", "corn", "watermelon", "apple", "garlic", "sweetcorn", "grapes", "ginger", "sweetpotato", "raddish", "tomato", "paprika", "carrot", "cucumber", "cauliflower", "kiwi", "orange", "banana", "soy beans", "beetroot", "jalepeno", "onion", "peas", "pear", "turnip" ]

ammardaffa/fruit_veg_detection

# fruit_veg_detection 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.6689 - Accuracy: 0.9116 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 87 | 0.8126 | 0.8913 | | No log | 2.0 | 174 | 0.6689 | 0.9116 | | No log | 3.0 | 261 | 0.5979 | 0.9087 | | No log | 4.0 | 348 | 0.5629 | 0.9116 | | No log | 5.0 | 435 | 0.5583 | 0.9014 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "spinach", "lettuce", "raddish", "capsicum", "cucumber", "kiwi", "tomato", "jalepeno", "chilli pepper", "mango", "garlic", "paprika", "sweetpotato", "potato", "pear", "ginger", "peas", "pineapple", "corn", "sweetcorn", "bell pepper", "eggplant", "lemon", "banana", "soy beans", "watermelon", "apple", "carrot", "cabbage", "orange", "onion", "beetroot", "pomegranate", "cauliflower", "turnip", "grapes" ]

dima806/face_obstruction_image_detection

Returns face obstruction type given a facial image with about 91% accuracy. See https://www.kaggle.com/code/dima806/face-obstruction-image-detection-vit for more details. ``` Classification report: precision recall f1-score support sunglasses 0.9974 0.9985 0.9980 3422 glasses 0.9896 0.9968 0.9932 3422 other 0.7198 0.7613 0.7400 3422 mask 0.9971 0.9985 0.9978 3422 hand 0.7505 0.7086 0.7290 3422 none 0.9976 0.9860 0.9918 3422 accuracy 0.9083 20532 macro avg 0.9087 0.9083 0.9083 20532 weighted avg 0.9087 0.9083 0.9083 20532 ```

[ "sunglasses", "glasses", "other", "mask", "hand", "none" ]

tejp/fine-tuned

# fine-tuned This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.0068 - Accuracy: 0.2857 - F1: 0.2030 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "boat", "children", "water", "dogs", "fireman", "firetruck", "mountains", "people", "river", "snow", "stairs" ]

Ahmeng/image_classification

# image_classification 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1943 - Accuracy: 0.9469 ## 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 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 52 | 0.2654 | 0.9324 | | No log | 2.0 | 104 | 0.1310 | 0.9807 | | No log | 3.0 | 156 | 0.1485 | 0.9662 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

lucascruz/CheXpert-ViT-U-MultiClass

# CheXpert-ViT-U-MultiClass This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown 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: 0.0004 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.1

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14" ]

zguo0525/myshell_nsfw_filter

## Example Usage Here's a step-by-step Python code example to classify an image: ```python from transformers import pipeline import requests from PIL import Image from io import BytesIO # 1. Load the pipeline for image classification pipe = pipeline("image-classification", model="zguo0525/myshell_nsfw_filter") # 2. Load the image into memory (assuming you have the URL for the image) image_url = 'https://img-myshell.net/meinamix/red_hair_girl' response = requests.get(image_url) image = Image.open(BytesIO(response.content)) # 3. Use the pipeline to classify the image results = pipe(image) # 4. Print the results label = results[0]['label'] score = results[0]['score'] print(f"{label}: {score:.4f}") ```

[ "ero", "porn", "safe" ]

qzheng75/swin-tiny-patch4-window7-224-finetuned-plot-images

# swin-tiny-patch4-window7-224-finetuned-plot-images 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: 0.0093 - Accuracy: 0.9960 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0333 | 1.0 | 426 | 0.0261 | 0.9937 | | 0.0223 | 2.0 | 852 | 0.0072 | 0.9975 | | 0.0103 | 3.0 | 1278 | 0.0093 | 0.9960 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "dot", "horizontal_bar", "line", "scatter", "vertical_bar" ]

Antdochi/results

# results This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1744 - Accuracy: 0.9428 - F1: 0.9428 - Precision: 0.9428 - Recall: 0.9428 ## 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: 0.01 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 16 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2883 | 1.0 | 99 | 0.3757 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | | 0.2195 | 2.0 | 198 | 0.2293 | 0.9178 | 0.9178 | 0.9178 | 0.9178 | | 0.1936 | 3.0 | 297 | 0.2120 | 0.9149 | 0.9149 | 0.9149 | 0.9149 | | 0.163 | 4.0 | 396 | 0.1744 | 0.9428 | 0.9428 | 0.9428 | 0.9428 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

lantian-chen/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: 1.6473 - Accuracy: 0.874 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7147 | 0.99 | 62 | 2.5361 | 0.804 | | 1.8577 | 2.0 | 125 | 1.8141 | 0.852 | | 1.6359 | 2.98 | 186 | 1.6473 | 0.874 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "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" ]

luminoussg/autotrain-xray-93756145904

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 93756145904 - CO2 Emissions (in grams): 5.1814 ## Validation Metrics - Loss: 0.062 - Accuracy: 0.977 - Precision: 0.983 - Recall: 0.986 - AUC: 0.997 - F1: 0.984

[ "normal", "pneumonia" ]

qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not

# swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not 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: 0.0000 - 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: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0006 | 1.0 | 448 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "is_not_plot", "is_plot" ]

qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not-finetuned-image-is-plot-or-not

# swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not-finetuned-image-is-plot-or-not This model is a fine-tuned version of [qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not](https://huggingface.co/qzheng75/swin-tiny-patch4-window7-224-finetuned-image-is-plot-or-not) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0012 - Accuracy: 0.9997 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0001 | 1.0 | 90 | 0.0009 | 0.9997 | | 0.0014 | 1.99 | 180 | 0.0022 | 0.9993 | | 0.0054 | 2.99 | 270 | 0.0012 | 0.9997 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "is_not_plot", "is_plot" ]

dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2

# swin-tiny-patch4-window7-224-uploads-classifier-v2 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: 0.0745 - Accuracy: 0.9843 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2482 | 1.0 | 18 | 0.4781 | 0.8824 | | 0.3036 | 2.0 | 36 | 0.0936 | 0.9804 | | 0.1687 | 3.0 | 54 | 0.0745 | 0.9843 | | 0.1392 | 4.0 | 72 | 0.0980 | 0.9725 | | 0.14 | 5.0 | 90 | 0.0778 | 0.9765 | | 0.1186 | 6.0 | 108 | 0.0837 | 0.9725 | | 0.1088 | 7.0 | 126 | 0.0645 | 0.9804 | | 0.0789 | 8.0 | 144 | 0.0675 | 0.9765 | | 0.0644 | 9.0 | 162 | 0.0940 | 0.9686 | | 0.0582 | 10.0 | 180 | 0.0879 | 0.9725 | | 0.0591 | 11.0 | 198 | 0.0935 | 0.9686 | | 0.0538 | 12.0 | 216 | 0.0540 | 0.9804 | | 0.0588 | 13.0 | 234 | 0.0725 | 0.9686 | | 0.0538 | 14.0 | 252 | 0.0637 | 0.9765 | | 0.0462 | 15.0 | 270 | 0.0694 | 0.9725 | | 0.0352 | 16.0 | 288 | 0.0771 | 0.9686 | | 0.0536 | 17.0 | 306 | 0.0629 | 0.9804 | | 0.0403 | 18.0 | 324 | 0.0933 | 0.9686 | | 0.0412 | 19.0 | 342 | 0.0848 | 0.9725 | | 0.0305 | 20.0 | 360 | 0.0820 | 0.9725 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "full-bleed", "illustration", "logo", "photo", "qr-code" ]

tejp/fine-tuned-augmented

# fine-tuned-augmented This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom_dataset_augmented dataset. It achieves the following results on the evaluation set: - Loss: 2.2134 - Accuracy: 0.2333 - F1: 0.0455 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "aeroplane", "car", "people", "river", "ski", "snow", "deer", "dogsledge", "factory", "fireman", "firetruck", "ladder", "lake", "mountain" ]

kenghweetan/clothing_category_model

# clothing_category_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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4070 - Accuracy: 0.2103 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.695 | 1.0 | 551 | 4.4070 | 0.2103 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cpu - Datasets 2.14.5 - Tokenizers 0.13.3

[ "accessories", "apparel set", "eyewear", "flip flops", "fragrance", "free gifts", "gloves", "hair", "headwear", "home furnishing", "innerwear", "jewellery", "bags", "lips", "loungewear and nightwear", "makeup", "mufflers", "nails", "perfumes", "sandal", "saree", "scarves", "shoe accessories", "bath and body", "shoes", "skin", "skin care", "socks", "sports accessories", "sports equipment", "stoles", "ties", "topwear", "umbrellas", "beauty accessories", "wallets", "watches", "water bottle", "wristbands", "belts", "bottomwear", "cufflinks", "dress", "eyes" ]

Frank0930/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 cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0864 - Accuracy: 0.9726 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4809 | 1.0 | 351 | 0.1388 | 0.9528 | | 0.3489 | 2.0 | 703 | 0.0945 | 0.9692 | | 0.3528 | 2.99 | 1053 | 0.0864 | 0.9726 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

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

Devarshi/Armature_Defect_Detection_Resin

# Armature_Defect_Detection_Resin 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: 0.4978 - Accuracy: 0.76 - F1: 0.76 - Recall: 0.76 - Precision: 0.76 ## 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 | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:------:|:---------:| | No log | 0.57 | 1 | 0.7205 | 0.44 | 0.44 | 0.44 | 0.44 | | No log | 1.57 | 2 | 0.5926 | 0.6 | 0.6 | 0.6 | 0.6 | | No log | 2.57 | 3 | 0.4978 | 0.76 | 0.76 | 0.76 | 0.76 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

[ "resin under fill", "good pieces" ]

romitbarua/autotrain-deepfakeface-94074145984

# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 32.1392 ## Validation Metricsg loss: 0.23420386016368866 f1_macro: 0.9410988547155245 f1_micro: 0.941 f1_weighted: 0.9410988547155245 precision_macro: 0.9415975677612235 precision_micro: 0.941 precision_weighted: 0.9415975677612235 recall_macro: 0.941 recall_micro: 0.941 recall_weighted: 0.941 accuracy: 0.941

[ "inpainting", "insight", "text2img", "wiki" ]

EscvNcl/MobileNet-V2-Retinopathy

# MobileNet-V2-Retinopathy This model is a fine-tuned version of [google/mobilenet_v2_1.4_224](https://huggingface.co/google/mobilenet_v2_1.4_224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2044 - Accuracy: 0.9307 ## 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: 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4403 | 1.0 | 113 | 0.5330 | 0.7079 | | 0.5538 | 2.0 | 227 | 0.4312 | 0.7723 | | 0.542 | 3.0 | 340 | 0.5137 | 0.7426 | | 0.4776 | 4.0 | 454 | 0.4656 | 0.7723 | | 0.4244 | 5.0 | 567 | 1.0400 | 0.5990 | | 0.4694 | 6.0 | 681 | 0.5936 | 0.7228 | | 0.4494 | 7.0 | 794 | 0.4667 | 0.7822 | | 0.4647 | 8.0 | 908 | 0.2629 | 0.8960 | | 0.3646 | 9.0 | 1021 | 0.2287 | 0.8861 | | 0.4827 | 10.0 | 1135 | 1.7967 | 0.5149 | | 0.3679 | 11.0 | 1248 | 0.4184 | 0.8267 | | 0.3454 | 12.0 | 1362 | 0.1885 | 0.9406 | | 0.3562 | 13.0 | 1475 | 0.2798 | 0.9059 | | 0.3397 | 14.0 | 1589 | 1.6444 | 0.5891 | | 0.4047 | 14.93 | 1695 | 0.2044 | 0.9307 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "nrdr", "rdr" ]

chanelcolgate/vit-base-image-classification-yenthienviet

# vit-base-image-classification-yenthienviet 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 image-classification-yenthienviet dataset. It achieves the following results on the evaluation set: - Loss: 0.2380 - Accuracy: 0.9344 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6118 | 0.56 | 100 | 0.4854 | 0.8616 | | 0.329 | 1.11 | 200 | 0.4473 | 0.8616 | | 0.3002 | 1.67 | 300 | 0.4167 | 0.8637 | | 0.1549 | 2.22 | 400 | 0.2911 | 0.9178 | | 0.1993 | 2.78 | 500 | 0.2934 | 0.9168 | | 0.1071 | 3.33 | 600 | 0.2389 | 0.9324 | | 0.1027 | 3.89 | 700 | 0.2380 | 0.9344 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "botkhi", "thuytinh", "ocvit", "ban", "contrung", "kimloai", "toc" ]

wasifh/model

# 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: 0.8671 - Accuracy: 0.8235 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9738 | 0.94 | 8 | 1.1530 | 0.5882 | | 0.8674 | 2.0 | 17 | 1.0818 | 0.5882 | | 0.708 | 2.94 | 25 | 1.0412 | 0.5882 | | 0.7004 | 4.0 | 34 | 0.9774 | 0.7647 | | 0.5957 | 4.94 | 42 | 1.0344 | 0.6471 | | 0.5273 | 5.65 | 48 | 0.8671 | 0.8235 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "blistering", "flatspot", "graining", "none" ]

superdinmc/autotrain-orbit-millets-94211146034

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94211146034 - CO2 Emissions (in grams): 0.0110 ## Validation Metrics - Loss: 1.742 - Accuracy: 0.357 - Macro F1: 0.314 - Micro F1: 0.357 - Weighted F1: 0.314 - Macro Precision: 0.321 - Micro Precision: 0.357 - Weighted Precision: 0.321 - Macro Recall: 0.357 - Micro Recall: 0.357 - Weighted Recall: 0.357

[ "bajra", "barri", "jhangora", "jowar", "kangni", "kodra", "ragi" ]

zkdeng/deit-base-patch16-224-finetuned-dangerousSpiders

# deit-base-patch16-224-finetuned-dangerousSpiders This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1957 - eval_accuracy: 0.915 - eval_precision: 0.8899 - eval_recall: 0.9510 - eval_f1: 0.9194 - eval_runtime: 5.3671 - eval_samples_per_second: 37.264 - eval_steps_per_second: 2.422 - step: 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: 2 ### Framework versions - Transformers 4.33.2 - Pytorch 2.2.0.dev20230921 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "lactrodectus_hesperus", "parasteatoda_tepidariorum" ]

dima806/oxford_flowers_image_detection

Returns flower type given an image. See https://www.kaggle.com/code/dima806/oxford-flowers-image-detection-vit for more details. ``` Classification report: precision recall f1-score support bolero deep blue 1.0000 1.0000 1.0000 94 toad lily 1.0000 1.0000 1.0000 94 bougainvillea 1.0000 1.0000 1.0000 94 blanket flower 1.0000 1.0000 1.0000 93 prince of wales feathers 1.0000 1.0000 1.0000 94 english marigold 1.0000 1.0000 1.0000 93 common dandelion 1.0000 1.0000 1.0000 94 mallow 1.0000 1.0000 1.0000 94 barbeton daisy 1.0000 1.0000 1.0000 94 desert-rose 1.0000 1.0000 1.0000 94 anthurium 1.0000 1.0000 1.0000 94 cyclamen 1.0000 1.0000 1.0000 94 marigold 1.0000 1.0000 1.0000 93 spring crocus 1.0000 1.0000 1.0000 94 petunia 1.0000 1.0000 1.0000 94 foxglove 1.0000 1.0000 1.0000 94 primula 1.0000 1.0000 1.0000 94 cape flower 1.0000 1.0000 1.0000 94 colt's foot 1.0000 1.0000 1.0000 93 osteospermum 1.0000 1.0000 1.0000 93 buttercup 1.0000 1.0000 1.0000 94 balloon flower 1.0000 1.0000 1.0000 94 fire lily 1.0000 1.0000 1.0000 93 bromelia 1.0000 1.0000 1.0000 93 artichoke 1.0000 1.0000 1.0000 93 daffodil 1.0000 1.0000 1.0000 94 pink-yellow dahlia 1.0000 1.0000 1.0000 93 geranium 1.0000 1.0000 1.0000 94 peruvian lily 1.0000 1.0000 1.0000 93 king protea 1.0000 1.0000 1.0000 94 silverbush 1.0000 1.0000 1.0000 94 alpine sea holly 1.0000 1.0000 1.0000 94 hibiscus 1.0000 1.0000 1.0000 93 giant white arum lily 1.0000 1.0000 1.0000 94 canna lily 1.0000 1.0000 1.0000 94 sunflower 1.0000 1.0000 1.0000 94 sweet pea 1.0000 1.0000 1.0000 94 mexican aster 1.0000 1.0000 1.0000 93 californian poppy 1.0000 1.0000 1.0000 94 pincushion flower 1.0000 1.0000 1.0000 93 black-eyed susan 1.0000 1.0000 1.0000 94 blackberry lily 1.0000 1.0000 1.0000 93 gaura 1.0000 1.0000 1.0000 94 love in the mist 1.0000 1.0000 1.0000 93 spear thistle 1.0000 1.0000 1.0000 94 orange dahlia 1.0000 1.0000 1.0000 93 wallflower 1.0000 1.0000 1.0000 93 tiger lily 1.0000 1.0000 1.0000 94 stemless gentian 1.0000 1.0000 1.0000 93 morning glory 1.0000 1.0000 1.0000 93 frangipani 1.0000 1.0000 1.0000 94 lotus lotus 1.0000 1.0000 1.0000 93 red ginger 1.0000 1.0000 1.0000 94 oxeye daisy 1.0000 1.0000 1.0000 94 windflower 1.0000 1.0000 1.0000 93 monkshood 1.0000 1.0000 1.0000 94 bishop of llandaff 1.0000 1.0000 1.0000 93 globe-flower 1.0000 1.0000 1.0000 93 globe thistle 1.0000 1.0000 1.0000 93 poinsettia 1.0000 1.0000 1.0000 94 wild pansy 1.0000 1.0000 1.0000 93 water lily 1.0000 1.0000 1.0000 94 watercress 1.0000 1.0000 1.0000 93 mexican petunia 1.0000 1.0000 1.0000 94 corn poppy 1.0000 1.0000 1.0000 93 bearded iris 1.0000 1.0000 1.0000 93 azalea 1.0000 1.0000 1.0000 93 camellia 1.0000 1.0000 1.0000 94 tree poppy 1.0000 1.0000 1.0000 93 moon orchid 1.0000 1.0000 1.0000 94 magnolia 1.0000 1.0000 1.0000 94 bee balm 1.0000 1.0000 1.0000 94 lenten rose 1.0000 1.0000 1.0000 94 trumpet creeper 1.0000 1.0000 1.0000 94 passion flower 1.0000 1.0000 1.0000 94 yellow iris 1.0000 1.0000 1.0000 93 pelargonium 1.0000 1.0000 1.0000 93 tree mallow 1.0000 1.0000 1.0000 94 thorn apple 1.0000 1.0000 1.0000 94 garden phlox 1.0000 1.0000 1.0000 94 sword lily 1.0000 1.0000 1.0000 94 carnation 1.0000 1.0000 1.0000 94 ruby-lipped cattleya 1.0000 1.0000 1.0000 94 ball moss 1.0000 1.0000 1.0000 94 columbine 1.0000 1.0000 1.0000 93 siam tulip 1.0000 1.0000 1.0000 94 snapdragon 1.0000 1.0000 1.0000 94 cautleya spicata 1.0000 1.0000 1.0000 94 hard-leaved pocket orchid 1.0000 1.0000 1.0000 93 pink primrose 1.0000 1.0000 1.0000 94 gazania 1.0000 1.0000 1.0000 93 hippeastrum 1.0000 1.0000 1.0000 93 fritillary 1.0000 1.0000 1.0000 93 canterbury bells 1.0000 1.0000 1.0000 94 great masterwort 1.0000 1.0000 1.0000 93 sweet william 1.0000 1.0000 1.0000 94 clematis 1.0000 1.0000 1.0000 93 purple coneflower 1.0000 1.0000 1.0000 94 japanese anemone 1.0000 1.0000 1.0000 94 bird of paradise 1.0000 1.0000 1.0000 93 rose 1.0000 1.0000 1.0000 94 grape hyacinth 1.0000 1.0000 1.0000 94 accuracy 1.0000 9548 macro avg 1.0000 1.0000 1.0000 9548 weighted avg 1.0000 1.0000 1.0000 9548 ```

[ "bolero deep blue", "toad lily", "bougainvillea", "blanket flower", "prince of wales feathers", "english marigold", "common dandelion", "mallow", "barbeton daisy", "desert-rose", "anthurium", "cyclamen", "marigold", "spring crocus", "petunia", "foxglove", "primula", "cape flower", "colt's foot", "osteospermum", "buttercup", "balloon flower", "fire lily", "bromelia", "artichoke", "daffodil", "pink-yellow dahlia", "geranium", "peruvian lily", "king protea", "silverbush", "alpine sea holly", "hibiscus", "giant white arum lily", "canna lily", "sunflower", "sweet pea", "mexican aster", "californian poppy", "pincushion flower", "black-eyed susan", "blackberry lily", "gaura", "love in the mist", "spear thistle", "orange dahlia", "wallflower", "tiger lily", "stemless gentian", "morning glory", "frangipani", "lotus lotus", "red ginger", "oxeye daisy", "windflower", "monkshood", "bishop of llandaff", "globe-flower", "globe thistle", "poinsettia", "wild pansy", "water lily", "watercress", "mexican petunia", "corn poppy", "bearded iris", "azalea", "camellia", "tree poppy", "moon orchid", "magnolia", "bee balm", "lenten rose", "trumpet creeper", "passion flower", "yellow iris", "pelargonium", "tree mallow", "thorn apple", "garden phlox", "sword lily", "carnation", "ruby-lipped cattleya", "ball moss", "columbine", "siam tulip", "snapdragon", "cautleya spicata", "hard-leaved pocket orchid", "pink primrose", "gazania", "hippeastrum", "fritillary", "canterbury bells", "great masterwort", "sweet william", "clematis", "purple coneflower", "japanese anemone", "bird of paradise", "rose", "grape hyacinth" ]

nandyc/swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2

# swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2 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 ASL_Isolated_Swin_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1269 - Accuracy: 0.9769 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5439 | 1.09 | 100 | 1.4188 | 0.5538 | | 0.8646 | 2.17 | 200 | 0.4542 | 0.8885 | | 0.5485 | 3.26 | 300 | 0.4103 | 0.8538 | | 0.5082 | 4.35 | 400 | 0.2925 | 0.8962 | | 0.5302 | 5.43 | 500 | 0.2471 | 0.9269 | | 0.4072 | 6.52 | 600 | 0.2676 | 0.9231 | | 0.4424 | 7.61 | 700 | 0.4150 | 0.9038 | | 0.3409 | 8.7 | 800 | 0.1922 | 0.9538 | | 0.3046 | 9.78 | 900 | 0.1917 | 0.9462 | | 0.2911 | 10.87 | 1000 | 0.2272 | 0.9423 | | 0.269 | 11.96 | 1100 | 0.0722 | 0.9692 | | 0.3709 | 13.04 | 1200 | 0.1473 | 0.9654 | | 0.3443 | 14.13 | 1300 | 0.1545 | 0.9615 | | 0.187 | 15.22 | 1400 | 0.1060 | 0.9731 | | 0.1879 | 16.3 | 1500 | 0.1124 | 0.9692 | | 0.2183 | 17.39 | 1600 | 0.1377 | 0.9615 | | 0.1478 | 18.48 | 1700 | 0.1269 | 0.9769 | | 0.1944 | 19.57 | 1800 | 0.0909 | 0.9769 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z" ]

superdinmc/autotrain-orbit-millets-2-94372146064

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94372146064 - CO2 Emissions (in grams): 0.0330 ## Validation Metrics - Loss: 1.710 - Accuracy: 0.327 - Macro F1: 0.262 - Micro F1: 0.327 - Weighted F1: 0.309 - Macro Precision: 0.277 - Micro Precision: 0.327 - Weighted Precision: 0.312 - Macro Recall: 0.270 - Micro Recall: 0.327 - Weighted Recall: 0.327

[ "bajra", "barri", "jhangora", "jowar", "kangni", "kodra", "ragi" ]

EscvNcl/ConvNext-V2-Retinopathy

# ConvNext-V2-Retinopathy This model is a fine-tuned version of [syedmuhammad/ConvNextV2-Diabetec-Retinopathy](https://huggingface.co/syedmuhammad/ConvNextV2-Diabetec-Retinopathy) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0219 - Accuracy: 0.9901 ## 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: 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.125 | 1.0 | 113 | 0.0339 | 0.9901 | | 0.2206 | 2.0 | 227 | 0.0139 | 0.9901 | | 0.1751 | 3.0 | 340 | 0.0114 | 0.9950 | | 0.0599 | 4.0 | 454 | 0.0277 | 0.9950 | | 0.1122 | 5.0 | 567 | 0.0328 | 0.9950 | | 0.093 | 6.0 | 681 | 0.0240 | 0.9901 | | 0.0673 | 7.0 | 794 | 0.0251 | 0.9950 | | 0.0718 | 8.0 | 908 | 0.0458 | 0.9851 | | 0.0632 | 9.0 | 1021 | 0.0477 | 0.9901 | | 0.0263 | 10.0 | 1135 | 0.0399 | 0.9950 | | 0.0304 | 11.0 | 1248 | 0.0295 | 0.9901 | | 0.0892 | 12.0 | 1362 | 0.0330 | 0.9950 | | 0.0227 | 13.0 | 1475 | 0.0287 | 0.9901 | | 0.0253 | 14.0 | 1589 | 0.0262 | 0.9901 | | 0.1242 | 14.93 | 1695 | 0.0219 | 0.9901 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "nrdr", "rdr" ]

wang1215/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: 1.6235 - Accuracy: 0.892 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7037 | 0.99 | 62 | 2.5304 | 0.832 | | 1.8523 | 2.0 | 125 | 1.8095 | 0.865 | | 1.5914 | 2.98 | 186 | 1.6235 | 0.892 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "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" ]

andriydovgal/mvp_flowers

# mvp_flowers 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: - Loss: 3.0181 - Accuracy: 0.907 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8355 | 0.99 | 62 | 3.7493 | 0.711 | | 3.2592 | 2.0 | 125 | 3.1841 | 0.886 | | 2.9952 | 2.98 | 186 | 3.0181 | 0.907 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "1", "10", "16", "98", "99", "17", "18", "19", "2", "20", "21", "22", "23", "24", "100", "25", "26", "27", "28", "29", "3", "30", "31", "32", "33", "101", "34", "35", "36", "37", "38", "39", "4", "40", "41", "42", "102", "43", "44", "45", "46", "47", "48", "49", "5", "50", "51", "11", "52", "53", "54", "55", "56", "57", "58", "59", "6", "60", "12", "61", "62", "63", "64", "65", "66", "67", "68", "69", "7", "13", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "14", "8", "80", "81", "82", "83", "84", "85", "86", "87", "88", "15", "89", "9", "90", "91", "92", "93", "94", "95", "96", "97" ]

galbitang/autotrain-sofa_style_classification-94412146080

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94412146080 - CO2 Emissions (in grams): 3.4207 ## Validation Metrics - Loss: 0.863 - Accuracy: 0.722 - Macro F1: 0.660 - Micro F1: 0.722 - Weighted F1: 0.711 - Macro Precision: 0.720 - Micro Precision: 0.722 - Weighted Precision: 0.735 - Macro Recall: 0.667 - Micro Recall: 0.722 - Weighted Recall: 0.722

[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]

galbitang/autotrain-bed_frame_style_classification-94482146114

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94482146114 - CO2 Emissions (in grams): 0.0920 ## Validation Metrics - Loss: 0.544 - Accuracy: 0.824 - Macro F1: 0.820 - Micro F1: 0.824 - Weighted F1: 0.822 - Macro Precision: 0.829 - Micro Precision: 0.824 - Weighted Precision: 0.825 - Macro Recall: 0.816 - Micro Recall: 0.824 - Weighted Recall: 0.824

[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]

galbitang/autotrain-chair_style_classification-94502146123

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94502146123 - CO2 Emissions (in grams): 0.0613 ## Validation Metrics - Loss: 0.736 - Accuracy: 0.760 - Macro F1: 0.688 - Micro F1: 0.760 - Weighted F1: 0.753 - Macro Precision: 0.764 - Micro Precision: 0.760 - Weighted Precision: 0.766 - Macro Recall: 0.671 - Micro Recall: 0.760 - Weighted Recall: 0.760

[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]

hongerzh/my_NFT_sale_classifier

# my_NFT_sale_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: - Loss: 0.6323 - Accuracy: 0.6560 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6234 | 1.0 | 112 | 0.6335 | 0.6565 | | 0.6077 | 2.0 | 225 | 0.6335 | 0.6583 | | 0.5896 | 2.99 | 336 | 0.6323 | 0.6560 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.14.0

[ "notsale", "sale" ]

Tushar86/yolo-testing

# Usage ``` from transformers import pipeline p = pipeline("image-classification", model="juliensimon/autotrain-food101-1471154053") result = p("my_image.jpg") ``` # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1471154053 - CO2 Emissions (in grams): 179.1154 ## Validation Metrics - Loss: 0.301 - Accuracy: 0.915 - Macro F1: 0.915 - Micro F1: 0.915 - Weighted F1: 0.915 - Macro Precision: 0.917 - Micro Precision: 0.915 - Weighted Precision: 0.917 - Macro Recall: 0.915 - Micro Recall: 0.915 - Weighted Recall: 0.915

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "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" ]

galbitang/autotrain-table_style_classification2-94510146124

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94510146124 - CO2 Emissions (in grams): 0.0786 ## Validation Metrics - Loss: 0.806 - Accuracy: 0.766 - Macro F1: 0.683 - Micro F1: 0.766 - Weighted F1: 0.750 - Macro Precision: 0.710 - Micro Precision: 0.766 - Weighted Precision: 0.744 - Macro Recall: 0.676 - Micro Recall: 0.766 - Weighted Recall: 0.766

[ "natural", "lovelyromantic", "koreanasia", "modern", "minimalsimple", "northerneurope", "vintageretro", "unique", "industrial", "classicantique", "frenchprovence" ]

fengdavid/my_awesome_model

# my_awesome_model This model is a fine-tuned version of [distbert](https://huggingface.co/distbert) on the blala dataset. It achieves the following results on the evaluation set: - Loss: 0.1908 - Accuracy: 0.929 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 391 | 0.1959 | 0.9243 | | 0.2489 | 2.0 | 782 | 0.1908 | 0.929 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "negative", "positive" ]

lucascruz/CheXpert-ViT-U-SelfTrained

# CheXpert-ViT-U-SelfTrained This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown 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: 0.0004 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.1

[ "label_0", "label_1", "label_2", "label_3", "label_4" ]

hyeongjin99/resnet_50_base_aihub_model_py

# resnet_50_base_aihub_model_py 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: 0.0987 - Accuracy: 0.9681 - Precision: 0.9712 - Recall: 0.9624 - F1: 0.9667 ## 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: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - 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 | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5577 | 1.0 | 149 | 0.4027 | 0.8453 | 0.8514 | 0.8415 | 0.8435 | | 0.323 | 2.0 | 299 | 0.2346 | 0.9097 | 0.9208 | 0.8962 | 0.9074 | | 0.2467 | 3.0 | 448 | 0.1786 | 0.9303 | 0.9465 | 0.9216 | 0.9326 | | 0.1953 | 4.0 | 598 | 0.1266 | 0.9573 | 0.9591 | 0.9483 | 0.9535 | | 0.1456 | 4.98 | 745 | 0.0987 | 0.9681 | 0.9712 | 0.9624 | 0.9667 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

[ "cloudy", "normal", "rainy", "snowy" ]

ombhojane/healthyPlantsModel

# healthy-plant-disease-identification This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the [Kaggle version](https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset) of the [Plant Village dataset](https://github.com/spMohanty/PlantVillage-Dataset). It achieves the following results on the evaluation set: - Cross Entropy Loss: 0.15 - Accuracy: 0.9541 ## Intended uses & limitations For identifying common diseases in crops and assessing plant health. Not to be used as a replacement for an actual diagnosis from experts. ## Training and evaluation data The plant village dataset consists of 38 classes of diseases in common crops (including healthy/normal crops). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 256 - eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 6 ### Framework versions - Transformers 4.27.3 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2

[ "apple scab", "apple with black rot", "cedar apple rust", "healthy apple", "healthy blueberry plant", "cherry with powdery mildew", "healthy cherry plant", "corn (maize) with cercospora and gray leaf spot", "corn (maize) with common rust", "corn (maize) with northern leaf blight", "healthy corn (maize) plant", "grape with black rot", "grape with esca (black measles)", "grape with isariopsis leaf spot", "healthy grape plant", "orange with citrus greening", "peach with bacterial spot", "healthy peach plant", "bell pepper with bacterial spot", "healthy bell pepper plant", "potato with early blight", "potato with late blight", "healthy potato plant", "healthy raspberry plant", "healthy soybean plant", "squash with powdery mildew", "strawberry with leaf scorch", "healthy strawberry plant", "tomato with bacterial spot", "tomato with early blight", "tomato with late blight", "tomato with leaf mold", "tomato with septoria leaf spot", "tomato with spider mites or two-spotted spider mite", "tomato with target spot", "tomato yellow leaf curl virus", "tomato mosaic virus", "healthy tomato plant" ]

merve/beans-vit-224

# beans-vit-224 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.3256 - Accuracy: 0.9375 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0032 | 0.98 | 16 | 0.6540 | 0.8828 | | 0.4711 | 1.97 | 32 | 0.4180 | 0.9297 | | 0.3711 | 2.95 | 48 | 0.3256 | 0.9375 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "angular_leaf_spot", "bean_rust", "healthy" ]

platzi/platzi-vit-model_JPLC

# platzi-vit-model_JPLC 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0400 - Accuracy: 0.9850 ## 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: 8 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1287 | 3.85 | 500 | 0.0400 | 0.9850 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "angular_leaf_spot", "bean_rust", "healthy" ]

dima806/surface_crack_image_detection

Check whether there is a surface crack given surface image. See https://www.kaggle.com/code/dima806/surface-crack-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Positive 0.9988 0.9995 0.9991 4000 Negative 0.9995 0.9988 0.9991 4000 accuracy 0.9991 8000 macro avg 0.9991 0.9991 0.9991 8000 weighted avg 0.9991 0.9991 0.9991 8000 ```

[ "positive", "negative" ]

romitbarua/autotrain-deepfakeface_only_faces-94737146192

# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 33.9961 ## Validation Metricsg loss: 0.479949951171875 f1_macro: 0.813704872275267 f1_micro: 0.8058823529411765 f1_weighted: 0.8066289996092723 precision_macro: 0.8195022394186146 precision_micro: 0.8058823529411765 precision_weighted: 0.808552105878264 recall_macro: 0.8091827901264657 recall_micro: 0.8058823529411765 recall_weighted: 0.8058823529411765 accuracy: 0.8058823529411765

[ "inpainting", "insight", "real", "text2img" ]

sweetzinc/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: 0.1113 - Accuracy: 0.9641 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1937 | 1.0 | 190 | 0.1113 | 0.9641 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cpu - Datasets 2.14.5 - Tokenizers 0.14.1

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

dima806/buscuit_wrappers_image_detection

Returns biscuit wrapper type based on an image with about 93% accuracy. See https://www.kaggle.com/code/dima806/biscuit-wrappers-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Americana Coconut Cookies 0.9677 0.9677 0.9677 31 Amul Chocolate Cookies 0.9688 1.0000 0.9841 31 Amul Elaichi Rusk 0.9143 1.0000 0.9552 32 Bhagwati Choco Vanilla Puff Biscuits 1.0000 1.0000 1.0000 32 Bhagwati Lemony Puff Biscuits 1.0000 1.0000 1.0000 31 Bisk Farm Sugar Free Biscuits 0.9688 1.0000 0.9841 31 Bonn Jeera Bite Biscuits 1.0000 1.0000 1.0000 31 Britannia 50-50 Maska Chaska 0.8750 0.4516 0.5957 31 Britannia 50-50 Maska Chaska Salted Biscuits 0.5111 0.7419 0.6053 31 Britannia 50-50 Potazos - Masti Masala 1.0000 1.0000 1.0000 31 Britannia 50-50 Sweet and Salty Biscuits 1.0000 0.8387 0.9123 31 Britannia 50-50 Timepass Classic Salted Biscuit 1.0000 1.0000 1.0000 31 Britannia Biscafe Coffee Cracker 0.8333 0.6452 0.7273 31 Britannia Bourbon 1.0000 0.8710 0.9310 31 Britannia Bourbon The Original Cream Biscuits 0.8889 1.0000 0.9412 32 Britannia Chocolush - Pure Magic 0.7045 1.0000 0.8267 31 Britannia Good Day - Chocochip Cookies 1.0000 0.9677 0.9836 31 Britannia Good Day Cashew Almond Cookies 0.6944 0.8065 0.7463 31 Britannia Good Day Harmony Biscuit 1.0000 0.7812 0.8772 32 Britannia Good Day Pista Badam Cookies 0.8378 1.0000 0.9118 31 Britannia Little Hearts 0.9688 1.0000 0.9841 31 Britannia Marie Gold Biscuit 1.0000 0.9688 0.9841 32 Britannia Milk Bikis Milk Biscuits 0.7381 1.0000 0.8493 31 Britannia Nice Time - Coconut Biscuits 0.8889 1.0000 0.9412 32 Britannia Nutri Choice Oats Cookies - Chocolate and Almonds 0.7500 0.8710 0.8060 31 Britannia Nutri Choice Oats Cookies - Orange With Almonds 1.0000 0.7097 0.8302 31 Britannia Nutri Choice Seed Biscuits 1.0000 0.9032 0.9492 31 Britannia Nutri Choice Sugar Free Cream Cracker Biscuits 1.0000 1.0000 1.0000 31 Britannia Nutrichoice Herbs Biscuits 1.0000 1.0000 1.0000 31 Britannia Tiger Glucose Biscuit 0.9667 0.9355 0.9508 31 Britannia Tiger Kreemz - Chocolate Cream Biscuits 0.9091 0.9375 0.9231 32 Britannia Tiger Kreemz - Elaichi Cream Biscuits 0.9688 1.0000 0.9841 31 Britannia Tiger Kreemz - Orange Cream Biscuits 0.8889 0.7742 0.8276 31 Britannia Tiger Krunch Chocochips Biscuit 0.8710 0.8710 0.8710 31 Britannia Treat Chocolate Cream Biscuits 1.0000 0.9032 0.9492 31 Britannia Treat Crazy Pineapple Cream Biscuit 0.9697 1.0000 0.9846 32 Britannia Treat Jim Jam Cream Biscuit 1.0000 1.0000 1.0000 31 Britannia Treat Osom Orange Cream Biscuit 0.9667 0.9355 0.9508 31 Britannia Vita Marie Gold Biscuits 1.0000 1.0000 1.0000 31 Cadbury Bournvita Biscuits 0.9667 0.9062 0.9355 32 Cadbury Chocobakes Choc Filled Cookies 1.0000 1.0000 1.0000 32 Cadbury Oreo Chocolate Flavour Biscuit Cream Sandwich 1.0000 0.8065 0.8929 31 Cadbury Oreo Strawberry Flavour Creme Sandwich Biscuit 1.0000 0.9677 0.9836 31 Canberra Big Orange Cream Biscuits 1.0000 0.8125 0.8966 32 CookieMan Hand Pound Chocolate Cookies 0.9394 1.0000 0.9688 31 Cremica Coconut Cookies 1.0000 1.0000 1.0000 31 Cremica Elaichi Sandwich Biscuits 1.0000 1.0000 1.0000 31 Cremica Jeera Lite 1.0000 0.9677 0.9836 31 Cremica Non-Stop Thin Potato Crackers - Baked, Crunchy Masala 1.0000 0.9355 0.9667 31 Cremica Orange Sandwich Biscuits 1.0000 0.8710 0.9310 31 Krown Black Magic Cream Biscuits 0.9655 0.9032 0.9333 31 MARIO Coconut Crunchy Biscuits 0.8378 1.0000 0.9118 31 McVities Bourbon Cream Biscuits 0.9688 0.9688 0.9688 32 McVities Dark Cookie Cream 1.0000 0.8065 0.8929 31 McVities Marie Biscuit 0.8710 0.8710 0.8710 31 Parle 20-20 Cashew Cookies 1.0000 1.0000 1.0000 32 Parle 20-20 Nice Biscuits 1.0000 1.0000 1.0000 32 Parle Happy Happy Choco-Chip Cookies 0.9394 1.0000 0.9688 31 Parle Hide and Seek 0.9333 0.9032 0.9180 31 Parle Hide and Seek - Black Bourbon Choco 0.9032 0.9032 0.9032 31 Parle Hide and Seek - Milano Choco Chip Cookies 1.0000 0.9677 0.9836 31 Parle Hide and Seek Caffe Mocha Cookies 0.9565 0.7097 0.8148 31 Parle Hide and Seek Chocolate and Almonds 0.9655 0.8750 0.9180 32 Parle Krack Jack Original Sweet and Salty Cracker Biscuit 0.9333 0.9032 0.9180 31 Parle Krackjack Biscuits 0.9643 0.8710 0.9153 31 Parle Magix Sandwich Biscuits - Chocolate 0.9375 0.9677 0.9524 31 Parle Milk Shakti Biscuits 0.9091 0.9677 0.9375 31 Parle Monaco Biscuit - Classic Regular 1.0000 0.9688 0.9841 32 Parle Monaco Piri Piri 1.0000 0.9062 0.9508 32 Parle Platina Hide and Seek Creme Sandwich - Vanilla 0.9412 1.0000 0.9697 32 Parle-G Gold Gluco Biscuits 0.9677 0.9677 0.9677 31 Parle-G Original Gluco Biscuits 1.0000 0.9677 0.9836 31 Patanjali Doodh Biscuit 1.0000 0.9688 0.9841 32 Priyagold Butter Delite Biscuits 1.0000 1.0000 1.0000 31 Priyagold CNC Biscuits 1.0000 0.8065 0.8929 31 Priyagold Cheese Chacker Biscuits 0.9333 0.9032 0.9180 31 Priyagold Snacks Zig Zag Biscuits 0.9688 1.0000 0.9841 31 Richlite Rich Butter Cookies 0.9688 1.0000 0.9841 31 RiteBite Max Protein 7 Grain Breakfast Cookies - Cashew Delite 1.0000 1.0000 1.0000 31 Sagar Coconut Munch Biscuits 1.0000 1.0000 1.0000 31 Sri Sri Tattva Cashew Nut Cookies 1.0000 1.0000 1.0000 31 Sri Sri Tattva Choco Hazelnut Cookies 0.8056 0.9355 0.8657 31 Sri Sri Tattva Coconut Cookies 0.8378 1.0000 0.9118 31 Sri Sri Tattva Digestive Cookies 1.0000 0.8710 0.9310 31 Sunfeast All Rounder - Cream and Herb 1.0000 0.9355 0.9667 31 Sunfeast All Rounder - Thin, Light and Crunchy Potato Biscuit With Chatpata Masala Flavour 1.0000 0.8387 0.9123 31 Sunfeast Bounce Creme Biscuits 0.9259 0.8065 0.8621 31 Sunfeast Bounce Creme Biscuits - Elaichi 0.7949 1.0000 0.8857 31 Sunfeast Bounce Creme Biscuits - Pineapple Zing 0.7949 1.0000 0.8857 31 Sunfeast Dark Fantasy - Choco Creme 0.7949 1.0000 0.8857 31 Sunfeast Dark Fantasy Bourbon Biscuits 0.6889 1.0000 0.8158 31 Sunfeast Dark Fantasy Choco Fills 1.0000 0.8065 0.8929 31 Sunfeast Glucose Biscuits 0.9310 0.8710 0.9000 31 Sunfeast Moms Magic - Fruit and Milk Cookies 0.8158 1.0000 0.8986 31 Sunfeast Moms Magic - Rich Butter Cookies 1.0000 0.9677 0.9836 31 Sunfeast Moms Magic - Rich Cashew and Almond Cookies 1.0000 0.9062 0.9508 32 Tasties Chocochip Cookies 1.0000 1.0000 1.0000 31 Tasties Coconut Cookies 1.0000 0.8750 0.9333 32 UNIBIC Choco Chip Cookies 0.8333 0.9677 0.8955 31 UNIBIC Pista Badam Cookies 0.8857 1.0000 0.9394 31 UNIBIC Snappers Potato Crackers 0.9667 0.9355 0.9508 31 accuracy 0.9305 3152 macro avg 0.9396 0.9304 0.9306 3152 weighted avg 0.9398 0.9305 0.9307 3152 ```

[ "americana coconut cookies", "amul chocolate cookies", "amul elaichi rusk", "bhagwati choco vanilla puff biscuits", "bhagwati lemony puff biscuits", "bisk farm sugar free biscuits", "bonn jeera bite biscuits", "britannia 50-50 maska chaska", "britannia 50-50 maska chaska salted biscuits", "britannia 50-50 potazos - masti masala", "britannia 50-50 sweet and salty biscuits", "britannia 50-50 timepass classic salted biscuit", "britannia biscafe coffee cracker", "britannia bourbon", "britannia bourbon the original cream biscuits", "britannia chocolush - pure magic", "britannia good day - chocochip cookies", "britannia good day cashew almond cookies", "britannia good day harmony biscuit", "britannia good day pista badam cookies", "britannia little hearts", "britannia marie gold biscuit", "britannia milk bikis milk biscuits", "britannia nice time - coconut biscuits", "britannia nutri choice oats cookies - chocolate and almonds", "britannia nutri choice oats cookies - orange with almonds", "britannia nutri choice seed biscuits", "britannia nutri choice sugar free cream cracker biscuits", "britannia nutrichoice herbs biscuits", "britannia tiger glucose biscuit", "britannia tiger kreemz - chocolate cream biscuits", "britannia tiger kreemz - elaichi cream biscuits", "britannia tiger kreemz - orange cream biscuits", "britannia tiger krunch chocochips biscuit", "britannia treat chocolate cream biscuits", "britannia treat crazy pineapple cream biscuit", "britannia treat jim jam cream biscuit", "britannia treat osom orange cream biscuit", "britannia vita marie gold biscuits", "cadbury bournvita biscuits", "cadbury chocobakes choc filled cookies", "cadbury oreo chocolate flavour biscuit cream sandwich", "cadbury oreo strawberry flavour creme sandwich biscuit", "canberra big orange cream biscuits", "cookieman hand pound chocolate cookies", "cremica coconut cookies", "cremica elaichi sandwich biscuits", "cremica jeera lite", "cremica non-stop thin potato crackers - baked, crunchy masala", "cremica orange sandwich biscuits", "krown black magic cream biscuits", "mario coconut crunchy biscuits", "mcvities bourbon cream biscuits", "mcvities dark cookie cream", "mcvities marie biscuit", "parle 20-20 cashew cookies", "parle 20-20 nice biscuits", "parle happy happy choco-chip cookies", "parle hide and seek", "parle hide and seek - black bourbon choco", "parle hide and seek - milano choco chip cookies", "parle hide and seek caffe mocha cookies", "parle hide and seek chocolate and almonds", "parle krack jack original sweet and salty cracker biscuit", "parle krackjack biscuits", "parle magix sandwich biscuits - chocolate", "parle milk shakti biscuits", "parle monaco biscuit - classic regular", "parle monaco piri piri", "parle platina hide and seek creme sandwich - vanilla", "parle-g gold gluco biscuits", "parle-g original gluco biscuits", "patanjali doodh biscuit", "priyagold butter delite biscuits", "priyagold cnc biscuits", "priyagold cheese chacker biscuits", "priyagold snacks zig zag biscuits", "richlite rich butter cookies", "ritebite max protein 7 grain breakfast cookies - cashew delite", "sagar coconut munch biscuits", "sri sri tattva cashew nut cookies", "sri sri tattva choco hazelnut cookies", "sri sri tattva coconut cookies", "sri sri tattva digestive cookies", "sunfeast all rounder - cream and herb", "sunfeast all rounder - thin, light and crunchy potato biscuit with chatpata masala flavour", "sunfeast bounce creme biscuits", "sunfeast bounce creme biscuits - elaichi", "sunfeast bounce creme biscuits - pineapple zing", "sunfeast dark fantasy - choco creme", "sunfeast dark fantasy bourbon biscuits", "sunfeast dark fantasy choco fills", "sunfeast glucose biscuits", "sunfeast moms magic - fruit and milk cookies", "sunfeast moms magic - rich butter cookies", "sunfeast moms magic - rich cashew and almond cookies", "tasties chocochip cookies", "tasties coconut cookies", "unibic choco chip cookies", "unibic pista badam cookies", "unibic snappers potato crackers" ]

ericrong888/logo_classifier

# ericrong888/logo_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: 0.7196 - Validation Loss: 0.8069 - Train Accuracy: 1.0 - 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': 75, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 1.1054 | 1.0410 | 0.8333 | 0 | | 0.9869 | 0.9692 | 0.8333 | 1 | | 0.8856 | 0.9035 | 1.0 | 2 | | 0.8117 | 0.8585 | 1.0 | 3 | | 0.7196 | 0.8069 | 1.0 | 4 | ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "amazon", "starbucks", "wellsfargo" ]

wangyk22/histoSwin-base

# histoSwin-base This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the nctcrche100_k dataset. It achieves the following results on the evaluation set: - Loss: 0.1260 - Accuracy: 0.9709 ## Model description More information needed ## Intended uses & limitations For TMA classification. ## 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: 2 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0386 | 1.0 | 1562 | 0.1740 | 0.9592 | | 0.05 | 2.0 | 3125 | 0.2313 | 0.9524 | | 0.0324 | 3.0 | 4687 | 0.2606 | 0.9604 | | 0.005 | 4.0 | 6250 | 0.1724 | 0.9660 | | 0.0037 | 5.0 | 7810 | 0.1260 | 0.9709 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

[ "str", "mus", "adi", "tum", "deb", "lym", "back", "muc", "norm" ]

dima806/beard_face_image_detection

Predicts the presence of a beard given a facial image. See https://www.kaggle.com/code/dima806/beard-face-image-detection-vit for more details. ``` Classification report: precision recall f1-score support Beard 1.0000 1.0000 1.0000 34 No Beard 1.0000 1.0000 1.0000 34 accuracy 1.0000 68 macro avg 1.0000 1.0000 1.0000 68 weighted avg 1.0000 1.0000 1.0000 68 ```

[ "beard", "no beard" ]

dima806/food_beverages_japan_image_detection

Returns whether the Japanese food or beverage is in an image with about 89% accuracy. See https://www.kaggle.com/code/dima806/food-beverages-japan-image-detection-vit for more details. ``` Classification report: precision recall f1-score support food 0.8898 0.8879 0.8889 473 beverage 0.8882 0.8901 0.8891 473 accuracy 0.8890 946 macro avg 0.8890 0.8890 0.8890 946 weighted avg 0.8890 0.8890 0.8890 946 ```

[ "food", "beverage" ]

dima806/tyre_quality_image_detection

Retuns tyre quality given a tyre image with about 99.3% accuracy. See https://www.kaggle.com/code/dima806/tyre-quality-image-detection-vit for more details. ``` Classification report: precision recall f1-score support defective 1.0000 0.9854 0.9926 411 good 0.9856 1.0000 0.9928 412 accuracy 0.9927 823 macro avg 0.9928 0.9927 0.9927 823 weighted avg 0.9928 0.9927 0.9927 823 ```

[ "defective", "good" ]

dima806/full_flat_tyre_image_detection

Check whether the tyre is flat given an image. See https://www.kaggle.com/code/dima806/full-flat-tyre-image-detection-vit for more details. ``` Classification report: precision recall f1-score support flat 1.0000 1.0000 1.0000 60 no-tire 1.0000 1.0000 1.0000 60 full 1.0000 1.0000 1.0000 60 accuracy 1.0000 180 macro avg 1.0000 1.0000 1.0000 180 weighted avg 1.0000 1.0000 1.0000 180 ```

[ "flat", "no-tire", "full" ]

romitbarua/autotrain-deepfakeface_only_faces_insightface-94902146221

# Model Trained Using AutoTrain - Problem type: Image Classification - CO2 Emissions (in grams): 0.3674 ## Validation Metricsg loss: 0.39755979180336 f1: 0.8185757948998676 precision: 0.8184637839810254 recall: 0.8186878364814308 auc: 0.9113633194857089 accuracy: 0.8150662636596141

[ "insight", "real" ]

galbitang/autotrain-jeongmi_lamp-94917146228

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94917146228 - CO2 Emissions (in grams): 0.0453 ## Validation Metrics - Loss: 1.114 - Accuracy: 0.642 - Macro F1: 0.465 - Micro F1: 0.642 - Weighted F1: 0.612 - Macro Precision: 0.482 - Micro Precision: 0.642 - Weighted Precision: 0.595 - Macro Recall: 0.468 - Micro Recall: 0.642 - Weighted Recall: 0.642

[ "classicantique", "frenchprovence", "vintageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]

galbitang/autotrain-jin0_table-94921146229

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94921146229 - CO2 Emissions (in grams): 0.1008 ## Validation Metrics - Loss: 0.892 - Accuracy: 0.727 - Macro F1: 0.672 - Micro F1: 0.727 - Weighted F1: 0.715 - Macro Precision: 0.682 - Micro Precision: 0.727 - Weighted Precision: 0.709 - Macro Recall: 0.675 - Micro Recall: 0.727 - Weighted Recall: 0.727

[ "classicantique", "frenchprovence", "vintageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]

galbitang/autotrain-jeongmi_chair-94919146230

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 94919146230 - CO2 Emissions (in grams): 0.0582 ## Validation Metrics - Loss: 0.714 - Accuracy: 0.735 - Macro F1: 0.622 - Micro F1: 0.735 - Weighted F1: 0.731 - Macro Precision: 0.678 - Micro Precision: 0.735 - Weighted Precision: 0.745 - Macro Recall: 0.597 - Micro Recall: 0.735 - Weighted Recall: 0.735

[ "classsicantique", "frenchprovence", "vintatageretro", "industrial", "koreaaisa", "lovelyromantic", "minimalsimple", "modern", "natural", "notherneurope", "unique" ]