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James4u/5FTdqwrPyVGw59V9YfKP1P1KJxHJsQo6Yp75wFVbfxdkedXU
|
# Model Card for Model ID
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|
[
"none",
"roadwork"
] |
James4u/5Gbbw6WCUiQrscuHjLyjaayaZCPaDpBhPBxybgtLDmDCjFBV
|
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|
[
"none",
"roadwork"
] |
James4u/5FeATnLym4BQ52qN65oC6Jkq9VmbtCKWQMjur388n4TJaswp
|
# Model Card for Model ID
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|
[
"none",
"roadwork"
] |
loveh/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3224
- Accuracy: 0.922
## Model description
More information needed
## Intended uses & 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5037 | 1.0 | 500 | 0.6134 | 0.907 |
| 0.3127 | 2.0 | 1000 | 0.3533 | 0.916 |
| 0.1961 | 3.0 | 1500 | 0.3224 | 0.922 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
ibuki95/vision_172_21
|
# Model Card for Model ID
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|
[
"none",
"roadwork"
] |
Runnies23/swin-tiny-patch4-finetuned-lung-cancer-ct-scans_liver_hsitopathlogy
|
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[
"f0",
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"f2",
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"f4"
] |
ProDev9515/roadwork-72-ctLiEN
|
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gfortune/roadwork3
|
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gfortune/roadwork4
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gfortune/roadwork5
|
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gfortune/roadwork6
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gfortune/roadwork22
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gfortune/roadwork23
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gfortune/roadwork24
|
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gfortune/roadwork25
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gfortune/roadwork26
|
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"none",
"roadwork"
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plumpyfield/natix_v2-010
|
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gfortune/roadwork27
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"roadwork"
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plumpyfield/natix_v2-011
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gfortune/roadwork28
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plumpyfield/natix_v2-012
|
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gfortune/roadwork29
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gfortune/roadwork30
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gfortune/roadwork31
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obiwan001/roadwork273
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gfortune/roadwork32
|
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gfortune/roadwork33
|
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gfortune/roadwork34
|
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gfortune/roadwork35
|
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gfortune/roadwork36
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gfortune/roadwork37
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plumpyfield/natix_v2-013
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kalemlhub/sn72-roadwork-u2jFDo
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kalemlhub/sn72-roadwork-siw79i
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ProDev9515/roadwork-72-2QPR44
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ProDev9515/roadwork-72-SjXzsD
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ProDev9515/roadwork-72-T8cajD
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ProDev9515/roadwork-72-jNL6JU
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ProDev9515/roadwork-72-q77pfu
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ProDev9515/roadwork-72-fnAiRD
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James4u/5GZ3PdFBvzV2U4hTXcfpAKm8D5b2rvkRyQGUscHVzqEh2zMY
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plumpyfield/natix_v2-014
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plumpyfield/natix_v2-015
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[
"none",
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James4u/5H8vf8BF4Rp2qozDBDte9S9jECA8QPsSyARfrNqhY2Ngxgtf
|
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James4u/5Cw9aT61UScPv4jLcxK8YbXThthGRAKqcyf63e97pnaLKK64
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ProDev9515/roadwork-72-mDTv8K
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ProDev9515/roadwork-72-fMtYns
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ProDev9515/roadwork-72-vC5iMt
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ProDev9515/roadwork-72-GCoFy4
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ProDev9515/roadwork-72-QLLnPq
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ProDev9515/roadwork-72-DvrRf4
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ProDev9515/roadwork-72-cbfLJk
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ProDev9515/roadwork-72-ymvPKA
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James4u/5CLuxcWQWqSHpvq4HGYnCuppjZ3psqThfDDfadgKsV2f7beC
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kalemlhub/sn72-roadwork-6UKWDd
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[
"none",
"roadwork"
] |
James4u/5FCAQ9dJpfyLkDkaoZeN7g5xTqDEMiRCZdafL65wr3V2QPUQ
|
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[
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James4u/5EAWYGDTumAKpCk7W9fypPN1qEgNKtRnGRs2ewnubY1K2sz2
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[
"none",
"roadwork"
] |
James4u/5Hdo1X5LpFcHTJaSWmU5PFyWUnbVy6EPgvseR99xarfGqrfZ
|
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[
"none",
"roadwork"
] |
iceberg0142/natix-test
|
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[
"none",
"roadwork"
] |
iceberg0142/natix-test-1
|
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[
"none",
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James4u/5G94yeMv9MZyjPcHUdHWuB1hjLSivAH56xTawYwCQVw3Zp9R
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|
[
"none",
"roadwork"
] |
James4u/5HNZVL9PRTr6vrVD1xiQUJBxMizHX2C8hvYiqBfiCTyr5q4Q
|
# Model Card for Model ID
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[
"none",
"roadwork"
] |
Larbz-7/test
|
<!-- 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. -->
# radioup-model-v5
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: 0.2171
- Accuracy: 0.9333
- F1: 0.9334
- Precision: 0.9384
- Recall: 0.9333
## Model description
More information needed
## Intended uses & 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.1325 | 0.1136 | 100 | 2.0321 | 0.1833 | 0.1024 | 0.0943 | 0.1833 |
| 1.8745 | 0.2273 | 200 | 2.0416 | 0.1667 | 0.0541 | 0.0382 | 0.1667 |
| 2.1848 | 0.3409 | 300 | 2.0512 | 0.1767 | 0.0830 | 0.0671 | 0.1767 |
| 2.2135 | 0.4545 | 400 | 2.0171 | 0.1667 | 0.0476 | 0.0278 | 0.1667 |
| 2.0455 | 0.5682 | 500 | 2.0368 | 0.2533 | 0.1505 | 0.1190 | 0.2533 |
| 2.178 | 0.6818 | 600 | 1.9420 | 0.2667 | 0.1447 | 0.1094 | 0.2667 |
| 2.0375 | 0.7955 | 700 | 1.9715 | 0.2067 | 0.1288 | 0.1051 | 0.2067 |
| 2.1917 | 0.9091 | 800 | 1.9342 | 0.2733 | 0.1684 | 0.1276 | 0.2733 |
| 1.7963 | 1.0227 | 900 | 1.8921 | 0.2267 | 0.1141 | 0.0763 | 0.2267 |
| 2.0072 | 1.1364 | 1000 | 1.9752 | 0.19 | 0.1125 | 0.0960 | 0.19 |
| 1.9791 | 1.25 | 1100 | 1.8841 | 0.2667 | 0.1682 | 0.1684 | 0.2667 |
| 2.1029 | 1.3636 | 1200 | 2.0206 | 0.2367 | 0.1075 | 0.0795 | 0.2367 |
| 2.1388 | 1.4773 | 1300 | 1.8935 | 0.2767 | 0.1514 | 0.1132 | 0.2767 |
| 2.0979 | 1.5909 | 1400 | 1.9137 | 0.2033 | 0.1131 | 0.1240 | 0.2033 |
| 2.3026 | 1.7045 | 1500 | 1.9526 | 0.27 | 0.1616 | 0.1235 | 0.27 |
| 1.5513 | 1.8182 | 1600 | 1.8520 | 0.2867 | 0.1743 | 0.1475 | 0.2867 |
| 2.3849 | 1.9318 | 1700 | 1.8976 | 0.21 | 0.1004 | 0.0768 | 0.21 |
| 1.8575 | 2.0455 | 1800 | 1.9197 | 0.21 | 0.1011 | 0.0782 | 0.21 |
| 1.8156 | 2.1591 | 1900 | 1.8727 | 0.2767 | 0.1670 | 0.1279 | 0.2767 |
| 1.7686 | 2.2727 | 2000 | 1.8727 | 0.3067 | 0.2090 | 0.1648 | 0.3067 |
| 1.6668 | 2.3864 | 2100 | 1.9466 | 0.24 | 0.1717 | 0.1441 | 0.24 |
| 2.0095 | 2.5 | 2200 | 1.9201 | 0.22 | 0.1281 | 0.0992 | 0.22 |
| 2.014 | 2.6136 | 2300 | 1.8951 | 0.2833 | 0.1770 | 0.1412 | 0.2833 |
| 2.1405 | 2.7273 | 2400 | 1.8732 | 0.29 | 0.1872 | 0.1483 | 0.29 |
| 1.4917 | 2.8409 | 2500 | 1.8817 | 0.2867 | 0.2120 | 0.1767 | 0.2867 |
| 2.2975 | 2.9545 | 2600 | 1.8635 | 0.2567 | 0.1860 | 0.1573 | 0.2567 |
| 1.5702 | 3.0682 | 2700 | 1.7966 | 0.3133 | 0.2017 | 0.1621 | 0.3133 |
| 1.9689 | 3.1818 | 2800 | 1.9080 | 0.2667 | 0.1841 | 0.1477 | 0.2667 |
| 1.947 | 3.2955 | 2900 | 1.8981 | 0.32 | 0.2062 | 0.1680 | 0.32 |
| 1.4395 | 3.4091 | 3000 | 1.7913 | 0.31 | 0.2131 | 0.1663 | 0.31 |
| 1.759 | 3.5227 | 3100 | 1.8260 | 0.2567 | 0.1808 | 0.1521 | 0.2567 |
| 1.9838 | 3.6364 | 3200 | 2.0104 | 0.2733 | 0.1828 | 0.1779 | 0.2733 |
| 2.3316 | 3.75 | 3300 | 1.8896 | 0.2833 | 0.2262 | 0.2220 | 0.2833 |
| 2.1299 | 3.8636 | 3400 | 1.8655 | 0.26 | 0.2294 | 0.2674 | 0.26 |
| 1.7605 | 3.9773 | 3500 | 1.7924 | 0.3133 | 0.2262 | 0.2218 | 0.3133 |
| 1.2404 | 4.0909 | 3600 | 1.7928 | 0.3067 | 0.2075 | 0.1666 | 0.3067 |
| 2.1577 | 4.2045 | 3700 | 1.7855 | 0.31 | 0.2178 | 0.1988 | 0.31 |
| 2.3638 | 4.3182 | 3800 | 1.6657 | 0.36 | 0.2616 | 0.2561 | 0.36 |
| 1.5629 | 4.4318 | 3900 | 1.7126 | 0.3467 | 0.2556 | 0.2339 | 0.3467 |
| 1.3863 | 4.5455 | 4000 | 1.7081 | 0.3533 | 0.2577 | 0.2127 | 0.3533 |
| 1.7062 | 4.6591 | 4100 | 1.7079 | 0.3433 | 0.3172 | 0.3025 | 0.3433 |
| 1.8169 | 4.7727 | 4200 | 1.7166 | 0.3233 | 0.2824 | 0.2959 | 0.3233 |
| 1.7194 | 4.8864 | 4300 | 1.6868 | 0.3367 | 0.2875 | 0.2966 | 0.3367 |
| 1.3832 | 5.0 | 4400 | 1.7165 | 0.35 | 0.2718 | 0.2567 | 0.35 |
| 2.5918 | 5.1136 | 4500 | 1.7122 | 0.3433 | 0.2933 | 0.2838 | 0.3433 |
| 1.5676 | 5.2273 | 4600 | 1.7024 | 0.3333 | 0.2896 | 0.3166 | 0.3333 |
| 2.1208 | 5.3409 | 4700 | 1.7090 | 0.3367 | 0.2856 | 0.3237 | 0.3367 |
| 1.8254 | 5.4545 | 4800 | 1.6732 | 0.39 | 0.3092 | 0.2774 | 0.39 |
| 1.6147 | 5.5682 | 4900 | 1.6215 | 0.3633 | 0.3220 | 0.3153 | 0.3633 |
| 0.972 | 5.6818 | 5000 | 1.7132 | 0.3667 | 0.2810 | 0.2491 | 0.3667 |
| 1.617 | 5.7955 | 5100 | 1.7317 | 0.3833 | 0.3096 | 0.2754 | 0.3833 |
| 1.5304 | 5.9091 | 5200 | 1.8030 | 0.3233 | 0.2622 | 0.2641 | 0.3233 |
| 1.5496 | 6.0227 | 5300 | 1.6435 | 0.3633 | 0.3171 | 0.3313 | 0.3633 |
| 1.2992 | 6.1364 | 5400 | 1.7029 | 0.3533 | 0.2990 | 0.2643 | 0.3533 |
| 1.4571 | 6.25 | 5500 | 1.7118 | 0.3933 | 0.3344 | 0.3007 | 0.3933 |
| 1.2905 | 6.3636 | 5600 | 1.5744 | 0.41 | 0.3646 | 0.3419 | 0.41 |
| 2.5343 | 6.4773 | 5700 | 1.6429 | 0.3867 | 0.3351 | 0.3487 | 0.3867 |
| 2.0219 | 6.5909 | 5800 | 1.7131 | 0.38 | 0.3056 | 0.2979 | 0.38 |
| 1.7597 | 6.7045 | 5900 | 1.6401 | 0.4033 | 0.3406 | 0.3349 | 0.4033 |
| 1.4103 | 6.8182 | 6000 | 1.6261 | 0.41 | 0.3560 | 0.4100 | 0.41 |
| 2.2024 | 6.9318 | 6100 | 1.7209 | 0.37 | 0.3233 | 0.3519 | 0.37 |
| 1.2347 | 7.0455 | 6200 | 1.5996 | 0.39 | 0.3559 | 0.3491 | 0.39 |
| 1.2568 | 7.1591 | 6300 | 1.5115 | 0.4 | 0.3341 | 0.3365 | 0.4 |
| 1.7057 | 7.2727 | 6400 | 1.5506 | 0.42 | 0.3743 | 0.3457 | 0.42 |
| 1.4601 | 7.3864 | 6500 | 1.4624 | 0.44 | 0.3986 | 0.3785 | 0.44 |
| 1.8181 | 7.5 | 6600 | 1.4508 | 0.4533 | 0.3842 | 0.3917 | 0.4533 |
| 1.5281 | 7.6136 | 6700 | 1.6279 | 0.44 | 0.3812 | 0.3438 | 0.44 |
| 2.1477 | 7.7273 | 6800 | 1.6037 | 0.42 | 0.3809 | 0.3940 | 0.42 |
| 1.6771 | 7.8409 | 6900 | 1.7062 | 0.4 | 0.3404 | 0.3308 | 0.4 |
| 1.6814 | 7.9545 | 7000 | 1.6030 | 0.3933 | 0.3495 | 0.3876 | 0.3933 |
| 1.7722 | 8.0682 | 7100 | 1.5115 | 0.4333 | 0.4010 | 0.4336 | 0.4333 |
| 1.027 | 8.1818 | 7200 | 1.6255 | 0.41 | 0.3556 | 0.3784 | 0.41 |
| 1.6206 | 8.2955 | 7300 | 1.8537 | 0.41 | 0.3379 | 0.3504 | 0.41 |
| 1.7434 | 8.4091 | 7400 | 1.5531 | 0.4267 | 0.3882 | 0.4349 | 0.4267 |
| 2.1487 | 8.5227 | 7500 | 1.4898 | 0.4367 | 0.3854 | 0.4242 | 0.4367 |
| 1.6752 | 8.6364 | 7600 | 1.6730 | 0.4133 | 0.3432 | 0.3759 | 0.4133 |
| 2.0036 | 8.75 | 7700 | 1.6937 | 0.3933 | 0.3543 | 0.3492 | 0.3933 |
| 2.0274 | 8.8636 | 7800 | 1.6572 | 0.42 | 0.3877 | 0.3727 | 0.42 |
| 1.2597 | 8.9773 | 7900 | 1.5253 | 0.4633 | 0.4251 | 0.5100 | 0.4633 |
| 1.1452 | 9.0909 | 8000 | 1.4956 | 0.4567 | 0.4039 | 0.3766 | 0.4567 |
| 1.398 | 9.2045 | 8100 | 1.6662 | 0.3633 | 0.3315 | 0.4616 | 0.3633 |
| 1.3116 | 9.3182 | 8200 | 1.7941 | 0.42 | 0.3844 | 0.3662 | 0.42 |
| 1.1882 | 9.4318 | 8300 | 1.7093 | 0.4967 | 0.4518 | 0.5084 | 0.4967 |
| 1.7041 | 9.5455 | 8400 | 1.4589 | 0.5033 | 0.4848 | 0.4948 | 0.5033 |
| 1.3751 | 9.6591 | 8500 | 1.5243 | 0.49 | 0.4396 | 0.4786 | 0.49 |
| 2.351 | 9.7727 | 8600 | 1.3950 | 0.49 | 0.4787 | 0.4788 | 0.49 |
| 1.5726 | 9.8864 | 8700 | 1.7219 | 0.46 | 0.4054 | 0.3889 | 0.46 |
| 1.8469 | 10.0 | 8800 | 1.4327 | 0.4467 | 0.4139 | 0.4410 | 0.4467 |
| 1.672 | 10.1136 | 8900 | 1.5570 | 0.4633 | 0.4271 | 0.4525 | 0.4633 |
| 1.4056 | 10.2273 | 9000 | 1.4028 | 0.48 | 0.4504 | 0.5025 | 0.48 |
| 1.0054 | 10.3409 | 9100 | 1.5788 | 0.4767 | 0.4473 | 0.5325 | 0.4767 |
| 1.6323 | 10.4545 | 9200 | 1.3638 | 0.51 | 0.5005 | 0.5339 | 0.51 |
| 2.2599 | 10.5682 | 9300 | 1.9310 | 0.4467 | 0.3990 | 0.4616 | 0.4467 |
| 1.0275 | 10.6818 | 9400 | 1.4148 | 0.5133 | 0.4983 | 0.5204 | 0.5133 |
| 1.5798 | 10.7955 | 9500 | 1.4635 | 0.49 | 0.4499 | 0.4870 | 0.49 |
| 1.2391 | 10.9091 | 9600 | 1.3368 | 0.53 | 0.5224 | 0.5328 | 0.53 |
| 1.4914 | 11.0227 | 9700 | 1.4432 | 0.5033 | 0.4696 | 0.5119 | 0.5033 |
| 0.5783 | 11.1364 | 9800 | 1.7681 | 0.4567 | 0.4195 | 0.4937 | 0.4567 |
| 1.6834 | 11.25 | 9900 | 1.5489 | 0.4933 | 0.4432 | 0.5159 | 0.4933 |
| 1.0594 | 11.3636 | 10000 | 1.4299 | 0.54 | 0.5331 | 0.5552 | 0.54 |
| 1.923 | 11.4773 | 10100 | 1.5363 | 0.52 | 0.5089 | 0.5122 | 0.52 |
| 0.9397 | 11.5909 | 10200 | 1.4348 | 0.5433 | 0.5046 | 0.5540 | 0.5433 |
| 1.3073 | 11.7045 | 10300 | 1.5407 | 0.5033 | 0.4829 | 0.5160 | 0.5033 |
| 1.6012 | 11.8182 | 10400 | 1.5216 | 0.5033 | 0.4703 | 0.5615 | 0.5033 |
| 1.439 | 11.9318 | 10500 | 1.4947 | 0.5133 | 0.4914 | 0.5764 | 0.5133 |
| 1.3553 | 12.0455 | 10600 | 1.3487 | 0.55 | 0.5370 | 0.5715 | 0.55 |
| 1.1319 | 12.1591 | 10700 | 1.4175 | 0.5433 | 0.5167 | 0.5064 | 0.5433 |
| 1.6714 | 12.2727 | 10800 | 1.4944 | 0.51 | 0.4915 | 0.5337 | 0.51 |
| 0.8275 | 12.3864 | 10900 | 1.3419 | 0.5533 | 0.5447 | 0.5615 | 0.5533 |
| 1.4667 | 12.5 | 11000 | 1.4655 | 0.55 | 0.5147 | 0.5655 | 0.55 |
| 1.4694 | 12.6136 | 11100 | 1.3455 | 0.5433 | 0.5133 | 0.5032 | 0.5433 |
| 1.3876 | 12.7273 | 11200 | 1.3658 | 0.57 | 0.5424 | 0.6040 | 0.57 |
| 1.4705 | 12.8409 | 11300 | 1.3043 | 0.57 | 0.5533 | 0.5843 | 0.57 |
| 2.4678 | 12.9545 | 11400 | 1.2052 | 0.58 | 0.5660 | 0.6041 | 0.58 |
| 1.6037 | 13.0682 | 11500 | 1.3322 | 0.54 | 0.5304 | 0.5558 | 0.54 |
| 0.8944 | 13.1818 | 11600 | 1.3380 | 0.57 | 0.5373 | 0.6221 | 0.57 |
| 1.2658 | 13.2955 | 11700 | 1.5095 | 0.5267 | 0.5038 | 0.5470 | 0.5267 |
| 2.0735 | 13.4091 | 11800 | 1.2082 | 0.6167 | 0.6047 | 0.6207 | 0.6167 |
| 1.0294 | 13.5227 | 11900 | 1.1833 | 0.5967 | 0.5891 | 0.6527 | 0.5967 |
| 0.8701 | 13.6364 | 12000 | 1.3908 | 0.5767 | 0.5572 | 0.6046 | 0.5767 |
| 1.8376 | 13.75 | 12100 | 1.2818 | 0.59 | 0.5833 | 0.6043 | 0.59 |
| 1.1665 | 13.8636 | 12200 | 1.1787 | 0.61 | 0.6032 | 0.6067 | 0.61 |
| 1.578 | 13.9773 | 12300 | 1.4198 | 0.5533 | 0.5540 | 0.5973 | 0.5533 |
| 0.7219 | 14.0909 | 12400 | 1.4571 | 0.5733 | 0.5588 | 0.5684 | 0.5733 |
| 0.9484 | 14.2045 | 12500 | 1.3443 | 0.5733 | 0.5676 | 0.5980 | 0.5733 |
| 1.1644 | 14.3182 | 12600 | 1.1967 | 0.64 | 0.6254 | 0.6571 | 0.64 |
| 1.6737 | 14.4318 | 12700 | 1.1579 | 0.6133 | 0.6114 | 0.6493 | 0.6133 |
| 0.4206 | 14.5455 | 12800 | 1.1327 | 0.65 | 0.6485 | 0.6636 | 0.65 |
| 1.7619 | 14.6591 | 12900 | 1.2594 | 0.6 | 0.5982 | 0.6673 | 0.6 |
| 1.4951 | 14.7727 | 13000 | 1.1301 | 0.65 | 0.6293 | 0.6649 | 0.65 |
| 2.0405 | 14.8864 | 13100 | 1.1906 | 0.63 | 0.6192 | 0.6357 | 0.63 |
| 1.5169 | 15.0 | 13200 | 1.1900 | 0.6333 | 0.6238 | 0.6446 | 0.6333 |
| 1.517 | 15.1136 | 13300 | 1.1868 | 0.6367 | 0.6269 | 0.6431 | 0.6367 |
| 1.8484 | 15.2273 | 13400 | 1.2448 | 0.6433 | 0.6236 | 0.6798 | 0.6433 |
| 1.3278 | 15.3409 | 13500 | 1.0725 | 0.66 | 0.6574 | 0.6810 | 0.66 |
| 1.2777 | 15.4545 | 13600 | 1.1123 | 0.65 | 0.6500 | 0.6620 | 0.65 |
| 1.5833 | 15.5682 | 13700 | 1.1579 | 0.6533 | 0.6505 | 0.6630 | 0.6533 |
| 1.2333 | 15.6818 | 13800 | 1.0351 | 0.67 | 0.6679 | 0.7362 | 0.67 |
| 1.9318 | 15.7955 | 13900 | 1.0084 | 0.6833 | 0.6821 | 0.6972 | 0.6833 |
| 1.2167 | 15.9091 | 14000 | 1.0112 | 0.67 | 0.6688 | 0.6893 | 0.67 |
| 1.3955 | 16.0227 | 14100 | 1.1367 | 0.65 | 0.6424 | 0.6675 | 0.65 |
| 1.6943 | 16.1364 | 14200 | 1.1927 | 0.6567 | 0.6581 | 0.6878 | 0.6567 |
| 2.3253 | 16.25 | 14300 | 1.1678 | 0.68 | 0.6712 | 0.6906 | 0.68 |
| 1.3754 | 16.3636 | 14400 | 1.0888 | 0.6667 | 0.6646 | 0.6757 | 0.6667 |
| 0.9967 | 16.4773 | 14500 | 1.1972 | 0.5967 | 0.5899 | 0.6351 | 0.5967 |
| 1.1325 | 16.5909 | 14600 | 1.1720 | 0.66 | 0.6581 | 0.6725 | 0.66 |
| 1.2693 | 16.7045 | 14700 | 1.0738 | 0.67 | 0.6609 | 0.6775 | 0.67 |
| 2.177 | 16.8182 | 14800 | 1.2318 | 0.6267 | 0.6097 | 0.6219 | 0.6267 |
| 1.9631 | 16.9318 | 14900 | 1.0616 | 0.7033 | 0.6951 | 0.7380 | 0.7033 |
| 0.4307 | 17.0455 | 15000 | 1.1593 | 0.6767 | 0.6751 | 0.7137 | 0.6767 |
| 1.5144 | 17.1591 | 15100 | 1.1575 | 0.6867 | 0.6816 | 0.7203 | 0.6867 |
| 1.0533 | 17.2727 | 15200 | 0.9916 | 0.7233 | 0.7147 | 0.7443 | 0.7233 |
| 2.0933 | 17.3864 | 15300 | 1.1640 | 0.69 | 0.6817 | 0.7049 | 0.69 |
| 0.7733 | 17.5 | 15400 | 1.0627 | 0.7033 | 0.6961 | 0.7222 | 0.7033 |
| 1.942 | 17.6136 | 15500 | 1.0537 | 0.72 | 0.7157 | 0.7453 | 0.72 |
| 0.1422 | 17.7273 | 15600 | 1.0434 | 0.6867 | 0.6773 | 0.6873 | 0.6867 |
| 1.6104 | 17.8409 | 15700 | 0.9983 | 0.7167 | 0.7183 | 0.7457 | 0.7167 |
| 2.0064 | 17.9545 | 15800 | 1.0145 | 0.71 | 0.7031 | 0.7177 | 0.71 |
| 1.5486 | 18.0682 | 15900 | 1.1365 | 0.7033 | 0.6973 | 0.7088 | 0.7033 |
| 2.7864 | 18.1818 | 16000 | 1.0285 | 0.73 | 0.7263 | 0.7446 | 0.73 |
| 1.213 | 18.2955 | 16100 | 1.0124 | 0.72 | 0.7119 | 0.7229 | 0.72 |
| 1.5922 | 18.4091 | 16200 | 1.1702 | 0.6633 | 0.6473 | 0.6508 | 0.6633 |
| 0.575 | 18.5227 | 16300 | 0.9927 | 0.74 | 0.7354 | 0.7483 | 0.74 |
| 1.2907 | 18.6364 | 16400 | 0.9610 | 0.7467 | 0.7471 | 0.7677 | 0.7467 |
| 1.2497 | 18.75 | 16500 | 0.9740 | 0.7267 | 0.7299 | 0.7562 | 0.7267 |
| 0.9504 | 18.8636 | 16600 | 0.8175 | 0.7667 | 0.7678 | 0.7851 | 0.7667 |
| 2.0678 | 18.9773 | 16700 | 1.0185 | 0.7267 | 0.7306 | 0.7610 | 0.7267 |
| 1.5767 | 19.0909 | 16800 | 1.3069 | 0.7 | 0.6941 | 0.7026 | 0.7 |
| 1.0399 | 19.2045 | 16900 | 1.0427 | 0.7133 | 0.7075 | 0.7134 | 0.7133 |
| 1.1016 | 19.3182 | 17000 | 0.7514 | 0.78 | 0.7781 | 0.7834 | 0.78 |
| 1.3244 | 19.4318 | 17100 | 0.8616 | 0.7833 | 0.7824 | 0.7945 | 0.7833 |
| 1.0574 | 19.5455 | 17200 | 1.0000 | 0.7533 | 0.7541 | 0.7798 | 0.7533 |
| 1.3972 | 19.6591 | 17300 | 1.0219 | 0.74 | 0.7370 | 0.7604 | 0.74 |
| 0.549 | 19.7727 | 17400 | 0.8382 | 0.7733 | 0.7701 | 0.7929 | 0.7733 |
| 1.2579 | 19.8864 | 17500 | 0.9918 | 0.7367 | 0.7361 | 0.7571 | 0.7367 |
| 2.4236 | 20.0 | 17600 | 1.2059 | 0.6733 | 0.6612 | 0.7173 | 0.6733 |
| 0.7265 | 20.1136 | 17700 | 0.8236 | 0.78 | 0.7758 | 0.7820 | 0.78 |
| 1.948 | 20.2273 | 17800 | 0.9320 | 0.77 | 0.7682 | 0.7870 | 0.77 |
| 2.0043 | 20.3409 | 17900 | 0.7740 | 0.7867 | 0.7849 | 0.7985 | 0.7867 |
| 1.0796 | 20.4545 | 18000 | 0.8225 | 0.78 | 0.7785 | 0.8069 | 0.78 |
| 1.4089 | 20.5682 | 18100 | 0.8105 | 0.78 | 0.7758 | 0.7872 | 0.78 |
| 0.3034 | 20.6818 | 18200 | 0.7958 | 0.78 | 0.7741 | 0.7793 | 0.78 |
| 0.8838 | 20.7955 | 18300 | 0.8089 | 0.8133 | 0.8099 | 0.8220 | 0.8133 |
| 1.4591 | 20.9091 | 18400 | 0.7593 | 0.8033 | 0.7995 | 0.8114 | 0.8033 |
| 1.1545 | 21.0227 | 18500 | 1.1446 | 0.7233 | 0.7253 | 0.7499 | 0.7233 |
| 0.732 | 21.1364 | 18600 | 0.7913 | 0.7967 | 0.7946 | 0.8040 | 0.7967 |
| 0.3987 | 21.25 | 18700 | 0.8410 | 0.7867 | 0.7819 | 0.7974 | 0.7867 |
| 1.0486 | 21.3636 | 18800 | 0.8319 | 0.7833 | 0.7776 | 0.7997 | 0.7833 |
| 0.5063 | 21.4773 | 18900 | 0.8443 | 0.8067 | 0.8020 | 0.8178 | 0.8067 |
| 0.7905 | 21.5909 | 19000 | 0.9596 | 0.77 | 0.7612 | 0.7790 | 0.77 |
| 0.7717 | 21.7045 | 19100 | 0.9244 | 0.8033 | 0.8005 | 0.8166 | 0.8033 |
| 0.5365 | 21.8182 | 19200 | 0.9661 | 0.7633 | 0.7597 | 0.7778 | 0.7633 |
| 0.8991 | 21.9318 | 19300 | 0.7786 | 0.7867 | 0.7820 | 0.7958 | 0.7867 |
| 0.6671 | 22.0455 | 19400 | 0.6612 | 0.8233 | 0.8200 | 0.8351 | 0.8233 |
| 0.4713 | 22.1591 | 19500 | 0.7054 | 0.8267 | 0.8217 | 0.8353 | 0.8267 |
| 0.7041 | 22.2727 | 19600 | 0.7471 | 0.8 | 0.7952 | 0.8153 | 0.8 |
| 0.197 | 22.3864 | 19700 | 0.6874 | 0.8267 | 0.8241 | 0.8396 | 0.8267 |
| 0.353 | 22.5 | 19800 | 0.7304 | 0.82 | 0.8161 | 0.8272 | 0.82 |
| 1.184 | 22.6136 | 19900 | 0.7151 | 0.82 | 0.8150 | 0.8290 | 0.82 |
| 1.3648 | 22.7273 | 20000 | 0.7410 | 0.8167 | 0.8132 | 0.8209 | 0.8167 |
| 0.9501 | 22.8409 | 20100 | 0.6499 | 0.8167 | 0.8159 | 0.8266 | 0.8167 |
| 0.262 | 22.9545 | 20200 | 0.6300 | 0.8367 | 0.8317 | 0.8459 | 0.8367 |
| 0.2318 | 23.0682 | 20300 | 0.8589 | 0.79 | 0.7858 | 0.7934 | 0.79 |
| 0.8504 | 23.1818 | 20400 | 0.6944 | 0.83 | 0.8272 | 0.8364 | 0.83 |
| 0.3976 | 23.2955 | 20500 | 0.6853 | 0.83 | 0.8253 | 0.8380 | 0.83 |
| 0.0863 | 23.4091 | 20600 | 0.7087 | 0.83 | 0.8268 | 0.8424 | 0.83 |
| 0.1308 | 23.5227 | 20700 | 0.6115 | 0.84 | 0.8377 | 0.8454 | 0.84 |
| 0.5146 | 23.6364 | 20800 | 0.6218 | 0.84 | 0.8361 | 0.8416 | 0.84 |
| 0.8203 | 23.75 | 20900 | 0.5224 | 0.8567 | 0.8552 | 0.8588 | 0.8567 |
| 1.1872 | 23.8636 | 21000 | 0.5631 | 0.85 | 0.8470 | 0.8569 | 0.85 |
| 1.0908 | 23.9773 | 21100 | 0.5524 | 0.85 | 0.8462 | 0.8585 | 0.85 |
| 0.0022 | 24.0909 | 21200 | 0.5363 | 0.86 | 0.8578 | 0.8673 | 0.86 |
| 1.6888 | 24.2045 | 21300 | 0.6602 | 0.8367 | 0.8304 | 0.8421 | 0.8367 |
| 0.2682 | 24.3182 | 21400 | 0.5271 | 0.86 | 0.8585 | 0.8680 | 0.86 |
| 0.2807 | 24.4318 | 21500 | 0.4464 | 0.8733 | 0.8716 | 0.8787 | 0.8733 |
| 0.2431 | 24.5455 | 21600 | 0.5111 | 0.8567 | 0.8535 | 0.8594 | 0.8567 |
| 2.7275 | 24.6591 | 21700 | 0.4631 | 0.8767 | 0.8750 | 0.8825 | 0.8767 |
| 0.706 | 24.7727 | 21800 | 0.5497 | 0.8533 | 0.8530 | 0.8580 | 0.8533 |
| 0.3191 | 24.8864 | 21900 | 0.6010 | 0.85 | 0.8499 | 0.8602 | 0.85 |
| 1.0717 | 25.0 | 22000 | 0.4868 | 0.8767 | 0.8759 | 0.8860 | 0.8767 |
| 1.0237 | 25.1136 | 22100 | 0.4212 | 0.87 | 0.8700 | 0.8766 | 0.87 |
| 0.3669 | 25.2273 | 22200 | 0.4204 | 0.88 | 0.8780 | 0.8851 | 0.88 |
| 0.2923 | 25.3409 | 22300 | 0.5041 | 0.8733 | 0.8726 | 0.8834 | 0.8733 |
| 0.468 | 25.4545 | 22400 | 0.5242 | 0.8633 | 0.8638 | 0.8707 | 0.8633 |
| 0.0333 | 25.5682 | 22500 | 0.3914 | 0.8967 | 0.8961 | 0.9014 | 0.8967 |
| 0.7682 | 25.6818 | 22600 | 0.4016 | 0.8867 | 0.8858 | 0.8912 | 0.8867 |
| 0.1863 | 25.7955 | 22700 | 0.4485 | 0.8833 | 0.8826 | 0.8927 | 0.8833 |
| 1.1239 | 25.9091 | 22800 | 0.4287 | 0.89 | 0.8896 | 0.8994 | 0.89 |
| 0.6257 | 26.0227 | 22900 | 0.3714 | 0.8933 | 0.8924 | 0.9008 | 0.8933 |
| 0.2438 | 26.1364 | 23000 | 0.4003 | 0.8933 | 0.8927 | 0.9002 | 0.8933 |
| 0.0027 | 26.25 | 23100 | 0.3834 | 0.9 | 0.9002 | 0.9059 | 0.9 |
| 0.5624 | 26.3636 | 23200 | 0.3970 | 0.9 | 0.9004 | 0.9089 | 0.9 |
| 0.0127 | 26.4773 | 23300 | 0.3772 | 0.8967 | 0.8955 | 0.8996 | 0.8967 |
| 0.7248 | 26.5909 | 23400 | 0.3919 | 0.8933 | 0.8915 | 0.8977 | 0.8933 |
| 0.7947 | 26.7045 | 23500 | 0.3874 | 0.9033 | 0.9019 | 0.9053 | 0.9033 |
| 0.7292 | 26.8182 | 23600 | 0.3422 | 0.91 | 0.9091 | 0.9139 | 0.91 |
| 0.047 | 26.9318 | 23700 | 0.2923 | 0.9167 | 0.9166 | 0.9219 | 0.9167 |
| 0.5063 | 27.0455 | 23800 | 0.3153 | 0.9133 | 0.9130 | 0.9188 | 0.9133 |
| 0.4127 | 27.1591 | 23900 | 0.3273 | 0.9133 | 0.9129 | 0.9175 | 0.9133 |
| 0.0093 | 27.2727 | 24000 | 0.3658 | 0.9133 | 0.9127 | 0.9179 | 0.9133 |
| 0.4859 | 27.3864 | 24100 | 0.3952 | 0.9033 | 0.9029 | 0.9061 | 0.9033 |
| 0.0075 | 27.5 | 24200 | 0.3231 | 0.9133 | 0.9132 | 0.9181 | 0.9133 |
| 0.0021 | 27.6136 | 24300 | 0.3003 | 0.91 | 0.9097 | 0.9127 | 0.91 |
| 1.1539 | 27.7273 | 24400 | 0.3282 | 0.9167 | 0.9164 | 0.9187 | 0.9167 |
| 0.4621 | 27.8409 | 24500 | 0.2971 | 0.9167 | 0.9168 | 0.9222 | 0.9167 |
| 0.6388 | 27.9545 | 24600 | 0.2677 | 0.9233 | 0.9241 | 0.9292 | 0.9233 |
| 0.4187 | 28.0682 | 24700 | 0.2462 | 0.92 | 0.9206 | 0.9255 | 0.92 |
| 0.3007 | 28.1818 | 24800 | 0.2599 | 0.9233 | 0.9241 | 0.9302 | 0.9233 |
| 0.0099 | 28.2955 | 24900 | 0.2981 | 0.9133 | 0.9125 | 0.9177 | 0.9133 |
| 1.1637 | 28.4091 | 25000 | 0.2819 | 0.9167 | 0.9167 | 0.9219 | 0.9167 |
| 0.0701 | 28.5227 | 25100 | 0.2788 | 0.92 | 0.9205 | 0.9267 | 0.92 |
| 0.6484 | 28.6364 | 25200 | 0.2541 | 0.9267 | 0.9268 | 0.9346 | 0.9267 |
| 0.0016 | 28.75 | 25300 | 0.2422 | 0.9333 | 0.9335 | 0.9389 | 0.9333 |
| 1.2359 | 28.8636 | 25400 | 0.2319 | 0.93 | 0.9300 | 0.9355 | 0.93 |
| 0.3842 | 28.9773 | 25500 | 0.2229 | 0.9333 | 0.9337 | 0.9384 | 0.9333 |
| 0.751 | 29.0909 | 25600 | 0.2323 | 0.9267 | 0.9274 | 0.9338 | 0.9267 |
| 0.4643 | 29.2045 | 25700 | 0.2354 | 0.9333 | 0.9338 | 0.9400 | 0.9333 |
| 0.8059 | 29.3182 | 25800 | 0.2268 | 0.9333 | 0.9336 | 0.9391 | 0.9333 |
| 0.2155 | 29.4318 | 25900 | 0.2322 | 0.9333 | 0.9336 | 0.9391 | 0.9333 |
| 0.628 | 29.5455 | 26000 | 0.2306 | 0.9333 | 0.9336 | 0.9391 | 0.9333 |
| 0.1146 | 29.6591 | 26100 | 0.2243 | 0.9333 | 0.9334 | 0.9384 | 0.9333 |
| 0.2068 | 29.7727 | 26200 | 0.2155 | 0.9333 | 0.9334 | 0.9384 | 0.9333 |
| 0.0016 | 29.8864 | 26300 | 0.2176 | 0.9333 | 0.9334 | 0.9384 | 0.9333 |
| 0.002 | 30.0 | 26400 | 0.2171 | 0.9333 | 0.9334 | 0.9384 | 0.9333 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"effusion",
"pneumonia",
"pneumothorax",
"mass",
"nodule",
"cardiomegaly",
"infiltrate",
"atelectasis"
] |
iceberg0142/natix-test-2
|
# Model Card for Model ID
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
[
"none",
"roadwork"
] |
BeckerAnas/cool-darkness-225
|
<!-- 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. -->
# cool-darkness-225
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0830
- Accuracy: 0.9783
- Precision: 0.9787
- Recall: 0.9783
- F1: 0.9783
- Roc Auc: 0.9995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.7349 | 1.0 | 161 | 0.7668 | 0.8664 | 0.8687 | 0.8664 | 0.8658 | 0.9880 |
| 0.6429 | 2.0 | 322 | 0.2021 | 0.9498 | 0.9506 | 0.9498 | 0.9498 | 0.9979 |
| 0.4363 | 3.0 | 483 | 0.1325 | 0.9601 | 0.9606 | 0.9601 | 0.9600 | 0.9991 |
| 0.3774 | 4.0 | 644 | 0.1117 | 0.966 | 0.9667 | 0.966 | 0.9660 | 0.9992 |
| 0.3413 | 5.0 | 805 | 0.0979 | 0.9694 | 0.9697 | 0.9694 | 0.9693 | 0.9994 |
| 0.3051 | 6.0 | 966 | 0.0824 | 0.9748 | 0.9749 | 0.9748 | 0.9748 | 0.9995 |
| 0.2808 | 7.0 | 1127 | 0.0796 | 0.9742 | 0.9744 | 0.9742 | 0.9742 | 0.9995 |
| 0.2622 | 8.0 | 1288 | 0.0753 | 0.9764 | 0.9765 | 0.9764 | 0.9765 | 0.9995 |
| 0.2512 | 9.0 | 1449 | 0.0769 | 0.9748 | 0.9748 | 0.9748 | 0.9747 | 0.9996 |
| 0.2368 | 10.0 | 1610 | 0.0816 | 0.9777 | 0.9780 | 0.9777 | 0.9777 | 0.9995 |
| 0.2303 | 11.0 | 1771 | 0.0686 | 0.98 | 0.9800 | 0.98 | 0.9800 | 0.9996 |
| 0.2141 | 12.0 | 1932 | 0.0684 | 0.9791 | 0.9792 | 0.9791 | 0.9791 | 0.9996 |
| 0.2086 | 13.0 | 2093 | 0.0766 | 0.9782 | 0.9787 | 0.9782 | 0.9783 | 0.9996 |
| 0.2012 | 14.0 | 2254 | 0.0830 | 0.9783 | 0.9787 | 0.9783 | 0.9783 | 0.9995 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
James4u/5EkPqbRFA6jQtVtWX9nbQ1DbQf17v4BuQsngpcsFctMZuhdw
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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iceberg0142/natix-test-3
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James4u/5EEfyB6LS2XUKu6Gf7tgR6EiyAZaHww3yPqb6fu3vsaBLFAQ
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plumpyfield/natix_v2-016
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James4u/5DbyJT3cbPr9ndD4y2XhCs91NwnziW9vAFury9cWBzmB9Hno
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iceberg0142/natix-test-4
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plumpyfield/natix_v2-017
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love-mimi/sn72-model-47
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love-mimi/sn72-model-53
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love-mimi/sn72-model-63
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love-mimi/sn72-model-64
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love-mimi/sn72-model-66
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love-mimi/sn72-model-70
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love-mimi/sn72-model-73
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love-mimi/sn72-model-78
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love-mimi/sn72-model-80
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love-mimi/sn72-model-82
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love-mimi/sn72-model-98
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love-mimi/sn72-model-100
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love-mimi/sn72-model-142
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love-mimi/sn72-model-147
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iceberg0142/natix-test-5
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iceberg0142/natix-test-6
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iceberg0142/natix-test-7
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plumpyfield/natix__hiro-hero_002
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plumpyfield/natix__hiro-hero_000
|
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plumpyfield/natix__hiro-hero_004
|
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plumpyfield/natix__hiro-hero_003
|
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plumpyfield/natix__hiro-hero_001
|
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James4u/5DU7XrkreiHSYES1R6Gyq24E44Uk5xA6ypLW5crhhc2aAozC
|
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plumpyfield/natix_v2-018
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plumpyfield/natix_v2-019
|
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James4u/5ETrSPafgv8hk1gQ37PgfiurhFtSNi9nNSnePJ3inb81qzKt
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obiwan001/roadwork271
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obiwan001/roadwork289
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"none",
"roadwork"
] |
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