| model_id
				 stringlengths 7 105 | model_card
				 stringlengths 1 130k | model_labels
				 listlengths 2 80k | 
|---|---|---|
| 
	James4u/5FTdqwrPyVGw59V9YfKP1P1KJxHJsQo6Yp75wFVbfxdkedXU | 
	
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	[
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| 
	James4u/5Gbbw6WCUiQrscuHjLyjaayaZCPaDpBhPBxybgtLDmDCjFBV | 
	
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	[
  "none",
  "roadwork"
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| 
	James4u/5FeATnLym4BQ52qN65oC6Jkq9VmbtCKWQMjur388n4TJaswp | 
	
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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 | 
	
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	[
  "none",
  "roadwork"
] | 
| 
	Runnies23/swin-tiny-patch4-finetuned-lung-cancer-ct-scans_liver_hsitopathlogy | 
	
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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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	James4u/5HNZVL9PRTr6vrVD1xiQUJBxMizHX2C8hvYiqBfiCTyr5q4Q | 
	
# Model Card for Model ID
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	[
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| 
	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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[More Information Needed] | 
	[
  "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 | 
	
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	plumpyfield/natix_v2-016 | 
	
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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_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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