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prove_melanomaprova_melanoma

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vit
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7133333333333334
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [UnipaPolitoUnimore/vit-large-patch32-384-melanoma](https://huggingface.co/UnipaPolitoUnimore/vit-large-patch32-384-melanoma) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7288
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- - Accuracy: 0.7133
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  ## Model description
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@@ -66,46 +66,46 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.9733 | 0.99 | 31 | 1.0798 | 0.52 |
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- | 0.7002 | 1.98 | 62 | 1.1144 | 0.52 |
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- | 0.7113 | 2.98 | 93 | 1.0708 | 0.52 |
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- | 0.8646 | 4.0 | 125 | 1.0199 | 0.52 |
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- | 0.787 | 4.99 | 156 | 0.9749 | 0.52 |
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- | 0.6584 | 5.98 | 187 | 0.9452 | 0.52 |
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- | 0.7525 | 6.98 | 218 | 0.9146 | 0.54 |
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- | 0.645 | 8.0 | 250 | 0.8944 | 0.56 |
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- | 0.6979 | 8.99 | 281 | 0.8396 | 0.5933 |
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- | 0.6977 | 9.98 | 312 | 0.8299 | 0.6133 |
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- | 0.7564 | 10.98 | 343 | 0.8143 | 0.66 |
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- | 0.8716 | 12.0 | 375 | 0.8066 | 0.6533 |
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- | 0.7524 | 12.99 | 406 | 0.7737 | 0.6733 |
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- | 0.769 | 13.98 | 437 | 0.7854 | 0.6733 |
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- | 0.5504 | 14.98 | 468 | 0.7668 | 0.6933 |
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- | 0.5284 | 16.0 | 500 | 0.7646 | 0.7 |
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- | 0.6882 | 16.99 | 531 | 0.7750 | 0.6733 |
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- | 0.6738 | 17.98 | 562 | 0.7630 | 0.6867 |
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- | 0.6819 | 18.98 | 593 | 0.7669 | 0.7133 |
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- | 0.6084 | 20.0 | 625 | 0.7345 | 0.7467 |
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- | 0.5081 | 20.99 | 656 | 0.7609 | 0.6933 |
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- | 0.7572 | 21.98 | 687 | 0.7346 | 0.7267 |
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- | 0.6763 | 22.98 | 718 | 0.7294 | 0.74 |
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- | 0.5902 | 24.0 | 750 | 0.7262 | 0.7467 |
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- | 0.7128 | 24.99 | 781 | 0.7489 | 0.7133 |
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- | 0.6443 | 25.98 | 812 | 0.7427 | 0.7067 |
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- | 0.5636 | 26.98 | 843 | 0.7349 | 0.7267 |
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- | 0.5304 | 28.0 | 875 | 0.7349 | 0.7133 |
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- | 0.5882 | 28.99 | 906 | 0.7356 | 0.7067 |
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- | 0.5236 | 29.98 | 937 | 0.7322 | 0.74 |
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- | 0.5905 | 30.98 | 968 | 0.7348 | 0.7067 |
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- | 0.4357 | 32.0 | 1000 | 0.7345 | 0.7133 |
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- | 0.5676 | 32.99 | 1031 | 0.7357 | 0.7133 |
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- | 0.5109 | 33.98 | 1062 | 0.7290 | 0.7133 |
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- | 0.5552 | 34.98 | 1093 | 0.7340 | 0.72 |
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- | 0.5794 | 36.0 | 1125 | 0.7287 | 0.7133 |
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- | 0.5967 | 36.99 | 1156 | 0.7311 | 0.7267 |
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- | 0.5398 | 37.98 | 1187 | 0.7309 | 0.7133 |
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- | 0.4551 | 38.98 | 1218 | 0.7278 | 0.72 |
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- | 0.6199 | 39.68 | 1240 | 0.7288 | 0.7133 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8466666666666667
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [UnipaPolitoUnimore/vit-large-patch32-384-melanoma](https://huggingface.co/UnipaPolitoUnimore/vit-large-patch32-384-melanoma) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5191
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+ - Accuracy: 0.8467
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.8964 | 0.99 | 31 | 1.0906 | 0.52 |
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+ | 0.6588 | 1.98 | 62 | 1.0817 | 0.52 |
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+ | 0.6774 | 2.98 | 93 | 0.9474 | 0.52 |
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+ | 0.7785 | 4.0 | 125 | 0.8185 | 0.6267 |
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+ | 0.6732 | 4.99 | 156 | 0.7531 | 0.7267 |
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+ | 0.5438 | 5.98 | 187 | 0.6972 | 0.7333 |
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+ | 0.5497 | 6.98 | 218 | 0.6714 | 0.7533 |
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+ | 0.4161 | 8.0 | 250 | 0.6440 | 0.7667 |
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+ | 0.4968 | 8.99 | 281 | 0.6438 | 0.78 |
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+ | 0.5861 | 9.98 | 312 | 0.6266 | 0.7933 |
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+ | 0.5182 | 10.98 | 343 | 0.6158 | 0.7867 |
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+ | 0.6797 | 12.0 | 375 | 0.6237 | 0.8133 |
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+ | 0.622 | 12.99 | 406 | 0.5858 | 0.8333 |
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+ | 0.6419 | 13.98 | 437 | 0.5735 | 0.8267 |
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+ | 0.3727 | 14.98 | 468 | 0.5641 | 0.8133 |
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+ | 0.3822 | 16.0 | 500 | 0.5520 | 0.8267 |
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+ | 0.4766 | 16.99 | 531 | 0.5642 | 0.8267 |
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+ | 0.4791 | 17.98 | 562 | 0.5309 | 0.8267 |
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+ | 0.3918 | 18.98 | 593 | 0.5749 | 0.8267 |
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+ | 0.3847 | 20.0 | 625 | 0.5317 | 0.84 |
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+ | 0.3722 | 20.99 | 656 | 0.5719 | 0.8267 |
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+ | 0.5402 | 21.98 | 687 | 0.5316 | 0.84 |
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+ | 0.4358 | 22.98 | 718 | 0.5292 | 0.8333 |
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+ | 0.2957 | 24.0 | 750 | 0.5172 | 0.8467 |
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+ | 0.4801 | 24.99 | 781 | 0.5376 | 0.84 |
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+ | 0.3656 | 25.98 | 812 | 0.5118 | 0.8333 |
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+ | 0.3956 | 26.98 | 843 | 0.5081 | 0.8533 |
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+ | 0.3343 | 28.0 | 875 | 0.5198 | 0.8533 |
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+ | 0.3839 | 28.99 | 906 | 0.5269 | 0.8467 |
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+ | 0.4286 | 29.98 | 937 | 0.5163 | 0.8467 |
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+ | 0.2736 | 30.98 | 968 | 0.5359 | 0.8333 |
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+ | 0.3465 | 32.0 | 1000 | 0.5277 | 0.84 |
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+ | 0.4244 | 32.99 | 1031 | 0.5385 | 0.8333 |
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+ | 0.308 | 33.98 | 1062 | 0.5141 | 0.8533 |
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+ | 0.3494 | 34.98 | 1093 | 0.5129 | 0.8533 |
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+ | 0.3851 | 36.0 | 1125 | 0.5199 | 0.84 |
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+ | 0.3949 | 36.99 | 1156 | 0.5250 | 0.84 |
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+ | 0.3235 | 37.98 | 1187 | 0.5142 | 0.8533 |
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+ | 0.3076 | 38.98 | 1218 | 0.5166 | 0.8533 |
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+ | 0.3679 | 39.68 | 1240 | 0.5191 | 0.8467 |
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  ### Framework versions