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sergiocannata
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prove_melanomaprova_melanoma

Image Classification
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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: 0.5933333333333334
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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: 0.8432
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- - Accuracy: 0.5933
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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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- | 1.0268 | 0.99 | 31 | 1.0905 | 0.5267 |
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- | 0.7815 | 1.98 | 62 | 1.0481 | 0.52 |
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- | 0.7063 | 2.98 | 93 | 1.0993 | 0.52 |
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- | 0.8725 | 4.0 | 125 | 1.0588 | 0.52 |
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- | 0.8119 | 4.99 | 156 | 1.0326 | 0.52 |
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- | 0.6807 | 5.98 | 187 | 1.0180 | 0.52 |
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- | 0.8068 | 6.98 | 218 | 1.0113 | 0.52 |
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- | 0.7082 | 8.0 | 250 | 0.9996 | 0.52 |
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- | 0.7786 | 8.99 | 281 | 0.9537 | 0.52 |
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- | 0.8226 | 9.98 | 312 | 0.9460 | 0.52 |
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- | 0.9777 | 10.98 | 343 | 0.9518 | 0.52 |
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- | 0.9744 | 12.0 | 375 | 0.9278 | 0.5067 |
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- | 0.8427 | 12.99 | 406 | 0.8966 | 0.5267 |
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- | 0.8014 | 13.98 | 437 | 0.8991 | 0.5267 |
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- | 0.6916 | 14.98 | 468 | 0.8934 | 0.54 |
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- | 0.6536 | 16.0 | 500 | 0.8861 | 0.5467 |
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- | 0.7935 | 16.99 | 531 | 0.8693 | 0.56 |
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- | 0.77 | 17.98 | 562 | 0.8670 | 0.5733 |
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- | 0.7804 | 18.98 | 593 | 0.8797 | 0.5733 |
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- | 0.728 | 20.0 | 625 | 0.8478 | 0.5867 |
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- | 0.6334 | 20.99 | 656 | 0.8555 | 0.58 |
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- | 0.8243 | 21.98 | 687 | 0.8581 | 0.58 |
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- | 0.7827 | 22.98 | 718 | 0.8560 | 0.58 |
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- | 0.8101 | 24.0 | 750 | 0.8605 | 0.58 |
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- | 0.7786 | 24.99 | 781 | 0.8613 | 0.5733 |
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- | 0.8387 | 25.98 | 812 | 0.8563 | 0.5933 |
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- | 0.642 | 26.98 | 843 | 0.8622 | 0.5867 |
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- | 0.6557 | 28.0 | 875 | 0.8552 | 0.58 |
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- | 0.6991 | 28.99 | 906 | 0.8535 | 0.5933 |
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- | 0.611 | 29.98 | 937 | 0.8499 | 0.5933 |
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- | 0.7419 | 30.98 | 968 | 0.8435 | 0.6 |
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- | 0.5573 | 32.0 | 1000 | 0.8415 | 0.5933 |
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- | 0.744 | 32.99 | 1031 | 0.8457 | 0.5867 |
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- | 0.684 | 33.98 | 1062 | 0.8424 | 0.5933 |
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- | 0.671 | 34.98 | 1093 | 0.8425 | 0.5933 |
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- | 0.7094 | 36.0 | 1125 | 0.8414 | 0.5933 |
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- | 0.7161 | 36.99 | 1156 | 0.8434 | 0.5867 |
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- | 0.6537 | 37.98 | 1187 | 0.8444 | 0.5933 |
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- | 0.6754 | 38.98 | 1218 | 0.8430 | 0.5933 |
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- | 0.7791 | 39.68 | 1240 | 0.8432 | 0.5933 |
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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: 0.7133333333333334
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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
 
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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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  | 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