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vit-base-skin

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7902
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- - Accuracy: 0.8446
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- - F1: 0.8443
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- - Precision: 0.8449
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- - Recall: 0.8446
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  ## Model description
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@@ -55,43 +55,43 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.8192 | 0.16 | 100 | 0.7593 | 0.7098 | 0.6664 | 0.7150 | 0.7098 |
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- | 0.7637 | 0.32 | 200 | 0.6606 | 0.7513 | 0.7008 | 0.7697 | 0.7513 |
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- | 0.6177 | 0.48 | 300 | 0.5816 | 0.7927 | 0.7706 | 0.7834 | 0.7927 |
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- | 0.837 | 0.64 | 400 | 0.5884 | 0.7927 | 0.7831 | 0.7980 | 0.7927 |
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- | 0.6409 | 0.8 | 500 | 0.5473 | 0.8290 | 0.8270 | 0.8419 | 0.8290 |
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- | 0.5896 | 0.96 | 600 | 0.5079 | 0.8083 | 0.7904 | 0.8440 | 0.8083 |
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- | 0.4101 | 1.12 | 700 | 0.4893 | 0.8290 | 0.8219 | 0.8343 | 0.8290 |
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- | 0.6753 | 1.28 | 800 | 0.6570 | 0.7668 | 0.7800 | 0.8298 | 0.7668 |
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- | 0.3907 | 1.44 | 900 | 0.4257 | 0.8238 | 0.8152 | 0.8117 | 0.8238 |
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- | 0.447 | 1.6 | 1000 | 0.5717 | 0.8031 | 0.8030 | 0.8447 | 0.8031 |
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- | 0.4131 | 1.76 | 1100 | 0.4189 | 0.8342 | 0.8271 | 0.8304 | 0.8342 |
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- | 0.3913 | 1.92 | 1200 | 0.3728 | 0.8860 | 0.8877 | 0.8995 | 0.8860 |
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- | 0.2686 | 2.08 | 1300 | 0.5161 | 0.7979 | 0.7995 | 0.8199 | 0.7979 |
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- | 0.2466 | 2.24 | 1400 | 0.4671 | 0.8601 | 0.8570 | 0.8683 | 0.8601 |
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- | 0.2456 | 2.4 | 1500 | 0.4479 | 0.8446 | 0.8371 | 0.8372 | 0.8446 |
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- | 0.2218 | 2.56 | 1600 | 0.5276 | 0.8342 | 0.8360 | 0.8483 | 0.8342 |
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- | 0.2019 | 2.72 | 1700 | 0.4866 | 0.8290 | 0.8289 | 0.8338 | 0.8290 |
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- | 0.1856 | 2.88 | 1800 | 0.4727 | 0.8342 | 0.8371 | 0.8426 | 0.8342 |
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- | 0.1614 | 3.04 | 1900 | 0.5576 | 0.8135 | 0.8126 | 0.8164 | 0.8135 |
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- | 0.0985 | 3.19 | 2000 | 0.5765 | 0.8394 | 0.8350 | 0.8413 | 0.8394 |
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- | 0.0826 | 3.35 | 2100 | 0.6482 | 0.8238 | 0.8062 | 0.7906 | 0.8238 |
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- | 0.1288 | 3.51 | 2200 | 0.6919 | 0.8238 | 0.8247 | 0.8415 | 0.8238 |
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- | 0.0808 | 3.67 | 2300 | 0.7174 | 0.8135 | 0.8141 | 0.8175 | 0.8135 |
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- | 0.0764 | 3.83 | 2400 | 0.6201 | 0.8446 | 0.8445 | 0.8454 | 0.8446 |
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- | 0.1346 | 3.99 | 2500 | 0.6639 | 0.8187 | 0.8165 | 0.8177 | 0.8187 |
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- | 0.0246 | 4.15 | 2600 | 0.7757 | 0.8135 | 0.8153 | 0.8177 | 0.8135 |
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- | 0.0059 | 4.31 | 2700 | 0.7933 | 0.8238 | 0.8195 | 0.8170 | 0.8238 |
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- | 0.0187 | 4.47 | 2800 | 0.8311 | 0.8238 | 0.8192 | 0.8153 | 0.8238 |
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- | 0.023 | 4.63 | 2900 | 0.8048 | 0.8238 | 0.8216 | 0.8217 | 0.8238 |
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- | 0.0186 | 4.79 | 3000 | 0.8510 | 0.8342 | 0.8301 | 0.8295 | 0.8342 |
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- | 0.0562 | 4.95 | 3100 | 0.7534 | 0.8446 | 0.8434 | 0.8429 | 0.8446 |
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- | 0.0046 | 5.11 | 3200 | 0.7817 | 0.8497 | 0.8480 | 0.8509 | 0.8497 |
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- | 0.0032 | 5.27 | 3300 | 0.7576 | 0.8446 | 0.8429 | 0.8447 | 0.8446 |
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- | 0.0031 | 5.43 | 3400 | 0.7692 | 0.8394 | 0.8374 | 0.8373 | 0.8394 |
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- | 0.0399 | 5.59 | 3500 | 0.7673 | 0.8394 | 0.8395 | 0.8415 | 0.8394 |
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- | 0.0052 | 5.75 | 3600 | 0.7809 | 0.8394 | 0.8388 | 0.8399 | 0.8394 |
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- | 0.0203 | 5.91 | 3700 | 0.7902 | 0.8446 | 0.8443 | 0.8449 | 0.8446 |
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  ### Framework versions
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7037
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+ - Accuracy: 0.8549
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+ - F1: 0.8534
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+ - Precision: 0.8536
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+ - Recall: 0.8549
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.7904 | 0.16 | 100 | 0.7231 | 0.7772 | 0.7372 | 0.7397 | 0.7772 |
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+ | 0.7746 | 0.32 | 200 | 0.6372 | 0.7668 | 0.7147 | 0.7458 | 0.7668 |
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+ | 0.611 | 0.48 | 300 | 0.6455 | 0.7409 | 0.6930 | 0.7665 | 0.7409 |
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+ | 0.8406 | 0.64 | 400 | 0.6233 | 0.8031 | 0.8098 | 0.8433 | 0.8031 |
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+ | 0.587 | 0.8 | 500 | 0.6028 | 0.7513 | 0.7163 | 0.7659 | 0.7513 |
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+ | 0.5532 | 0.96 | 600 | 0.4689 | 0.8290 | 0.8090 | 0.8377 | 0.8290 |
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+ | 0.4326 | 1.12 | 700 | 0.4968 | 0.8290 | 0.8200 | 0.8368 | 0.8290 |
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+ | 0.4713 | 1.28 | 800 | 0.4973 | 0.8187 | 0.8222 | 0.8436 | 0.8187 |
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+ | 0.4333 | 1.44 | 900 | 0.5500 | 0.7720 | 0.7615 | 0.7705 | 0.7720 |
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+ | 0.441 | 1.6 | 1000 | 0.5518 | 0.8238 | 0.8398 | 0.8774 | 0.8238 |
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+ | 0.4172 | 1.76 | 1100 | 0.5608 | 0.8031 | 0.7802 | 0.8260 | 0.8031 |
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+ | 0.4062 | 1.92 | 1200 | 0.4730 | 0.8290 | 0.8312 | 0.8704 | 0.8290 |
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+ | 0.271 | 2.08 | 1300 | 0.4893 | 0.8031 | 0.8018 | 0.8164 | 0.8031 |
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+ | 0.2294 | 2.24 | 1400 | 0.4859 | 0.8342 | 0.8369 | 0.8442 | 0.8342 |
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+ | 0.2687 | 2.4 | 1500 | 0.4805 | 0.8394 | 0.8391 | 0.8424 | 0.8394 |
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+ | 0.2348 | 2.56 | 1600 | 0.4667 | 0.8497 | 0.8522 | 0.8567 | 0.8497 |
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+ | 0.2038 | 2.72 | 1700 | 0.5050 | 0.8135 | 0.8148 | 0.8222 | 0.8135 |
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+ | 0.2102 | 2.88 | 1800 | 0.4730 | 0.8497 | 0.8527 | 0.8695 | 0.8497 |
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+ | 0.0978 | 3.04 | 1900 | 0.4673 | 0.8446 | 0.8450 | 0.8508 | 0.8446 |
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+ | 0.104 | 3.19 | 2000 | 0.5348 | 0.8342 | 0.8274 | 0.8313 | 0.8342 |
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+ | 0.0562 | 3.35 | 2100 | 0.5748 | 0.8342 | 0.8264 | 0.8299 | 0.8342 |
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+ | 0.1443 | 3.51 | 2200 | 0.5903 | 0.8446 | 0.8432 | 0.8448 | 0.8446 |
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+ | 0.1245 | 3.67 | 2300 | 0.5773 | 0.8601 | 0.8627 | 0.8779 | 0.8601 |
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+ | 0.081 | 3.83 | 2400 | 0.6190 | 0.8394 | 0.8424 | 0.8487 | 0.8394 |
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+ | 0.1314 | 3.99 | 2500 | 0.6078 | 0.8549 | 0.8509 | 0.8506 | 0.8549 |
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+ | 0.0415 | 4.15 | 2600 | 0.7039 | 0.8290 | 0.8312 | 0.8358 | 0.8290 |
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+ | 0.0402 | 4.31 | 2700 | 0.7477 | 0.8238 | 0.8166 | 0.8179 | 0.8238 |
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+ | 0.0045 | 4.47 | 2800 | 0.7207 | 0.8497 | 0.8493 | 0.8539 | 0.8497 |
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+ | 0.0608 | 4.63 | 2900 | 0.7339 | 0.8342 | 0.8370 | 0.8469 | 0.8342 |
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+ | 0.0168 | 4.79 | 3000 | 0.7894 | 0.8290 | 0.8388 | 0.8539 | 0.8290 |
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+ | 0.0042 | 4.95 | 3100 | 0.7268 | 0.8601 | 0.8628 | 0.8681 | 0.8601 |
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+ | 0.0149 | 5.11 | 3200 | 0.7145 | 0.8601 | 0.8577 | 0.8600 | 0.8601 |
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+ | 0.0074 | 5.27 | 3300 | 0.7424 | 0.8342 | 0.8354 | 0.8380 | 0.8342 |
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+ | 0.0029 | 5.43 | 3400 | 0.7123 | 0.8653 | 0.8649 | 0.8686 | 0.8653 |
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+ | 0.0123 | 5.59 | 3500 | 0.7052 | 0.8653 | 0.8633 | 0.8632 | 0.8653 |
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+ | 0.0028 | 5.75 | 3600 | 0.7027 | 0.8601 | 0.8590 | 0.8601 | 0.8601 |
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+ | 0.0029 | 5.91 | 3700 | 0.7037 | 0.8549 | 0.8534 | 0.8536 | 0.8549 |
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  ### Framework versions