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hkivancoral
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smids_3x_beit_base_sgd_001_fold1

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +54 -54
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.34056761268781305
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2142
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- - Accuracy: 0.3406
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -65,56 +65,56 @@ 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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- | 1.3018 | 1.0 | 226 | 1.3497 | 0.3088 |
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- | 1.233 | 2.0 | 452 | 1.3433 | 0.3105 |
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- | 1.2502 | 3.0 | 678 | 1.3371 | 0.3122 |
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- | 1.2877 | 4.0 | 904 | 1.3312 | 0.3088 |
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- | 1.2473 | 5.0 | 1130 | 1.3254 | 0.3072 |
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- | 1.2074 | 6.0 | 1356 | 1.3198 | 0.3088 |
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- | 1.2425 | 7.0 | 1582 | 1.3144 | 0.3088 |
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- | 1.2061 | 8.0 | 1808 | 1.3093 | 0.3072 |
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- | 1.1665 | 9.0 | 2034 | 1.3043 | 0.3105 |
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- | 1.2345 | 10.0 | 2260 | 1.2993 | 0.3139 |
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- | 1.169 | 11.0 | 2486 | 1.2948 | 0.3139 |
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- | 1.1772 | 12.0 | 2712 | 1.2903 | 0.3139 |
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- | 1.2448 | 13.0 | 2938 | 1.2861 | 0.3172 |
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- | 1.1958 | 14.0 | 3164 | 1.2819 | 0.3205 |
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- | 1.1284 | 15.0 | 3390 | 1.2779 | 0.3222 |
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- | 1.1355 | 16.0 | 3616 | 1.2740 | 0.3222 |
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- | 1.1754 | 17.0 | 3842 | 1.2702 | 0.3222 |
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- | 1.1535 | 18.0 | 4068 | 1.2665 | 0.3239 |
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- | 1.1762 | 19.0 | 4294 | 1.2631 | 0.3255 |
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- | 1.2186 | 20.0 | 4520 | 1.2598 | 0.3255 |
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- | 1.1196 | 21.0 | 4746 | 1.2566 | 0.3272 |
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- | 1.1702 | 22.0 | 4972 | 1.2535 | 0.3255 |
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- | 1.1389 | 23.0 | 5198 | 1.2506 | 0.3255 |
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- | 1.1587 | 24.0 | 5424 | 1.2478 | 0.3289 |
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- | 1.1561 | 25.0 | 5650 | 1.2450 | 0.3306 |
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- | 1.1709 | 26.0 | 5876 | 1.2425 | 0.3322 |
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- | 1.1654 | 27.0 | 6102 | 1.2400 | 0.3322 |
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- | 1.1373 | 28.0 | 6328 | 1.2376 | 0.3339 |
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- | 1.1306 | 29.0 | 6554 | 1.2354 | 0.3339 |
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- | 1.1454 | 30.0 | 6780 | 1.2333 | 0.3356 |
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- | 1.1141 | 31.0 | 7006 | 1.2313 | 0.3322 |
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- | 1.1582 | 32.0 | 7232 | 1.2295 | 0.3339 |
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- | 1.1347 | 33.0 | 7458 | 1.2277 | 0.3339 |
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- | 1.1265 | 34.0 | 7684 | 1.2261 | 0.3356 |
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- | 1.1205 | 35.0 | 7910 | 1.2245 | 0.3356 |
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- | 1.122 | 36.0 | 8136 | 1.2231 | 0.3356 |
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- | 1.1483 | 37.0 | 8362 | 1.2218 | 0.3372 |
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- | 1.1233 | 38.0 | 8588 | 1.2206 | 0.3389 |
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- | 1.1213 | 39.0 | 8814 | 1.2195 | 0.3389 |
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- | 1.0703 | 40.0 | 9040 | 1.2185 | 0.3389 |
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- | 1.1268 | 41.0 | 9266 | 1.2176 | 0.3389 |
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- | 1.082 | 42.0 | 9492 | 1.2169 | 0.3406 |
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- | 1.0999 | 43.0 | 9718 | 1.2162 | 0.3406 |
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- | 1.1354 | 44.0 | 9944 | 1.2156 | 0.3406 |
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- | 1.1066 | 45.0 | 10170 | 1.2151 | 0.3406 |
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- | 1.1669 | 46.0 | 10396 | 1.2148 | 0.3406 |
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- | 1.1433 | 47.0 | 10622 | 1.2145 | 0.3406 |
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- | 1.1581 | 48.0 | 10848 | 1.2143 | 0.3406 |
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- | 1.1184 | 49.0 | 11074 | 1.2142 | 0.3406 |
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- | 1.1092 | 50.0 | 11300 | 1.2142 | 0.3406 |
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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: 0.8681135225375626
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3433
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+ - Accuracy: 0.8681
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.8537 | 1.0 | 226 | 0.8519 | 0.6194 |
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+ | 0.627 | 2.0 | 452 | 0.6595 | 0.7162 |
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+ | 0.5599 | 3.0 | 678 | 0.5836 | 0.7563 |
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+ | 0.5294 | 4.0 | 904 | 0.5378 | 0.7830 |
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+ | 0.541 | 5.0 | 1130 | 0.5094 | 0.7930 |
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+ | 0.4706 | 6.0 | 1356 | 0.4828 | 0.8030 |
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+ | 0.4252 | 7.0 | 1582 | 0.4633 | 0.8264 |
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+ | 0.4132 | 8.0 | 1808 | 0.4456 | 0.8214 |
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+ | 0.3872 | 9.0 | 2034 | 0.4395 | 0.8197 |
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+ | 0.4235 | 10.0 | 2260 | 0.4220 | 0.8347 |
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+ | 0.363 | 11.0 | 2486 | 0.4107 | 0.8414 |
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+ | 0.3912 | 12.0 | 2712 | 0.4033 | 0.8381 |
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+ | 0.304 | 13.0 | 2938 | 0.4009 | 0.8381 |
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+ | 0.3002 | 14.0 | 3164 | 0.4007 | 0.8414 |
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+ | 0.3207 | 15.0 | 3390 | 0.3907 | 0.8431 |
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+ | 0.3295 | 16.0 | 3616 | 0.3840 | 0.8481 |
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+ | 0.2579 | 17.0 | 3842 | 0.3879 | 0.8447 |
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+ | 0.2728 | 18.0 | 4068 | 0.3774 | 0.8514 |
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+ | 0.2754 | 19.0 | 4294 | 0.3738 | 0.8514 |
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+ | 0.2647 | 20.0 | 4520 | 0.3691 | 0.8531 |
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+ | 0.2981 | 21.0 | 4746 | 0.3671 | 0.8548 |
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+ | 0.2768 | 22.0 | 4972 | 0.3598 | 0.8614 |
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+ | 0.3183 | 23.0 | 5198 | 0.3674 | 0.8548 |
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+ | 0.2945 | 24.0 | 5424 | 0.3555 | 0.8598 |
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+ | 0.2478 | 25.0 | 5650 | 0.3546 | 0.8648 |
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+ | 0.325 | 26.0 | 5876 | 0.3592 | 0.8564 |
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+ | 0.2291 | 27.0 | 6102 | 0.3605 | 0.8548 |
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+ | 0.2815 | 28.0 | 6328 | 0.3547 | 0.8564 |
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+ | 0.2072 | 29.0 | 6554 | 0.3519 | 0.8614 |
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+ | 0.3343 | 30.0 | 6780 | 0.3526 | 0.8631 |
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+ | 0.2535 | 31.0 | 7006 | 0.3589 | 0.8614 |
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+ | 0.25 | 32.0 | 7232 | 0.3529 | 0.8648 |
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+ | 0.21 | 33.0 | 7458 | 0.3549 | 0.8581 |
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+ | 0.2119 | 34.0 | 7684 | 0.3504 | 0.8598 |
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+ | 0.2671 | 35.0 | 7910 | 0.3485 | 0.8648 |
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+ | 0.2299 | 36.0 | 8136 | 0.3541 | 0.8614 |
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+ | 0.2465 | 37.0 | 8362 | 0.3470 | 0.8631 |
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+ | 0.2631 | 38.0 | 8588 | 0.3446 | 0.8648 |
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+ | 0.239 | 39.0 | 8814 | 0.3471 | 0.8598 |
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+ | 0.1883 | 40.0 | 9040 | 0.3479 | 0.8648 |
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+ | 0.226 | 41.0 | 9266 | 0.3450 | 0.8681 |
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+ | 0.258 | 42.0 | 9492 | 0.3451 | 0.8698 |
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+ | 0.2562 | 43.0 | 9718 | 0.3456 | 0.8681 |
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+ | 0.1938 | 44.0 | 9944 | 0.3435 | 0.8681 |
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+ | 0.2807 | 45.0 | 10170 | 0.3449 | 0.8664 |
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+ | 0.253 | 46.0 | 10396 | 0.3419 | 0.8698 |
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+ | 0.2465 | 47.0 | 10622 | 0.3435 | 0.8664 |
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+ | 0.2602 | 48.0 | 10848 | 0.3432 | 0.8664 |
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+ | 0.2179 | 49.0 | 11074 | 0.3434 | 0.8681 |
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+ | 0.2786 | 50.0 | 11300 | 0.3433 | 0.8681 |
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  ### Framework versions
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