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vit-base-patch16-224-in21k-finetuned-papsmear

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  1. README.md +54 -37
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9411764705882353
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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
@@ -33,8 +33,8 @@ 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 the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2523
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- - Accuracy: 0.9412
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  ## Model description
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@@ -54,50 +54,67 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 32
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 30
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | 1.6954 | 0.9935 | 38 | 1.6106 | 0.3456 |
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- | 1.2818 | 1.9869 | 76 | 1.2412 | 0.5735 |
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- | 1.0023 | 2.9804 | 114 | 0.9875 | 0.7132 |
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- | 0.7163 | 4.0 | 153 | 0.8399 | 0.6912 |
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- | 0.5173 | 4.9935 | 191 | 0.6546 | 0.8162 |
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- | 0.5057 | 5.9869 | 229 | 0.6251 | 0.8309 |
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- | 0.4313 | 6.9804 | 267 | 0.5696 | 0.8309 |
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- | 0.325 | 8.0 | 306 | 0.5507 | 0.8309 |
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- | 0.3811 | 8.9935 | 344 | 0.4429 | 0.8676 |
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- | 0.2341 | 9.9869 | 382 | 0.4222 | 0.875 |
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- | 0.3082 | 10.9804 | 420 | 0.6573 | 0.7721 |
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- | 0.2571 | 12.0 | 459 | 0.4229 | 0.8897 |
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- | 0.2374 | 12.9935 | 497 | 0.4233 | 0.875 |
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- | 0.128 | 13.9869 | 535 | 0.3671 | 0.8971 |
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- | 0.1718 | 14.9804 | 573 | 0.3430 | 0.8971 |
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- | 0.16 | 16.0 | 612 | 0.4104 | 0.875 |
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- | 0.1096 | 16.9935 | 650 | 0.2920 | 0.9118 |
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- | 0.1408 | 17.9869 | 688 | 0.2630 | 0.9044 |
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- | 0.113 | 18.9804 | 726 | 0.3084 | 0.8824 |
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- | 0.1272 | 20.0 | 765 | 0.2523 | 0.9412 |
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- | 0.119 | 20.9935 | 803 | 0.4254 | 0.8824 |
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- | 0.1068 | 21.9869 | 841 | 0.3519 | 0.8971 |
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- | 0.0723 | 22.9804 | 879 | 0.3293 | 0.9191 |
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- | 0.0769 | 24.0 | 918 | 0.2613 | 0.9265 |
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- | 0.095 | 24.9935 | 956 | 0.2609 | 0.9412 |
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- | 0.0863 | 25.9869 | 994 | 0.2650 | 0.9265 |
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- | 0.0795 | 26.9804 | 1032 | 0.2978 | 0.9118 |
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- | 0.0564 | 28.0 | 1071 | 0.2737 | 0.9191 |
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- | 0.0562 | 28.9935 | 1109 | 0.2941 | 0.9191 |
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- | 0.0751 | 29.8039 | 1140 | 0.3111 | 0.9191 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9338235294117647
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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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2870
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+ - Accuracy: 0.9338
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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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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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 0.9231 | 9 | 1.7879 | 0.2059 |
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+ | 1.8037 | 1.9487 | 19 | 1.6485 | 0.4044 |
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+ | 1.6961 | 2.9744 | 29 | 1.4882 | 0.3971 |
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+ | 1.5407 | 4.0 | 39 | 1.3069 | 0.5221 |
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+ | 1.3308 | 4.9231 | 48 | 1.1339 | 0.6029 |
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+ | 1.1074 | 5.9487 | 58 | 0.9396 | 0.75 |
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+ | 0.9162 | 6.9744 | 68 | 0.8551 | 0.7647 |
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+ | 0.8174 | 8.0 | 78 | 0.8291 | 0.7574 |
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+ | 0.7135 | 8.9231 | 87 | 0.7505 | 0.7941 |
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+ | 0.6222 | 9.9487 | 97 | 0.6434 | 0.8456 |
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+ | 0.5445 | 10.9744 | 107 | 0.5996 | 0.8529 |
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+ | 0.4935 | 12.0 | 117 | 0.5514 | 0.8529 |
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+ | 0.4131 | 12.9231 | 126 | 0.5029 | 0.8603 |
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+ | 0.4012 | 13.9487 | 136 | 0.5566 | 0.8382 |
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+ | 0.3689 | 14.9744 | 146 | 0.5533 | 0.8382 |
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+ | 0.3533 | 16.0 | 156 | 0.4232 | 0.8971 |
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+ | 0.2954 | 16.9231 | 165 | 0.4589 | 0.8897 |
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+ | 0.2907 | 17.9487 | 175 | 0.4223 | 0.8971 |
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+ | 0.2804 | 18.9744 | 185 | 0.4056 | 0.8971 |
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+ | 0.2469 | 20.0 | 195 | 0.3904 | 0.9118 |
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+ | 0.2643 | 20.9231 | 204 | 0.3866 | 0.9044 |
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+ | 0.2212 | 21.9487 | 214 | 0.4173 | 0.875 |
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+ | 0.2476 | 22.9744 | 224 | 0.6001 | 0.8015 |
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+ | 0.2347 | 24.0 | 234 | 0.3900 | 0.9044 |
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+ | 0.207 | 24.9231 | 243 | 0.4033 | 0.8897 |
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+ | 0.1803 | 25.9487 | 253 | 0.3510 | 0.9265 |
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+ | 0.1979 | 26.9744 | 263 | 0.3723 | 0.9191 |
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+ | 0.1821 | 28.0 | 273 | 0.4320 | 0.8824 |
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+ | 0.1992 | 28.9231 | 282 | 0.3557 | 0.9118 |
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+ | 0.2154 | 29.9487 | 292 | 0.3362 | 0.9191 |
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+ | 0.1801 | 30.9744 | 302 | 0.4358 | 0.875 |
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+ | 0.1794 | 32.0 | 312 | 0.3500 | 0.9191 |
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+ | 0.1566 | 32.9231 | 321 | 0.3046 | 0.9265 |
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+ | 0.1432 | 33.9487 | 331 | 0.3239 | 0.9265 |
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+ | 0.145 | 34.9744 | 341 | 0.3311 | 0.9338 |
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+ | 0.1578 | 36.0 | 351 | 0.3029 | 0.9338 |
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+ | 0.1511 | 36.9231 | 360 | 0.3010 | 0.9338 |
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+ | 0.139 | 37.9487 | 370 | 0.2982 | 0.9265 |
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+ | 0.1294 | 38.9744 | 380 | 0.3261 | 0.9191 |
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+ | 0.1263 | 40.0 | 390 | 0.2932 | 0.9338 |
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+ | 0.1263 | 40.9231 | 399 | 0.2944 | 0.9338 |
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+ | 0.1216 | 41.9487 | 409 | 0.2867 | 0.9338 |
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+ | 0.1199 | 42.9744 | 419 | 0.2887 | 0.9338 |
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+ | 0.128 | 44.0 | 429 | 0.2825 | 0.9338 |
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+ | 0.1115 | 44.9231 | 438 | 0.2880 | 0.9338 |
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+ | 0.1179 | 45.9487 | 448 | 0.2871 | 0.9338 |
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+ | 0.12 | 46.1538 | 450 | 0.2870 | 0.9338 |
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  ### Framework versions
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