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  1. README.md +34 -7
  2. model.safetensors +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: 0.9476209516193522
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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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1349
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- - Accuracy: 0.9476
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  ## Model description
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@@ -61,15 +61,42 @@ The following hyperparameters were used during training:
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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: 3
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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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- | 0.2131 | 1.0 | 51 | 0.1843 | 0.9320 |
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- | 0.1521 | 2.0 | 102 | 0.1215 | 0.9552 |
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- | 0.1318 | 3.0 | 153 | 0.1349 | 0.9476 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9546181527389045
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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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1631
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+ - Accuracy: 0.9546
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  ## Model description
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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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+ | 0.4463 | 1.0 | 51 | 0.3232 | 0.9166 |
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+ | 0.2061 | 2.0 | 102 | 0.1659 | 0.9398 |
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+ | 0.1795 | 3.0 | 153 | 0.2043 | 0.9192 |
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+ | 0.1356 | 4.0 | 204 | 0.1204 | 0.9542 |
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+ | 0.1193 | 5.0 | 255 | 0.1357 | 0.9454 |
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+ | 0.1054 | 6.0 | 306 | 0.1197 | 0.9516 |
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+ | 0.0974 | 7.0 | 357 | 0.1081 | 0.9586 |
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+ | 0.092 | 8.0 | 408 | 0.1220 | 0.9532 |
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+ | 0.0601 | 9.0 | 459 | 0.1587 | 0.9466 |
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+ | 0.0639 | 10.0 | 510 | 0.1676 | 0.9440 |
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+ | 0.072 | 11.0 | 561 | 0.1058 | 0.9618 |
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+ | 0.0606 | 12.0 | 612 | 0.1061 | 0.9634 |
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+ | 0.0572 | 13.0 | 663 | 0.1375 | 0.9552 |
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+ | 0.0563 | 14.0 | 714 | 0.1377 | 0.9548 |
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+ | 0.0413 | 15.0 | 765 | 0.1823 | 0.9470 |
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+ | 0.0361 | 16.0 | 816 | 0.0992 | 0.9674 |
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+ | 0.0471 | 17.0 | 867 | 0.1508 | 0.9550 |
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+ | 0.04 | 18.0 | 918 | 0.1700 | 0.9506 |
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+ | 0.0417 | 19.0 | 969 | 0.1760 | 0.9454 |
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+ | 0.0238 | 20.0 | 1020 | 0.1311 | 0.9600 |
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+ | 0.0319 | 21.0 | 1071 | 0.1502 | 0.9562 |
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+ | 0.0328 | 22.0 | 1122 | 0.1843 | 0.9484 |
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+ | 0.0363 | 23.0 | 1173 | 0.1473 | 0.9558 |
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+ | 0.0385 | 24.0 | 1224 | 0.1625 | 0.9516 |
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+ | 0.0198 | 25.0 | 1275 | 0.1749 | 0.9490 |
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+ | 0.0349 | 26.0 | 1326 | 0.1586 | 0.9528 |
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+ | 0.0337 | 27.0 | 1377 | 0.1343 | 0.9614 |
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+ | 0.0261 | 28.0 | 1428 | 0.1624 | 0.9542 |
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+ | 0.0253 | 29.0 | 1479 | 0.1727 | 0.9532 |
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+ | 0.0271 | 30.0 | 1530 | 0.1631 | 0.9546 |
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  ### Framework versions
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