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SW2-RHS-DA

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swinv2
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  1. README.md +43 -43
  2. model.safetensors +1 -1
  3. runs/Dec04_00-07-09_DESKTOP-SKBE9FB/events.out.tfevents.1733292431.DESKTOP-SKBE9FB.8600.0 +2 -2
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: 0.616822429906542
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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/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.1002
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- - Accuracy: 0.6168
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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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- | 1.302 | 0.98 | 22 | 2.8474 | 0.4112 |
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- | 0.6224 | 2.0 | 45 | 1.4650 | 0.4112 |
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- | 0.3905 | 2.98 | 67 | 1.4865 | 0.4112 |
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- | 0.1416 | 4.0 | 90 | 0.9453 | 0.5981 |
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- | 0.1116 | 4.98 | 112 | 0.9801 | 0.5514 |
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- | 0.0866 | 6.0 | 135 | 1.5201 | 0.6075 |
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- | 0.0579 | 6.98 | 157 | 1.7234 | 0.5981 |
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- | 0.0667 | 8.0 | 180 | 1.9649 | 0.5981 |
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- | 0.0664 | 8.98 | 202 | 1.9505 | 0.5981 |
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- | 0.0742 | 10.0 | 225 | 1.9448 | 0.5981 |
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- | 0.0558 | 10.98 | 247 | 1.9545 | 0.5981 |
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- | 0.0475 | 12.0 | 270 | 2.1516 | 0.5888 |
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- | 0.114 | 12.98 | 292 | 2.1002 | 0.6168 |
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- | 0.051 | 14.0 | 315 | 2.2643 | 0.5981 |
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- | 0.0318 | 14.98 | 337 | 2.3468 | 0.5981 |
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- | 0.0673 | 16.0 | 360 | 2.3341 | 0.6075 |
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- | 0.0566 | 16.98 | 382 | 2.3191 | 0.6075 |
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- | 0.0543 | 18.0 | 405 | 2.2807 | 0.6168 |
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- | 0.0458 | 18.98 | 427 | 2.2148 | 0.6168 |
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- | 0.0421 | 20.0 | 450 | 2.5468 | 0.5888 |
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- | 0.0139 | 20.98 | 472 | 2.2408 | 0.6168 |
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- | 0.012 | 22.0 | 495 | 2.5689 | 0.5888 |
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- | 0.017 | 22.98 | 517 | 2.5485 | 0.6168 |
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- | 0.0529 | 24.0 | 540 | 2.6746 | 0.6075 |
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- | 0.0414 | 24.98 | 562 | 2.7693 | 0.5888 |
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- | 0.0158 | 26.0 | 585 | 2.7447 | 0.5888 |
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- | 0.0205 | 26.98 | 607 | 2.8566 | 0.5888 |
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- | 0.0205 | 28.0 | 630 | 2.8469 | 0.5888 |
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- | 0.006 | 28.98 | 652 | 2.9508 | 0.5888 |
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- | 0.0061 | 30.0 | 675 | 3.0560 | 0.5888 |
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- | 0.0227 | 30.98 | 697 | 3.0431 | 0.5888 |
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- | 0.034 | 32.0 | 720 | 3.0497 | 0.5888 |
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- | 0.0039 | 32.98 | 742 | 3.0936 | 0.5888 |
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- | 0.0031 | 34.0 | 765 | 3.1158 | 0.5888 |
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- | 0.0118 | 34.98 | 787 | 3.1093 | 0.5888 |
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- | 0.0045 | 36.0 | 810 | 3.0885 | 0.5888 |
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- | 0.0168 | 36.98 | 832 | 3.0814 | 0.5888 |
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- | 0.0071 | 38.0 | 855 | 3.0733 | 0.5888 |
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- | 0.0238 | 38.98 | 877 | 3.0825 | 0.5888 |
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- | 0.0072 | 39.11 | 880 | 3.0828 | 0.5888 |
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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: 0.8504672897196262
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  ---
27
 
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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/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4536
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+ - Accuracy: 0.8505
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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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+ | 2.0538 | 0.99 | 35 | 1.2866 | 0.4112 |
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+ | 0.7464 | 2.0 | 71 | 0.6780 | 0.5888 |
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+ | 0.7061 | 2.99 | 106 | 0.6851 | 0.5888 |
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+ | 0.6951 | 4.0 | 142 | 0.6742 | 0.5888 |
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+ | 0.6928 | 4.99 | 177 | 0.6917 | 0.4486 |
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+ | 0.683 | 6.0 | 213 | 0.6531 | 0.5794 |
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+ | 0.7013 | 6.99 | 248 | 0.7000 | 0.4486 |
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+ | 0.6921 | 8.0 | 284 | 0.7519 | 0.5514 |
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+ | 0.6166 | 8.99 | 319 | 0.5947 | 0.6822 |
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+ | 0.6128 | 10.0 | 355 | 0.5434 | 0.7850 |
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+ | 0.5737 | 10.99 | 390 | 0.5533 | 0.7570 |
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+ | 0.5376 | 12.0 | 426 | 0.5347 | 0.7103 |
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+ | 0.5056 | 12.99 | 461 | 0.4949 | 0.7664 |
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+ | 0.5396 | 14.0 | 497 | 0.5151 | 0.7477 |
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+ | 0.4826 | 14.99 | 532 | 0.5669 | 0.7196 |
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+ | 0.4269 | 16.0 | 568 | 0.4796 | 0.7570 |
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+ | 0.5004 | 16.99 | 603 | 0.4489 | 0.8037 |
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+ | 0.4116 | 18.0 | 639 | 0.4362 | 0.8224 |
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+ | 0.3776 | 18.99 | 674 | 0.5300 | 0.7570 |
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+ | 0.3646 | 20.0 | 710 | 0.4175 | 0.8037 |
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+ | 0.3683 | 20.99 | 745 | 0.4700 | 0.8224 |
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+ | 0.3277 | 22.0 | 781 | 0.4707 | 0.8131 |
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+ | 0.3534 | 22.99 | 816 | 0.5240 | 0.8131 |
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+ | 0.3083 | 24.0 | 852 | 0.5012 | 0.8131 |
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+ | 0.2829 | 24.99 | 887 | 0.4421 | 0.8318 |
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+ | 0.2564 | 26.0 | 923 | 0.4548 | 0.8224 |
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+ | 0.3136 | 26.99 | 958 | 0.4374 | 0.8318 |
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+ | 0.2443 | 28.0 | 994 | 0.5277 | 0.8131 |
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+ | 0.258 | 28.99 | 1029 | 0.4601 | 0.8224 |
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+ | 0.2673 | 30.0 | 1065 | 0.4520 | 0.8318 |
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+ | 0.2233 | 30.99 | 1100 | 0.4541 | 0.8598 |
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+ | 0.2276 | 32.0 | 1136 | 0.4247 | 0.8505 |
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+ | 0.2653 | 32.99 | 1171 | 0.4091 | 0.8505 |
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+ | 0.2007 | 34.0 | 1207 | 0.4719 | 0.8505 |
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+ | 0.2082 | 34.99 | 1242 | 0.4624 | 0.8411 |
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+ | 0.1794 | 36.0 | 1278 | 0.4856 | 0.8318 |
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+ | 0.1987 | 36.99 | 1313 | 0.4904 | 0.8224 |
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+ | 0.2066 | 38.0 | 1349 | 0.4741 | 0.8505 |
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+ | 0.1972 | 38.99 | 1384 | 0.4530 | 0.8505 |
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+ | 0.2319 | 39.44 | 1400 | 0.4536 | 0.8505 |
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  ### Framework versions
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