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swinv2-tiny-patch4-window8-256-ve-Ub

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  1. README.md +26 -26
  2. model.safetensors +1 -1
  3. runs/Jun17_10-52-28_DESKTOP-SKBE9FB/events.out.tfevents.1718643149.DESKTOP-SKBE9FB.1840.0 +2 -2
README.md CHANGED
@@ -32,7 +32,7 @@ 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: 53618207116050790111890112512.0000
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  - Accuracy: 0.0980
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  ## Model description
@@ -65,31 +65,31 @@ The following hyperparameters were used during training:
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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 | 0.57 | 1 | 53618207116050790111890112512.0000 | 0.0980 |
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- | No log | 1.71 | 3 | 53618207116050790111890112512.0000 | 0.0980 |
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- | No log | 2.86 | 5 | 53618207116050790111890112512.0000 | 0.0980 |
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- | No log | 4.0 | 7 | 53618207116050790111890112512.0000 | 0.0980 |
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- | No log | 4.57 | 8 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 5.71 | 10 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 6.86 | 12 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 8.0 | 14 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 8.57 | 15 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 9.71 | 17 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52775460204830951003775827968.0000 | 10.86 | 19 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 12.0 | 21 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 12.57 | 22 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 13.71 | 24 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 14.86 | 26 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 16.0 | 28 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52792343609480503040906625024.0000 | 16.57 | 29 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52872559615505121741583155200.0000 | 17.71 | 31 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52872559615505121741583155200.0000 | 18.86 | 33 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52872559615505121741583155200.0000 | 20.0 | 35 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52872559615505121741583155200.0000 | 20.57 | 36 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52872559615505121741583155200.0000 | 21.71 | 38 | 53618207116050790111890112512.0000 | 0.0980 |
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- | 52783892462422761283050799104.0000 | 22.86 | 40 | 53618207116050790111890112512.0000 | 0.0980 |
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  ### Framework versions
 
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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: 4.3714
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  - Accuracy: 0.0980
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  ## Model description
 
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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 | 0.57 | 1 | 8.0201 | 0.0980 |
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+ | No log | 1.71 | 3 | 8.0044 | 0.0980 |
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+ | No log | 2.86 | 5 | 7.9306 | 0.0980 |
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+ | No log | 4.0 | 7 | 7.7713 | 0.0980 |
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+ | No log | 4.57 | 8 | 7.6511 | 0.0980 |
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+ | 7.7785 | 5.71 | 10 | 7.3653 | 0.0980 |
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+ | 7.7785 | 6.86 | 12 | 7.0246 | 0.0980 |
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+ | 7.7785 | 8.0 | 14 | 6.6413 | 0.0980 |
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+ | 7.7785 | 8.57 | 15 | 6.4670 | 0.0980 |
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+ | 7.7785 | 9.71 | 17 | 6.1321 | 0.0980 |
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+ | 7.7785 | 10.86 | 19 | 5.8360 | 0.0980 |
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+ | 6.5357 | 12.0 | 21 | 5.5743 | 0.0980 |
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+ | 6.5357 | 12.57 | 22 | 5.4552 | 0.0980 |
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+ | 6.5357 | 13.71 | 24 | 5.2367 | 0.0980 |
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+ | 6.5357 | 14.86 | 26 | 5.0418 | 0.0980 |
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+ | 6.5357 | 16.0 | 28 | 4.8706 | 0.0980 |
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+ | 6.5357 | 16.57 | 29 | 4.7939 | 0.0980 |
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+ | 5.2494 | 17.71 | 31 | 4.6596 | 0.0980 |
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+ | 5.2494 | 18.86 | 33 | 4.5508 | 0.0980 |
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+ | 5.2494 | 20.0 | 35 | 4.4676 | 0.0980 |
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+ | 5.2494 | 20.57 | 36 | 4.4356 | 0.0980 |
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+ | 5.2494 | 21.71 | 38 | 4.3906 | 0.0980 |
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+ | 4.5614 | 22.86 | 40 | 4.3714 | 0.0980 |
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  ### Framework versions
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