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djbp
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swin-tiny-patch4-window7-224-MM_Classification

Image Classification
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swin
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  1. README.md +13 -13
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@@ -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: 0.9229934924078091
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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: 0.2217
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- - Accuracy: 0.9230
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  ## Model description
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@@ -67,16 +67,16 @@ 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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- | 0.9712 | 0.9846 | 16 | 0.5834 | 0.7473 |
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- | 0.4751 | 1.9692 | 32 | 0.3465 | 0.8644 |
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- | 0.3677 | 2.9538 | 48 | 0.3184 | 0.8785 |
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- | 0.3052 | 4.0 | 65 | 0.3005 | 0.8829 |
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- | 0.2935 | 4.9846 | 81 | 0.3044 | 0.8796 |
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- | 0.2811 | 5.9692 | 97 | 0.2830 | 0.8959 |
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- | 0.2659 | 6.9538 | 113 | 0.2784 | 0.8959 |
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- | 0.2584 | 8.0 | 130 | 0.2824 | 0.8937 |
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- | 0.2623 | 8.9846 | 146 | 0.2790 | 0.8970 |
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- | 0.2487 | 9.8462 | 160 | 0.2765 | 0.8937 |
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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: 0.8947939262472885
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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: 0.2758
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+ - Accuracy: 0.8948
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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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+ | 1.0041 | 0.9846 | 16 | 0.6399 | 0.7082 |
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+ | 0.4441 | 1.9692 | 32 | 0.3671 | 0.8688 |
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+ | 0.3563 | 2.9538 | 48 | 0.3454 | 0.8688 |
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+ | 0.3071 | 4.0 | 65 | 0.3100 | 0.8861 |
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+ | 0.2933 | 4.9846 | 81 | 0.2900 | 0.8894 |
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+ | 0.2841 | 5.9692 | 97 | 0.2917 | 0.8829 |
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+ | 0.2715 | 6.9538 | 113 | 0.2846 | 0.8894 |
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+ | 0.2564 | 8.0 | 130 | 0.2835 | 0.8926 |
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+ | 0.2639 | 8.9846 | 146 | 0.2799 | 0.8926 |
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+ | 0.2505 | 9.8462 | 160 | 0.2758 | 0.8948 |
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  ### Framework versions