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

Image Classification
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Generated from Trainer
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  1. README.md +14 -14
  2. model.safetensors +1 -1
  3. runs/Jul26_19-51-36_affba4c7048c/events.out.tfevents.1722023505.affba4c7048c +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: 0.8915401301518439
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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: 0.2895
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- - Accuracy: 0.8915
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  ## Model description
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@@ -67,21 +67,21 @@ 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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- | 1.0635 | 0.9846 | 16 | 0.7524 | 0.6725 |
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- | 0.4571 | 1.9692 | 32 | 0.3692 | 0.8742 |
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- | 0.3819 | 2.9538 | 48 | 0.3500 | 0.8688 |
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- | 0.3278 | 4.0 | 65 | 0.3158 | 0.8796 |
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- | 0.2941 | 4.9846 | 81 | 0.2886 | 0.8883 |
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- | 0.2912 | 5.9692 | 97 | 0.2895 | 0.8915 |
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- | 0.2575 | 6.9538 | 113 | 0.2801 | 0.8839 |
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- | 0.2604 | 8.0 | 130 | 0.2847 | 0.8861 |
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- | 0.2519 | 8.9846 | 146 | 0.2804 | 0.8872 |
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- | 0.2592 | 9.8462 | 160 | 0.2795 | 0.8872 |
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  ### Framework versions
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- - Transformers 4.43.2
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  - Pytorch 1.13.1+cu117
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  - Datasets 2.20.0
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  - Tokenizers 0.19.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8937093275488069
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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: 0.2765
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+ - Accuracy: 0.8937
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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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+ | 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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+ - Transformers 4.43.3
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  - Pytorch 1.13.1+cu117
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  - Datasets 2.20.0
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  - Tokenizers 0.19.1
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