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segformer-class-classWeights-augmentation

Image Classification
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swin
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update model card README.md

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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: 0.9655172413793104
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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: 0.0355
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- - Accuracy: 0.9655
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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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- | No log | 0.89 | 6 | 1.0977 | 0.5517 |
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- | 1.0215 | 1.93 | 13 | 0.6858 | 0.7931 |
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- | 0.6364 | 2.96 | 20 | 0.9383 | 0.6897 |
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- | 0.6364 | 4.0 | 27 | 0.2391 | 0.9310 |
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- | 0.2716 | 4.89 | 33 | 0.1767 | 0.8966 |
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- | 0.2295 | 5.93 | 40 | 0.2729 | 0.9310 |
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- | 0.2295 | 6.96 | 47 | 0.1429 | 0.9655 |
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- | 0.1311 | 8.0 | 54 | 0.1929 | 0.9655 |
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- | 0.1503 | 8.89 | 60 | 0.1718 | 0.9655 |
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- | 0.1503 | 9.93 | 67 | 0.1631 | 0.9655 |
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- | 0.1554 | 10.96 | 74 | 0.2690 | 0.9655 |
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- | 0.1157 | 12.0 | 81 | 0.1331 | 0.9655 |
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- | 0.1157 | 12.89 | 87 | 0.0512 | 0.9655 |
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- | 0.1093 | 13.93 | 94 | 0.0273 | 1.0 |
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- | 0.134 | 14.96 | 101 | 0.0356 | 0.9655 |
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- | 0.134 | 16.0 | 108 | 0.0477 | 0.9655 |
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- | 0.0926 | 16.89 | 114 | 0.0381 | 0.9655 |
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- | 0.1363 | 17.78 | 120 | 0.0355 | 0.9655 |
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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: 1.0
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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: 0.0018
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+ - Accuracy: 1.0
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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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+ | No log | 0.89 | 6 | 0.9811 | 0.5862 |
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+ | 1.0899 | 1.93 | 13 | 0.9601 | 0.5862 |
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+ | 0.7599 | 2.96 | 20 | 0.8930 | 0.6897 |
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+ | 0.7599 | 4.0 | 27 | 0.5230 | 0.7931 |
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+ | 0.3436 | 4.89 | 33 | 0.1652 | 0.9310 |
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+ | 0.1853 | 5.93 | 40 | 0.0544 | 1.0 |
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+ | 0.1853 | 6.96 | 47 | 0.0937 | 0.9655 |
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+ | 0.1839 | 8.0 | 54 | 0.0566 | 0.9655 |
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+ | 0.1772 | 8.89 | 60 | 0.0084 | 1.0 |
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+ | 0.1772 | 9.93 | 67 | 0.0114 | 1.0 |
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+ | 0.1016 | 10.96 | 74 | 0.0039 | 1.0 |
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+ | 0.1534 | 12.0 | 81 | 0.0038 | 1.0 |
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+ | 0.1534 | 12.89 | 87 | 0.0187 | 0.9655 |
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+ | 0.1256 | 13.93 | 94 | 0.0023 | 1.0 |
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+ | 0.1234 | 14.96 | 101 | 0.0016 | 1.0 |
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+ | 0.1234 | 16.0 | 108 | 0.0019 | 1.0 |
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+ | 0.1129 | 16.89 | 114 | 0.0018 | 1.0 |
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+ | 0.17 | 17.78 | 120 | 0.0018 | 1.0 |
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  ### Framework versions