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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2 |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2 |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0909 |
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- Accuracy: 0.9769 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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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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| 1.5439 | 1.09 | 100 | 1.4188 | 0.5538 | |
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| 0.8646 | 2.17 | 200 | 0.4542 | 0.8885 | |
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| 0.5485 | 3.26 | 300 | 0.4103 | 0.8538 | |
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| 0.5082 | 4.35 | 400 | 0.2925 | 0.8962 | |
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| 0.5302 | 5.43 | 500 | 0.2471 | 0.9269 | |
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| 0.4072 | 6.52 | 600 | 0.2676 | 0.9231 | |
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| 0.4424 | 7.61 | 700 | 0.4150 | 0.9038 | |
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| 0.3409 | 8.7 | 800 | 0.1922 | 0.9538 | |
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| 0.3046 | 9.78 | 900 | 0.1917 | 0.9462 | |
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| 0.2911 | 10.87 | 1000 | 0.2272 | 0.9423 | |
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| 0.269 | 11.96 | 1100 | 0.0722 | 0.9692 | |
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| 0.3709 | 13.04 | 1200 | 0.1473 | 0.9654 | |
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| 0.3443 | 14.13 | 1300 | 0.1545 | 0.9615 | |
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| 0.187 | 15.22 | 1400 | 0.1060 | 0.9731 | |
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| 0.1879 | 16.3 | 1500 | 0.1124 | 0.9692 | |
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| 0.2183 | 17.39 | 1600 | 0.1377 | 0.9615 | |
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| 0.1478 | 18.48 | 1700 | 0.1269 | 0.9769 | |
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| 0.1944 | 19.57 | 1800 | 0.0909 | 0.9769 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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