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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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- image-classification |
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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 the ASL_Isolated_Swin_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0439 |
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- Accuracy: 0.9846 |
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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: 50 |
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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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| 1.5603 | 1.09 | 100 | 1.0931 | 0.6423 | |
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| 0.9055 | 2.17 | 200 | 0.5069 | 0.8615 | |
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| 0.4254 | 3.26 | 300 | 0.5634 | 0.8154 | |
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| 0.5814 | 4.35 | 400 | 0.2883 | 0.9154 | |
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| 0.4953 | 5.43 | 500 | 0.2710 | 0.9154 | |
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| 0.4456 | 6.52 | 600 | 0.2451 | 0.9346 | |
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| 0.4524 | 7.61 | 700 | 0.2625 | 0.9308 | |
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| 0.3095 | 8.7 | 800 | 0.2397 | 0.9462 | |
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| 0.3224 | 9.78 | 900 | 0.1787 | 0.9385 | |
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| 0.4069 | 10.87 | 1000 | 0.3376 | 0.9231 | |
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| 0.3467 | 11.96 | 1100 | 0.1603 | 0.9538 | |
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| 0.469 | 13.04 | 1200 | 0.2247 | 0.9423 | |
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| 0.4523 | 14.13 | 1300 | 0.1552 | 0.9538 | |
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| 0.2923 | 15.22 | 1400 | 0.3376 | 0.9346 | |
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| 0.3139 | 16.3 | 1500 | 0.1449 | 0.9577 | |
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| 0.3873 | 17.39 | 1600 | 0.1495 | 0.9654 | |
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| 0.2994 | 18.48 | 1700 | 0.1821 | 0.9654 | |
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| 0.2611 | 19.57 | 1800 | 0.1294 | 0.9769 | |
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| 0.1883 | 20.65 | 1900 | 0.0879 | 0.9731 | |
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| 0.2076 | 21.74 | 2000 | 0.1969 | 0.95 | |
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| 0.3531 | 22.83 | 2100 | 0.2135 | 0.9538 | |
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| 0.4339 | 23.91 | 2200 | 0.1030 | 0.9615 | |
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| 0.2959 | 25.0 | 2300 | 0.1579 | 0.9731 | |
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| 0.1546 | 26.09 | 2400 | 0.1648 | 0.9692 | |
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| 0.1315 | 27.17 | 2500 | 0.1514 | 0.9577 | |
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| 0.2191 | 28.26 | 2600 | 0.1257 | 0.9538 | |
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| 0.16 | 29.35 | 2700 | 0.1162 | 0.9692 | |
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| 0.1567 | 30.43 | 2800 | 0.1252 | 0.9731 | |
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| 0.1147 | 31.52 | 2900 | 0.2642 | 0.9577 | |
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| 0.1434 | 32.61 | 3000 | 0.1371 | 0.9769 | |
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| 0.2488 | 33.7 | 3100 | 0.1161 | 0.9769 | |
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| 0.1646 | 34.78 | 3200 | 0.2052 | 0.9615 | |
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| 0.1326 | 35.87 | 3300 | 0.1995 | 0.9769 | |
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| 0.137 | 36.96 | 3400 | 0.1124 | 0.9731 | |
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| 0.1633 | 38.04 | 3500 | 0.1620 | 0.9692 | |
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| 0.1593 | 39.13 | 3600 | 0.1838 | 0.9731 | |
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| 0.2192 | 40.22 | 3700 | 0.1331 | 0.9769 | |
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| 0.1495 | 41.3 | 3800 | 0.1291 | 0.9731 | |
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| 0.226 | 42.39 | 3900 | 0.1090 | 0.9692 | |
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| 0.1383 | 43.48 | 4000 | 0.0994 | 0.9654 | |
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| 0.0491 | 44.57 | 4100 | 0.0660 | 0.9769 | |
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| 0.1034 | 45.65 | 4200 | 0.0698 | 0.9808 | |
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| 0.0893 | 46.74 | 4300 | 0.0439 | 0.9846 | |
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| 0.1789 | 47.83 | 4400 | 0.0577 | 0.9808 | |
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| 0.0569 | 48.91 | 4500 | 0.0547 | 0.9846 | |
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| 0.1113 | 50.0 | 4600 | 0.0605 | 0.9846 | |
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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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