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swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2

Image Classification
Transformers
PyTorch
swin
Generated from Trainer
Inference Endpoints
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  1. README.md +22 -14
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
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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
@@ -16,10 +15,10 @@ 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: 0.1319
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- - Accuracy: 0.9769
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  ## Model description
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@@ -44,21 +43,30 @@ The following hyperparameters were used during training:
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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: 10
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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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- | 1.6751 | 1.09 | 100 | 1.1247 | 0.6308 |
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- | 0.928 | 2.17 | 200 | 0.5505 | 0.8462 |
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- | 0.4477 | 3.26 | 300 | 0.3167 | 0.9038 |
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- | 0.5154 | 4.35 | 400 | 0.2101 | 0.9423 |
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- | 0.4627 | 5.43 | 500 | 0.2289 | 0.9346 |
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- | 0.3103 | 6.52 | 600 | 0.1501 | 0.9615 |
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- | 0.3637 | 7.61 | 700 | 0.1319 | 0.9769 |
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- | 0.1941 | 8.7 | 800 | 0.0861 | 0.9769 |
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- | 0.1663 | 9.78 | 900 | 0.0867 | 0.9769 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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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  # 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: 0.0834
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+ - Accuracy: 0.9846
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  ## Model description
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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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+ | 1.7296 | 1.09 | 100 | 1.2288 | 0.6115 |
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+ | 1.0961 | 2.17 | 200 | 0.6633 | 0.8115 |
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+ | 0.5971 | 3.26 | 300 | 0.2989 | 0.8846 |
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+ | 0.5663 | 4.35 | 400 | 0.2658 | 0.9538 |
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+ | 0.6431 | 5.43 | 500 | 0.3100 | 0.8846 |
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+ | 0.49 | 6.52 | 600 | 0.2457 | 0.9192 |
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+ | 0.4639 | 7.61 | 700 | 0.2958 | 0.9154 |
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+ | 0.2436 | 8.7 | 800 | 0.2147 | 0.9192 |
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+ | 0.2652 | 9.78 | 900 | 0.1822 | 0.9615 |
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+ | 0.2346 | 10.87 | 1000 | 0.2716 | 0.9385 |
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+ | 0.3326 | 11.96 | 1100 | 0.0930 | 0.9692 |
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+ | 0.4255 | 13.04 | 1200 | 0.1565 | 0.95 |
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+ | 0.3486 | 14.13 | 1300 | 0.0881 | 0.9808 |
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+ | 0.2801 | 15.22 | 1400 | 0.1083 | 0.9577 |
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+ | 0.164 | 16.3 | 1500 | 0.0994 | 0.9731 |
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+ | 0.2892 | 17.39 | 1600 | 0.0784 | 0.9846 |
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+ | 0.1033 | 18.48 | 1700 | 0.0933 | 0.9808 |
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+ | 0.1612 | 19.57 | 1800 | 0.0834 | 0.9846 |
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  ### Framework versions
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