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swin-tiny-patch4-window7-224-finetuned-mobile-eye-tracking-dataset-v2

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  2. model.safetensors +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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: 0.4013
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  - Accuracy: 0.8653
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  ## Model description
@@ -61,17 +61,27 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 5
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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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- | 0.3599 | 0.99 | 59 | 0.4013 | 0.8653 |
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- | 0.3768 | 2.0 | 119 | 0.3993 | 0.8653 |
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- | 0.3549 | 2.99 | 178 | 0.3922 | 0.8653 |
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- | 0.3838 | 4.0 | 238 | 0.3893 | 0.8653 |
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- | 0.3531 | 4.96 | 295 | 0.3903 | 0.8653 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 0.3944
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  - Accuracy: 0.8653
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  ## Model description
 
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 15
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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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+ | 0.3648 | 0.99 | 59 | 0.3998 | 0.8653 |
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+ | 0.3789 | 2.0 | 119 | 0.4005 | 0.8653 |
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+ | 0.3572 | 2.99 | 178 | 0.4006 | 0.8653 |
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+ | 0.3842 | 4.0 | 238 | 0.3905 | 0.8653 |
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+ | 0.356 | 4.99 | 297 | 0.3894 | 0.8653 |
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+ | 0.3564 | 6.0 | 357 | 0.3936 | 0.8653 |
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+ | 0.3668 | 6.99 | 416 | 0.3934 | 0.8653 |
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+ | 0.3538 | 8.0 | 476 | 0.3882 | 0.8653 |
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+ | 0.353 | 8.99 | 535 | 0.3870 | 0.8653 |
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+ | 0.3481 | 10.0 | 595 | 0.3867 | 0.8653 |
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+ | 0.3315 | 10.99 | 654 | 0.3949 | 0.8653 |
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+ | 0.3456 | 12.0 | 714 | 0.3919 | 0.8678 |
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+ | 0.3329 | 12.99 | 773 | 0.3905 | 0.8653 |
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+ | 0.3409 | 14.0 | 833 | 0.3930 | 0.8653 |
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+ | 0.313 | 14.87 | 885 | 0.3944 | 0.8653 |
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  ### Framework versions
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