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--- |
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library_name: transformers |
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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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- precision |
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- f1 |
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model-index: |
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- name: model_0-D2-SW-Tuned |
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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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# model_0-D2-SW-Tuned |
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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: 0.1783 |
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- Accuracy: 0.9889 |
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- Precision: 0.9889 |
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- Sensitivity: 0.9667 |
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- Specificity: 0.9933 |
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- F1: 0.9889 |
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- Auc: 0.9991 |
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- Mcc: 0.96 |
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- J Stat: 0.96 |
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- Confusion Matrix: [[149, 1], [1, 29]] |
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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: 4.286661471431606e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.07159086635599052 |
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- num_epochs: 7 |
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- label_smoothing_factor: 0.0713211794743841 |
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- weight_decay: 0.010753387469303224 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Sensitivity | Specificity | F1 | Auc | Mcc | J Stat | Confusion Matrix | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:-----------:|:-----------:|:------:|:------:|:------:|:------:|:-------------------:| |
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| 0.2816 | 1.0 | 376 | 0.2185 | 0.9722 | 0.9727 | 0.9333 | 0.98 | 0.9724 | 0.9929 | 0.9015 | 0.9133 | [[147, 3], [2, 28]] | |
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| 0.2998 | 2.0 | 752 | 0.2313 | 0.9611 | 0.9607 | 0.8667 | 0.98 | 0.9608 | 0.9947 | 0.8583 | 0.8467 | [[147, 3], [4, 26]] | |
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| 0.1991 | 3.0 | 1128 | 0.2396 | 0.9667 | 0.9662 | 0.8667 | 0.9867 | 0.9662 | 0.9951 | 0.8775 | 0.8533 | [[148, 2], [4, 26]] | |
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| 0.2611 | 4.0 | 1504 | 0.1923 | 0.9833 | 0.9837 | 0.9667 | 0.9867 | 0.9834 | 0.998 | 0.9410 | 0.9533 | [[148, 2], [1, 29]] | |
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| 0.2044 | 5.0 | 1880 | 0.2105 | 0.9722 | 0.9741 | 0.9667 | 0.9733 | 0.9727 | 0.9989 | 0.9054 | 0.94 | [[146, 4], [1, 29]] | |
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| 0.207 | 6.0 | 2256 | 0.1922 | 0.9833 | 0.9832 | 0.9333 | 0.9933 | 0.9832 | 0.9991 | 0.9394 | 0.9267 | [[149, 1], [2, 28]] | |
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| 0.1677 | 7.0 | 2632 | 0.1783 | 0.9889 | 0.9889 | 0.9667 | 0.9933 | 0.9889 | 0.9991 | 0.96 | 0.96 | [[149, 1], [1, 29]] | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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