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--- |
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base_model: VIT |
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tags: |
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- image-classification |
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- breast cancer |
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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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- recall |
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- f1 |
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model-index: |
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- name: vit |
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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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# vit |
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This model is a fine-tuned version of [VIT](https://huggingface.co/VIT) on the Mammogram V1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1157 |
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- Accuracy: 0.9625 |
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- Precision: 0.9745 |
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- Recall: 0.9625 |
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- F1: 0.9682 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4204 | 1.0 | 1112 | 0.1572 | 0.9797 | 0.9740 | 0.9797 | 0.9767 | |
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| 0.3987 | 2.0 | 2224 | 0.2308 | 0.9253 | 0.9745 | 0.9253 | 0.9482 | |
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| 0.2347 | 3.0 | 3336 | 0.1360 | 0.9516 | 0.9737 | 0.9516 | 0.9622 | |
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| 0.1283 | 4.0 | 4448 | 0.1255 | 0.9564 | 0.9743 | 0.9564 | 0.9649 | |
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| 0.1304 | 5.0 | 5560 | 0.1157 | 0.9625 | 0.9745 | 0.9625 | 0.9682 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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