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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: google/vit-base-patch16-224-in21k |
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tags: |
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- medical-imaging |
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- chest-xray |
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- tumor-detection |
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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: vit-xray-tumor |
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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-xray-tumor |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chest-xray-tumor dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2989 |
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- Accuracy: 0.9574 |
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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: 1e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Use 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: cosine |
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- lr_scheduler_warmup_steps: 100 |
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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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| 0.5283 | 3.6765 | 125 | 0.2948 | 0.9606 | |
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| 0.516 | 7.3529 | 250 | 0.2843 | 0.9601 | |
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| 0.4878 | 11.0294 | 375 | 0.2756 | 0.9601 | |
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| 0.459 | 14.7059 | 500 | 0.2801 | 0.9601 | |
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| 0.4462 | 18.3824 | 625 | 0.2761 | 0.9595 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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