| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: microsoft/resnet-50 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: rsna |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # rsna |
|
|
| This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0842 |
| - Accuracy: 0.9697 |
| - Auc: 0.9606 |
| - F1: 0.6931 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| |
| | 0.1066 | 1.0 | 18964 | 0.1029 | 0.9629 | 0.9349 | 0.6084 | |
| | 0.0872 | 2.0 | 37928 | 0.0921 | 0.9670 | 0.9502 | 0.6681 | |
| | 0.0900 | 3.0 | 56892 | 0.0872 | 0.9686 | 0.9563 | 0.6803 | |
| | 0.0902 | 4.0 | 75856 | 0.0847 | 0.9694 | 0.9594 | 0.6934 | |
| | 0.0824 | 5.0 | 94820 | 0.0842 | 0.9697 | 0.9606 | 0.6931 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.0.0.dev0 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
