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# mit-b4-finetuned-stroke-binary

This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1162
- Accuracy: 0.9701
- F1: 0.9701
- Precision: 0.9701
- Recall: 0.9701

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5714        | 0.6202  | 100  | 0.4776          | 0.7879   | 0.7800 | 0.7900    | 0.7879 |
| 0.3897        | 1.2357  | 200  | 0.3239          | 0.8716   | 0.8704 | 0.8711    | 0.8716 |
| 0.2951        | 1.8558  | 300  | 0.3120          | 0.8765   | 0.8724 | 0.8858    | 0.8765 |
| 0.23          | 2.4713  | 400  | 0.1994          | 0.9281   | 0.9271 | 0.9304    | 0.9281 |
| 0.2135        | 3.0868  | 500  | 0.2157          | 0.9281   | 0.9267 | 0.9333    | 0.9281 |
| 0.2106        | 3.7070  | 600  | 0.1809          | 0.9380   | 0.9382 | 0.9387    | 0.9380 |
| 0.1576        | 4.3225  | 700  | 0.1629          | 0.9403   | 0.9404 | 0.9404    | 0.9403 |
| 0.1434        | 4.9426  | 800  | 0.1526          | 0.9543   | 0.9542 | 0.9543    | 0.9543 |
| 0.1391        | 5.5581  | 900  | 0.1268          | 0.9575   | 0.9575 | 0.9575    | 0.9575 |
| 0.1048        | 6.1736  | 1000 | 0.1489          | 0.9557   | 0.9555 | 0.9558    | 0.9557 |
| 0.1271        | 6.7938  | 1100 | 0.1448          | 0.9570   | 0.9566 | 0.9586    | 0.9570 |
| 0.091         | 7.4093  | 1200 | 0.1451          | 0.9570   | 0.9567 | 0.9580    | 0.9570 |
| 0.1159        | 8.0248  | 1300 | 0.1205          | 0.9629   | 0.9627 | 0.9636    | 0.9629 |
| 0.1151        | 8.6450  | 1400 | 0.1124          | 0.9665   | 0.9664 | 0.9666    | 0.9665 |
| 0.0735        | 9.2605  | 1500 | 0.1175          | 0.9643   | 0.9641 | 0.9645    | 0.9643 |
| 0.0537        | 9.8806  | 1600 | 0.1154          | 0.9679   | 0.9678 | 0.9679    | 0.9679 |
| 0.0666        | 10.4961 | 1700 | 0.1162          | 0.9701   | 0.9701 | 0.9701    | 0.9701 |
| 0.0732        | 11.1116 | 1800 | 0.1133          | 0.9679   | 0.9678 | 0.9679    | 0.9679 |
| 0.0775        | 11.7318 | 1900 | 0.1130          | 0.9683   | 0.9683 | 0.9684    | 0.9683 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0



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