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---
library_name: transformers
license: other
base_model: nvidia/mit-b4
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: mit-b4-finetuned-stroke-binary
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. -->
# 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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