---
library_name: transformers
license: apache-2.0
base_model: Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ViT-NIH-Chest-X-ray-dataset-small
  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. -->

# ViT-NIH-Chest-X-ray-dataset-small

This model is a fine-tuned version of [Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small](https://huggingface.co/Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small) on the Sohaibsoussi/NIH-Chest-X-ray-dataset-small dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2988
- Accuracy: 0.2299

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2128        | 0.3690 | 100  | 0.2092          | 0.0      |
| 0.1848        | 0.7380 | 200  | 0.1909          | 0.3821   |
| 0.171         | 1.1070 | 300  | 0.1967          | 0.5387   |
| 0.1772        | 1.4760 | 400  | 0.1932          | 0.5451   |
| 0.1629        | 1.8450 | 500  | 0.1842          | 0.4486   |
| 0.1942        | 2.2140 | 600  | 0.1770          | 0.4197   |
| 0.1714        | 2.5830 | 700  | 0.1797          | 0.5023   |
| 0.1832        | 2.9520 | 800  | 0.1730          | 0.3688   |
| 0.1766        | 3.3210 | 900  | 0.1755          | 0.3428   |
| 0.1697        | 3.6900 | 1000 | 0.1601          | 0.5168   |
| 0.1568        | 4.0590 | 1100 | 0.1577          | 0.5353   |
| 0.1484        | 4.4280 | 1200 | 0.1514          | 0.4919   |
| 0.1483        | 4.7970 | 1300 | 0.1482          | 0.5699   |
| 0.1301        | 5.1661 | 1400 | 0.1315          | 0.5434   |
| 0.1149        | 5.5351 | 1500 | 0.1294          | 0.5584   |
| 0.1448        | 5.9041 | 1600 | 0.1266          | 0.5416   |
| 0.1035        | 6.2731 | 1700 | 0.1151          | 0.6017   |
| 0.1048        | 6.6421 | 1800 | 0.1060          | 0.6046   |
| 0.1168        | 7.0111 | 1900 | 0.1007          | 0.6173   |
| 0.1104        | 7.3801 | 2000 | 0.0949          | 0.6445   |
| 0.0873        | 7.7491 | 2100 | 0.0923          | 0.6526   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3