---
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: 0.6731
- Accuracy: 0.2189

## 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: 9
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0271        | 0.3690 | 100  | 0.0347          | 0.8584   |
| 0.0334        | 0.7380 | 200  | 0.0291          | 0.8624   |
| 0.0438        | 1.1070 | 300  | 0.0352          | 0.8607   |
| 0.0215        | 1.4760 | 400  | 0.0319          | 0.8746   |
| 0.0267        | 1.8450 | 500  | 0.0277          | 0.8798   |
| 0.0266        | 2.2140 | 600  | 0.0177          | 0.9116   |
| 0.014         | 2.5830 | 700  | 0.0127          | 0.9497   |
| 0.0207        | 2.9520 | 800  | 0.0144          | 0.9410   |
| 0.0115        | 3.3210 | 900  | 0.0097          | 0.9653   |
| 0.0113        | 3.6900 | 1000 | 0.0077          | 0.9711   |
| 0.0054        | 4.0590 | 1100 | 0.0068          | 0.9844   |
| 0.0047        | 4.4280 | 1200 | 0.0046          | 0.9850   |
| 0.0056        | 4.7970 | 1300 | 0.0040          | 0.9902   |
| 0.0026        | 5.1661 | 1400 | 0.0032          | 0.9925   |
| 0.0037        | 5.5351 | 1500 | 0.0027          | 0.9936   |
| 0.0039        | 5.9041 | 1600 | 0.0023          | 0.9977   |
| 0.0019        | 6.2731 | 1700 | 0.0019          | 0.9971   |
| 0.0019        | 6.6421 | 1800 | 0.0017          | 0.9988   |
| 0.0016        | 7.0111 | 1900 | 0.0015          | 1.0      |
| 0.002         | 7.3801 | 2000 | 0.0014          | 1.0      |
| 0.0013        | 7.7491 | 2100 | 0.0014          | 1.0      |
| 0.0015        | 8.1181 | 2200 | 0.0013          | 1.0      |
| 0.0011        | 8.4871 | 2300 | 0.0013          | 1.0      |
| 0.0013        | 8.8561 | 2400 | 0.0013          | 1.0      |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3