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---
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
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