---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-chest-xray
  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-base-chest-xray

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the trpakov/chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0856
- Accuracy: 0.9742

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1891        | 0.1307 | 100  | 0.1028          | 0.9665   |
| 0.2123        | 0.2614 | 200  | 0.1254          | 0.9562   |
| 0.0536        | 0.3922 | 300  | 0.1142          | 0.9691   |
| 0.0799        | 0.5229 | 400  | 0.1173          | 0.9648   |
| 0.0537        | 0.6536 | 500  | 0.0856          | 0.9742   |
| 0.0911        | 0.7843 | 600  | 0.2005          | 0.9425   |
| 0.1027        | 0.9150 | 700  | 0.0869          | 0.9708   |
| 0.1011        | 1.0458 | 800  | 0.1063          | 0.9631   |
| 0.0717        | 1.1765 | 900  | 0.1424          | 0.9588   |
| 0.0605        | 1.3072 | 1000 | 0.1525          | 0.9648   |
| 0.0573        | 1.4379 | 1100 | 0.0970          | 0.9700   |
| 0.024         | 1.5686 | 1200 | 0.0867          | 0.9751   |
| 0.0056        | 1.6993 | 1300 | 0.0888          | 0.9760   |
| 0.0051        | 1.8301 | 1400 | 0.1054          | 0.9768   |
| 0.063         | 1.9608 | 1500 | 0.1896          | 0.9571   |
| 0.002         | 2.0915 | 1600 | 0.1886          | 0.9588   |
| 0.005         | 2.2222 | 1700 | 0.1184          | 0.9734   |
| 0.0083        | 2.3529 | 1800 | 0.1084          | 0.9760   |
| 0.0013        | 2.4837 | 1900 | 0.0903          | 0.9777   |
| 0.0298        | 2.6144 | 2000 | 0.1023          | 0.9734   |
| 0.0008        | 2.7451 | 2100 | 0.1104          | 0.9768   |
| 0.0011        | 2.8758 | 2200 | 0.1128          | 0.9785   |
| 0.0006        | 3.0065 | 2300 | 0.1395          | 0.9734   |
| 0.0059        | 3.1373 | 2400 | 0.1419          | 0.9725   |
| 0.0005        | 3.2680 | 2500 | 0.1335          | 0.9777   |
| 0.0005        | 3.3987 | 2600 | 0.1249          | 0.9768   |
| 0.0007        | 3.5294 | 2700 | 0.1157          | 0.9777   |
| 0.0005        | 3.6601 | 2800 | 0.1202          | 0.9785   |
| 0.001         | 3.7908 | 2900 | 0.1239          | 0.9777   |
| 0.0004        | 3.9216 | 3000 | 0.1231          | 0.9768   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1