---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vit-cxr4
  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-cxr4

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3774
- Precision: 0.8587
- Recall: 0.9317
- F1: 0.8937
- Accuracy: 0.8924

## 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: 3e-05
- train_batch_size: 96
- eval_batch_size: 64
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3151        | 0.31  | 100  | 0.3317          | 0.8152    | 0.9143 | 0.8619 | 0.8552   |
| 0.319         | 0.63  | 200  | 0.3048          | 0.8670    | 0.8514 | 0.8591 | 0.8620   |
| 0.2926        | 0.94  | 300  | 0.2867          | 0.8580    | 0.8662 | 0.8621 | 0.8631   |
| 0.1884        | 1.25  | 400  | 0.2635          | 0.8468    | 0.9381 | 0.8901 | 0.8856   |
| 0.234         | 1.57  | 500  | 0.2639          | 0.8232    | 0.9677 | 0.8896 | 0.8814   |
| 0.2349        | 1.88  | 600  | 0.2478          | 0.8530    | 0.9328 | 0.8911 | 0.8874   |
| 0.1476        | 2.19  | 700  | 0.2560          | 0.8584    | 0.9297 | 0.8926 | 0.8895   |
| 0.1289        | 2.51  | 800  | 0.2698          | 0.8809    | 0.8916 | 0.8862 | 0.8869   |
| 0.1579        | 2.82  | 900  | 0.2614          | 0.8879    | 0.8715 | 0.8796 | 0.8822   |
| 0.0745        | 3.13  | 1000 | 0.2783          | 0.8854    | 0.8905 | 0.8880 | 0.8889   |
| 0.0697        | 3.45  | 1100 | 0.2844          | 0.8893    | 0.8879 | 0.8886 | 0.8900   |
| 0.0602        | 3.76  | 1200 | 0.3213          | 0.8797    | 0.8932 | 0.8864 | 0.8869   |
| 0.0246        | 4.08  | 1300 | 0.3393          | 0.8753    | 0.9096 | 0.8921 | 0.8913   |
| 0.0301        | 4.39  | 1400 | 0.3593          | 0.8644    | 0.9307 | 0.8964 | 0.8937   |
| 0.0348        | 4.7   | 1500 | 0.3804          | 0.8653    | 0.9344 | 0.8986 | 0.8957   |
| 0.011         | 5.02  | 1600 | 0.3897          | 0.8622    | 0.9365 | 0.8978 | 0.8947   |
| 0.0077        | 5.33  | 1700 | 0.4088          | 0.8754    | 0.9180 | 0.8962 | 0.8950   |
| 0.0064        | 5.64  | 1800 | 0.4281          | 0.8780    | 0.9170 | 0.8971 | 0.8960   |
| 0.0031        | 5.96  | 1900 | 0.4289          | 0.8736    | 0.9207 | 0.8965 | 0.8950   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0