---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cxr
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9356966199505359
---

<!-- 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-patch16-224-in21k-finetuned-cxr

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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1758
- Accuracy: 0.9357

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2994        | 0.99  | 85   | 0.3337          | 0.8854   |
| 0.2806        | 2.0   | 171  | 0.2670          | 0.9101   |
| 0.2519        | 2.99  | 256  | 0.2495          | 0.9134   |
| 0.2456        | 4.0   | 342  | 0.2450          | 0.9143   |
| 0.2094        | 4.99  | 427  | 0.2105          | 0.9258   |
| 0.1808        | 6.0   | 513  | 0.1984          | 0.9308   |
| 0.1959        | 6.99  | 598  | 0.2022          | 0.9258   |
| 0.179         | 8.0   | 684  | 0.1980          | 0.9299   |
| 0.1915        | 8.99  | 769  | 0.1889          | 0.9308   |
| 0.1735        | 10.0  | 855  | 0.1931          | 0.9324   |
| 0.174         | 10.99 | 940  | 0.1872          | 0.9324   |
| 0.167         | 12.0  | 1026 | 0.1758          | 0.9357   |
| 0.1408        | 12.99 | 1111 | 0.1890          | 0.9349   |
| 0.1442        | 14.0  | 1197 | 0.1849          | 0.9324   |
| 0.1661        | 14.91 | 1275 | 0.1879          | 0.9266   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0