---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- chest-xray-classification
metrics:
- accuracy
model-index:
- name: vit-pneumonia-classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: chest-xray-classification
      type: chest-xray-classification
      config: full
      split: validation
      args: full
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9560951680156978
---

<!-- 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-pneumonia-classification

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 chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1301
- Accuracy: 0.9561

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4786        | 1.0   | 32   | 0.3081          | 0.8609   |
| 0.213         | 2.0   | 64   | 0.1645          | 0.9399   |
| 0.1724        | 3.0   | 96   | 0.1419          | 0.9502   |
| 0.1438        | 4.0   | 128  | 0.0950          | 0.9734   |
| 0.1267        | 5.0   | 160  | 0.1225          | 0.9579   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0