README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- chest-xray-classification
- keremberke/chest-xray-classification
metrics:
- accuracy
model-index:
- name: vit-xray-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: 0.9742489270386266
pipeline_tag: image-classification
---

<!-- 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-xray-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: 0.0868
- Accuracy: 0.9742

## Inference example

```python
from transformers import pipeline

classifier = pipeline(model="lxyuan/vit-xray-pneumonia-classification")

# image taken from https://www.news-medical.net/health/What-is-Viral-Pneumonia.aspx
classifier("https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20200618040600/ri/650/picture/2020/6/shutterstock_786937069.jpg")

>>>
[{'score': 0.990334689617157, 'label': 'PNEUMONIA'},
 {'score': 0.009665317833423615, 'label': 'NORMAL'}]

```

## Training procedure

Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/ViT-xray-classification.ipynb)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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

```python
from transformers import EarlyStoppingCallback

training_args = TrainingArguments(
    output_dir="vit-xray-pneumonia-classification",
    remove_unused_columns=False,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=16,
    gradient_accumulation_steps=4,
    per_device_eval_batch_size=16,
    num_train_epochs=15,
    save_total_limit=2,
    warmup_ratio=0.1,
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
    fp16=True,
    push_to_hub=True,
    report_to="tensorboard"
)

early_stopping = EarlyStoppingCallback(early_stopping_patience=3)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    tokenizer=processor,
    compute_metrics=compute_metrics,
    callbacks=[early_stopping],
)
```

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5152        | 0.99  | 63   | 0.2507          | 0.9245   |
| 0.2334        | 1.99  | 127  | 0.1766          | 0.9382   |
| 0.1647        | 3.0   | 191  | 0.1218          | 0.9588   |
| 0.144         | 4.0   | 255  | 0.1222          | 0.9502   |
| 0.1348        | 4.99  | 318  | 0.1293          | 0.9571   |
| 0.1276        | 5.99  | 382  | 0.1000          | 0.9665   |
| 0.1175        | 7.0   | 446  | 0.1177          | 0.9502   |
| 0.109         | 8.0   | 510  | 0.1079          | 0.9665   |
| 0.0914        | 8.99  | 573  | 0.0804          | 0.9717   |
| 0.0872        | 9.99  | 637  | 0.0800          | 0.9717   |
| 0.0804        | 11.0  | 701  | 0.0862          | 0.9682   |
| 0.0935        | 12.0  | 765  | 0.0883          | 0.9657   |
| 0.0686        | 12.99 | 828  | 0.0868          | 0.9742   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.9.0+cu102
- Datasets 2.12.0
- Tokenizers 0.13.3