---
license: mit
library_name: transformers

pipeline_tag: image-classification

base_model: facebook/convnextv2-tiny-22k-224
tags:
  - chest-xray
  - convnext
  - convnextv2
  - image-classification
  - medical
  - medical-imaging
  - pneumonia
  - pytorch
  - transformers
datasets:
  - keremberke/chest-xray-classification

metrics:
  - accuracy
  - f1

widget:
  - src: https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/bacterial_pneumonia.png
    example_title: bacterial_pneumonia
  - src: https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/normal.png
    example_title: normal
  - src: https://huggingface.co/kiselyovd/chest-xray-classifier/resolve/main/samples/viral_pneumonia.png
    example_title: viral_pneumonia


model-index:
  - name: kiselyovd/chest-xray-classifier
    results:
      - task:
          type: image-classification
        dataset:
          type: keremberke/chest-xray-classification
          name: Chest X-Ray Images (Pneumonia)
        metrics:
          - type: auroc_macro_ovr
            value: 0.9752638346619307
          - type: accuracy
            value: 0.9134615384615384
          - type: macro_f1
            value: 0.9029730638714358

---

# chest-xray-classifier

Production-grade 3-class chest X-ray classifier: normal vs bacterial pneumonia vs viral pneumonia.

## Metrics

| Metric | Value |
|---|---|
| auroc_macro_ovr | 0.9752638346619307 |
| accuracy | 0.9134615384615384 |
| macro_f1 | 0.9029730638714358 |

## Usage


```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

processor = AutoImageProcessor.from_pretrained("kiselyovd/chest-xray-classifier")
model = AutoModelForImageClassification.from_pretrained("kiselyovd/chest-xray-classifier")

image = Image.open("your_image.png")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class = logits.argmax(-1).item()
print(model.config.id2label[predicted_class])
```


## Training Data

Trained on [Chest X-Ray Images (Pneumonia)](https://huggingface.co/datasets/keremberke/chest-xray-classification).


## Source Code

[GitHub Repository](https://github.com/kiselyovd/chest-xray-classifier)

## Intended Use

This model is provided for research and educational purposes. The authors make no warranties about its suitability for any particular application. Users are responsible for evaluating the model's fitness for their use case, including fairness, safety, and compliance with applicable regulations.

> **Note:** This model card was generated from the [ml-project-template](https://github.com/kiselyovd/ml-project-template) scaffold.