README.md

metadata

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

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).

Source Code

GitHub Repository

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 scaffold.