README.md

---
tags:
- autotrain
- image-classification
- vision
datasets:
- CXR-Classifier/autotrain-data
license: apache-2.0
pipeline_tag: image-classification
---

# CXR-Classifier(Small-size model)

It is a fine tuned version of [Vision Transformer(base-sized model)](https://huggingface.co/google/vit-base-patch16-224). It was trained on a private dataset.

## Model description

The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.

By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.

It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him

## Validation Metrics
loss: 0.1180819422006607

f1: 0.9755686604886269

precision: 0.9780405405405406

recall: 0.973109243697479

auc: 0.9916270580630442

accuracy: 0.9644607843137255

## How to use
Here is how to use this model to identify a pneumonia from a chest x-ray image.

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

processor = AutoImageProcessor.from_pretrained("NeuronZero/CXR-Classifier")
model = AutoModelForImageClassification.from_pretrained("NeuronZero/CXR-Classifier")

#dataset URL: "https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia

image_url = "https://storage.googleapis.com/kagglesdsdata/datasets/17810/23812/chest_xray/test/PNEUMONIA/person100_bacteria_482.jpeg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240406%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240406T182943Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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"
image = Image.open(requests.get(image_url, stream=True).raw)

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```