CXR-Classifier(Small-size model)

It is a fine tuned version of Vision Transformer(base-sized model). 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 by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository 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.

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])