This repository contains a fine-tuned Vision Transformer (ViT) model for classifying chest X-ray images, utilizing the CheXpert dataset. The model is fine-tuned on the task of classifying various lung diseases from chest radiographs, achieving impressive accuracy in distinguishing between different conditions.

Model Overview

The fine-tuned Model Code on github is based on the Vision Transformer (ViT) architecture, which excels in handling image-based tasks by leveraging attention mechanisms for efficient feature extraction. The model was trained on the CheXpert dataset, which consists of labeled chest X-ray images for detecting diseases such as pneumonia, cardiomegaly, and others.

Performance

Final Validation Accuracy: 98.46%
Final Training Loss: 0.1069
Final Validation Loss: 0.0980

The model achieved a significant accuracy improvement during training, demonstrating its ability to generalize well to unseen chest X-ray images.

Dataset

The dataset used for fine-tuning the model is the CheXpert dataset, which includes chest X-ray images from various patients with multi-label annotations. The data includes frontal and lateral views of the chest for each patient, annotated with labels for various lung diseases.

For more details on the dataset, visit the CheXpert official website.

Training Details

The model was fine-tuned using the following settings:

Optimizer: AdamW
Learning Rate: 3e-5
Batch Size: 32
Epochs: 10
Loss Function: Binary Cross-Entropy with Logits
Precision: Mixed precision (via torch.amp)

Usage

Inference

To use the fine-tuned model for inference, simply load the model from Hugging Face's Model Hub and input a chest X-ray image:

python

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

# Load model and processor
processor = AutoImageProcessor.from_pretrained("codewithdark/vit-chest-xray")
model = AutoModelForImageClassification.from_pretrained("codewithdark/vit-chest-xray")

# Define label columns (class names)
label_columns = ['Cardiomegaly', 'Edema', 'Consolidation', 'Pneumonia', 'No Finding']

# Step 1: Load and preprocess the image
image_path = "/content/images.jpeg"  # Replace with your image path

# Open the image
image = Image.open(image_path)

# Ensure the image is in RGB mode (required by most image classification models)
if image.mode != 'RGB':
    image = image.convert('RGB')
    print("Image converted to RGB.")

# Step 2: Preprocess the image using the processor
inputs = processor(images=image, return_tensors="pt")

# Step 3: Make a prediction (using the model)
with torch.no_grad():  # Disable gradient computation during inference
    outputs = model(**inputs)

# Step 4: Extract logits and get the predicted class index
logits = outputs.logits  # Raw logits from the model
predicted_class_idx = torch.argmax(logits, dim=-1).item()  # Get the class index

# Step 5: Map the predicted index to a class label
# You can also use `model.config.id2label`, but we'll use `label_columns` for this task
predicted_class_label = label_columns[predicted_class_idx]

# Output the results
print(f"Predicted Class Index: {predicted_class_idx}")
print(f"Predicted Class Label: {predicted_class_label}")

'''
Output :
Predicted Class Index: 4
Predicted Class Label: No Finding
'''

Fine-Tuning

To fine-tune the model on your own dataset, you can follow the instructions in this repo to adapt the code to your dataset and training configuration.

Contributing

We welcome contributions! If you have suggestions, improvements, or bug fixes, feel free to fork the repository and open a pull request.

License

This model is available under the MIT License. See LICENSE for more details.

Acknowledgements

CheXpert Dataset
Hugging Face for providing the transformers library and Model Hub.

Happy coding! 🚀