Update README.md

README.md

@@ -11,4 +11,87 @@ library_name: transformers
 tags:
 - medical
 - biology
----
+---
+
+
+# Chest X-ray Image Classifier
+
+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 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](https://stanfordmlgroup.github.io/chexpert/).
+
+## 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 transformers import ViTForImageClassification, ViTFeatureExtractor
+import torch
+from PIL import Image
+
+# Load model and feature extractor
+model = ViTForImageClassification.from_pretrained('codewithdark/chest-xray-classifier')
+feature_extractor = ViTFeatureExtractor.from_pretrained('codewithdark/chest-xray-classifier')
+
+# Prepare an image for prediction
+image = Image.open('path_to_chest_xray_image.jpg')
+
+# Preprocess the image and make predictions
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+predictions = torch.sigmoid(logits).squeeze()
+
+# Display predictions
+print(predictions)
+```
+
+### 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](LICENSE) for more details.
+
+## Acknowledgements
+
+- [CheXpert Dataset](https://stanfordmlgroup.github.io/chexpert/)
+- Hugging Face for providing the `transformers` library and Model Hub.
+
+---
+Happy coding! 🚀
+