Rename README.md to Update README.md

README.md

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----
-license: apache-2.0
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- microsoft/resnet-50
-- timm/vgg19.tv_in1k
-- google/vit-base-patch16-224
-- xai-org/grok-1
-pipeline_tag: image-classification
-tags:
-- Ocular-Toxoplasmosis(FundusImages)
-- Retinal-images(Diabetics,Cataract,Gulocoma,Healthy)
-- Pytorch
-- Transformers
-- Image-Classification
-- Image_feature_extraction
-- Grad-CAM
-- XAI-Visualization
----

Update README.md

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+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- microsoft/resnet-50
+- timm/vgg19.tv_in1k
+- google/vit-base-patch16-224
+- xai-org/grok-1
+pipeline_tag: image-classification
+tags:
+- Ocular-Toxoplasmosis(FundusImages)
+- Retinal-images(Diabetics,Cataract,Gulocoma,Healthy)
+- Pytorch
+- Transformers
+- Image-Classification
+- Image_feature_extraction
+- Grad-CAM
+- XAI-Visualization
+---
+
+# Model Card: ROYXAI [Vision Transformer + VGG19 + ResNet50 Ensemble with Grad-CAM]
+
+## Model Description
+This model is an ensemble of three deep learning architectures: **Vision Transformer (ViT), VGG19, and ResNet50**. The ensemble approach enhances classification performance on medical image datasets related to ocular diseases. The model also integrates **Grad-CAM** visualization to highlight regions of interest for better interpretability.
+
+## Model Details
+- **Model Name**: ROYXAI
+- **Developed by**: Avishek Roy Sparsho
+- **Framework**: PyTorch
+- **Ensemble Method**: Bagging
+- **Backbone Models**: Vision Transformer, VGG19, ResNet50
+- **Target Task**: Medical Image Classification
+- **Supported Classes**:
+  - OT
+  - Healthy
+  - SC_diabetes
+  - SC_cataract
+  - SC_glucoma
+
+## Dataset
+- **Dataset Name**: Custom Ocular Disease and its Secondary complications Dataset
+- **Dataset Source**: Private Dataset (Medical Images)
+- **Dataset Structure**: Images stored in folders based on class labels
+- **Preprocessing**:
+  - Resized images to 224x224 pixels
+  - Normalized using ImageNet mean and standard deviation
+
+## Model Performance
+- **Accuracy**: 98% on the test dataset
+- **Precision/Recall/F1-score**: Evaluated and optimized for medical diagnosis
+- **Overfitting Prevention**: Implemented **data augmentation, dropout, weight regularization**
+
+## Installation and Usage
+### Clone the Repository
+```bash
+git clone https://huggingface.co/Aviroy/ROYXAI
+cd ROYXAI
+```
+
+### Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### Training the Model
+To train the model from scratch, run:
+```bash
+python train.py --epochs 50 --batch_size 32
+```
+
+### Load Pretrained Model
+To directly use the trained model:
+```python
+import torch
+from PIL import Image
+import torchvision.transforms as transforms
+from model import ensemble_model  # Load the trained ensemble model
+
+# Define image transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load and preprocess an image
+image_path = "path/to/image.jpg"
+image = Image.open(image_path).convert('RGB')
+image = transform(image).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Perform inference
+ensemble_model.eval()
+with torch.no_grad():
+    output = ensemble_model(image)
+    predicted_class = torch.argmax(output, dim=1).item()
+
+# Print classification result
+print("Predicted Class:", predicted_class)
+```
+
+## Grad-CAM Visualization
+### Visualizing Attention Maps for Interpretability
+#### Vision Transformer (ViT)
+```python
+from visualization import visualize_gradcam_vit  # Function for ViT Grad-CAM
+
+# Generate Grad-CAM visualization
+overlay = visualize_gradcam_vit(ensemble_model.models[0], image, target_class=predicted_class)
+
+# Display the Grad-CAM output
+import matplotlib.pyplot as plt
+plt.imshow(overlay)
+plt.axis('off')
+plt.title("Grad-CAM for Vision Transformer")
+plt.show()
+```
+
+#### ResNet50
+```python
+from visualization import visualize_gradcam  # General Grad-CAM function
+
+# Generate Grad-CAM visualization for ResNet50
+overlay = visualize_gradcam(ensemble_model.models[2], image, target_class=predicted_class)
+
+# Display the Grad-CAM output
+import matplotlib.pyplot as plt
+plt.imshow(overlay)
+plt.axis('off')
+plt.title("Grad-CAM for ResNet50")
+plt.show()
+```
+
+#### VGG19
+```python
+from visualization import visualize_gradcam  # General Grad-CAM function
+
+# Generate Grad-CAM visualization for VGG19
+overlay = visualize_gradcam(ensemble_model.models[1], image, target_class=predicted_class)
+
+# Display the Grad-CAM output
+import matplotlib.pyplot as plt
+plt.imshow(overlay)
+plt.axis('off')
+plt.title("Grad-CAM for VGG19")
+plt.show()
+```
+
+## Training Configuration
+- **Optimizer**: Adam with weight decay
+- **Learning Rate Scheduler**: Cosine Annealing LR
+- **Loss Function**: Cross-Entropy Loss
+- **Batch Size**: 32
+- **Training Epochs**: 20
+- **Hardware Used**: T4 GPU x2 ,M1chip ,GPU P100
+
+## Limitations & Considerations
+- This model is trained on a specific dataset and may not generalize well to other medical image datasets without fine-tuning.
+- It is **not a substitute for professional medical diagnosis**.
+- The Vision Transformer model is computationally expensive compared to CNNs.
+
+## Citation
+If you use this model in your research, please cite:
+```
+@article{Sparsho2025,
+  author    = {Avishek Roy Sparsho},
+  title     = {ROYXAI Model For Proper Visualization of Classified Medical Image},
+  journal   = {Medical AI Research},
+  year      = {2025}
+}
+```
+
+## Acknowledgments
+Special thanks to the open-source community and Kaggle for providing medical datasets for deep learning research.
+
+## License
+This model is released under the **Apache 2.0 License**. Use it responsibly.
+
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