Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,186 @@
+---
+license: apache-2.0
+tags:
+- medical-imaging
+- polyp-detection
+- colonoscopy
+- segmentation
+- pytorch
+- ducknet
+- kvasir-seg
+task: image-segmentation
+widget:
+- src: https://example.com/colonoscopy-image.jpg
+  example_title: "Colonoscopy Image"
+---
+
+# 🦆 Duck-Net: Polyp Segmentation Model
+
+![Model Architecture](https://img.shields.io/badge/Architecture-DuckNet-blue)
+![Framework](https://img.shields.io/badge/Framework-PyTorch-red)
+![Task](https://img.shields.io/badge/Task-Medical%20Segmentation-green)
+![Dataset](https://img.shields.io/badge/Dataset-Kvasir--SEG-orange)
+
+## 🎯 Model Description
+
+This is a **Duck-Net** model fine-tuned for polyp segmentation in colonoscopy images. The model is based on a U-Net architecture with Duck-inspired multi-scale feature extraction blocks for superior medical image segmentation performance.
+
+### 📊 Model Performance
+
+- **Validation Dice Coefficient**: 92.88%
+- **Validation Jaccard Index**: 48.92%
+- **Dataset**: Kvasir-SEG (1000 colonoscopy images)
+- **Training Time**: ~83 minutes on GPU
+- **Model Size**: ~29.6 MB
+
+## 🚀 Quick Start
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+import torch.nn as nn
+
+# Define the model architecture
+class DuckNet(nn.Module):
+    def __init__(self, img_size=(256, 256), num_classes=3):
+        super(DuckNet, self).__init__()
+        # ... (model definition)
+        
+    def forward(self, x):
+        # ... (forward pass)
+        return torch.sigmoid(output)
+
+# Download and load model
+model_path = hf_hub_download(
+    repo_id="ibrahim313/ducknet-polyp-segmentation",
+    filename="pytorch_model.bin"
+)
+
+model = DuckNet(img_size=(256, 256), num_classes=3)
+model.load_state_dict(torch.load(model_path, map_location='cpu'))
+model.eval()
+
+# Inference
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+transform = A.Compose([
+    A.Resize(256, 256),
+    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ToTensorV2()
+])
+
+# Process image
+transformed = transform(image=your_image)
+input_tensor = transformed['image'].unsqueeze(0)
+
+with torch.no_grad():
+    prediction = model(input_tensor)
+    binary_mask = (prediction > 0.5).float()
+```
+
+## 🏗️ Model Architecture
+
+This Duck-Net implementation features:
+
+1. **Encoder-Decoder Structure**: U-Net based architecture
+2. **Multi-scale Features**: Duck-inspired feature extraction
+3. **Skip Connections**: Direct feature transfer between encoder and decoder
+4. **Batch Normalization**: Stable training and inference
+5. **Sigmoid Activation**: Multi-class segmentation output
+
+### Key Features
+
+- **Input Size**: 256×256×3 (RGB images)
+- **Output**: 256×256×3 (Multi-class segmentation mask)
+- **Parameters**: ~7,766,051
+- **Architecture**: Duck-Net (U-Net variant)
+
+## 📋 Training Details
+
+### Dataset
+- **Source**: Kvasir-SEG dataset
+- **Total Images**: 1000 colonoscopy images with polyp annotations
+- **Split**: 800 training, 100 validation, 100 test
+- **Classes**: Background, Polyp, Other tissue
+
+### Training Configuration
+- **Optimizer**: Adam (lr=0.001)
+- **Loss Function**: Jaccard Coefficient Loss
+- **Batch Size**: 8
+- **Epochs**: 50
+- **Hardware**: Tesla P100 GPU
+- **Framework**: Originally TensorFlow/Keras, converted to PyTorch
+
+## 💻 Usage Example
+
+```python
+import cv2
+import numpy as np
+from PIL import Image
+
+def predict_polyp(image_path, model, threshold=0.5):
+    # Load image
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # Preprocess
+    transformed = transform(image=image)
+    input_tensor = transformed['image'].unsqueeze(0)
+    
+    # Predict
+    model.eval()
+    with torch.no_grad():
+        prediction = model(input_tensor)
+        binary_mask = (prediction > threshold).float()
+    
+    return binary_mask.squeeze().numpy()
+```
+
+## ⚠️ Medical Disclaimer
+
+**Important**: This model is for research and educational purposes only. It should not be used for clinical diagnosis or treatment decisions. Always consult qualified medical professionals for clinical applications.
+
+## 📚 Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{ducknet_polyp_2024,
+  title={Duck-Net for Polyp Segmentation in Colonoscopy Images},
+  author={Ibrahim313},
+  year={2024},
+  howpublished={Hugging Face Model Hub},
+  url={https://huggingface.co/ibrahim313/ducknet-polyp-segmentation}
+}
+```
+
+## 👥 Model Card Authors
+
+- **Developed by**: ibrahim313
+- **Model Type**: Convolutional Neural Network (Duck-Net)
+- **License**: Apache 2.0
+- **Repository**: https://huggingface.co/ibrahim313/ducknet-polyp-segmentation
+
+## 🔧 Technical Specifications
+
+| Specification | Value |
+|---------------|-------|
+| Input Resolution | 256×256 |
+| Input Channels | 3 (RGB) |
+| Output Channels | 3 (Multi-class) |
+| Model Size | ~29.6 MB |
+| Parameters | 7,766,051 |
+| Inference Time | <1 second (CPU) |
+| Memory Usage | ~2GB (inference) |
+
+## 🏥 Applications
+
+- Colonoscopy image analysis
+- Polyp detection and segmentation
+- Medical imaging research
+- Computer-aided diagnosis (research only)
+
+## 🤝 Contact
+
+For questions or issues, please open an issue in the repository or contact the model author.