---
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.