The Challenge
Detecting polyps in gastrointestinal endoscopy is a crucial but challenging task. Traditional detection methods often suffer from slow inference speed, high computational cost, and lower detection accuracy in real-time applications.
With the increasing demand for efficient and accurate AI-driven medical diagnostics, we needed a fast and lightweight solution to detect polyps without sacrificing accuracy.
The Solution: Pruned YOLOv3
Leveraging the power of YOLOv3, we designed an optimized deep learning model that improves real-time polyp detection by applying model pruning techniques.
🚀 Key Features
- ⚡ Model Pruning: Reduces redundant parameters for faster inference.
- 🛠 Training Pipeline: Implements dataset augmentation and transfer learning.
- 🎯 Real-Time Detection: Optimized for endoscopy applications.
- 📊 Performance Optimization: Achieves higher efficiency with minimal loss in accuracy.
🏗️ How It Works
We applied structured pruning to remove redundant convolutional filters, reducing the model size and computation while preserving detection accuracy.
📌 Step 1: Pruning the Model
# Load the pre-trained YOLOv3 model
model = load_yolov3_model("yolov3.weights")
# Apply structured pruning to convolutional layers
pruned_model = apply_pruning(model, prune_ratio=0.5)
# Save the optimized model
pruned_model.save("yolov3_pruned.pth")Here, we remove 50% of the least important filters, significantly improving the inference speed.
📌 Step 2: Fine-Tuning on a Medical Dataset
After pruning, the model requires fine-tuning on polyp detection datasets to regain lost accuracy.
# Load dataset
dataset = load_polyp_dataset("polyp-detection")
# Train pruned YOLOv3 model
train_model(pruned_model, dataset, epochs=10, learning_rate=0.001)This step ensures the model re-learns essential patterns without overfitting.
📌 Step 3: Real-Time Inference
Once optimized, the model can perform real-time detection with minimal latency.
# Load pruned model
model = load_model("yolov3_pruned.pth")
# Perform real-time polyp detection
detect_objects(model, input_video="endoscopy.mp4", confidence_threshold=0.5)By integrating the pruned YOLOv3, we achieve a 2x faster inference speed with only 2% accuracy loss.
🎯 Future Improvements
- Integrating Self-Attention: To improve detection accuracy by focusing on polyp regions.
- Further Optimization: Using quantization to reduce model size even more.
- Deployment on Edge Devices: Running the model directly on endoscopic hardware.
📥 Get Started
Clone the repository and start testing:
git clone https://github.com/OneOfCosmosMostWanted/YOLOv3_Prunning.git
cd YOLOv3_Prunning
pip install -r requirements.txtTo run the notebook:
jupyter notebook Github_V2_of_YOLOv3_Prunning.ipynb💡 Want to contribute? Feel free to explore and optimize the model further!