Overview
Developed a deep learning model using PyTorch for human segmentation in images, focusing on accurately distinguishing human figures from the background in diverse scenarios. The project demonstrates advanced computer vision techniques for semantic segmentation tasks.
Key Features
- Advanced Architecture: Implemented a U-Net based architecture with a pre-trained EfficientNet encoder for robust feature extraction
- Data Augmentation: Utilized sophisticated data augmentation techniques to improve model generalization across various scenarios
- High Accuracy: Achieved state-of-the-art performance on human segmentation benchmarks
- Efficient Inference: Optimized model for real-time performance on consumer hardware
Technical Implementation
Model Architecture
- Backbone: Pre-trained EfficientNet as the encoder
- Decoder: Custom U-Net style decoder with skip connections
- Loss Function: Combined Dice Loss and Binary Cross-Entropy for optimal training
Data Pipeline
- Augmentation: Random crops, flips, color jitter, and elastic transformations
- Preprocessing: Normalization using ImageNet statistics
- Batch Processing: Custom data loader for efficient memory usage
Results
- Achieved IoU (Intersection over Union) of 0.92 on test dataset
- Real-time inference at 30 FPS on NVIDIA T4 GPU
- Robust performance across various lighting conditions and poses
Code Repository
Explore the implementation on GitHub:
git clone https://github.com/ramkrishs/deepsegmentation-humanfigures.git
cd deepsegmentation-humanfigures
pip install -r requirements.txt
Future Enhancements
- Port to ONNX/TensorRT for further optimization
- Add support for video segmentation
- Implement a web demo using Gradio or Streamlit
- Extend to multi-person segmentation in crowded scenes
Technologies Used
PyTorchEfficientNetU-NetPython 3.9