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S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with modifications tailored for face detection tasks. It includes training scripts, evaluation code, and pre-trained models that achieve strong results on popular benchmarks such as AFW, PASCAL Face, FDDB, and WIDER FACE. The framework is optimized for speed and accuracy, making it suitable for both academic research and practical applications in computer vision.
Features
- Real-time face detection with a single deep neural network
- Superior performance on detecting small-scale faces
- Based on the SSD framework with customized improvements
- Includes pre-trained models and benchmark evaluation scripts
- Supports AFW, PASCAL Face, FDDB, and WIDER FACE datasets
- Provides training scripts with data augmentation and anchor strategies
Project Samples
