Lightweight and Efficient Model Design for Diverse Tasks.

This project tackles core tasks in real-world scenarios, including pedestrian, motor vehicle, and non-motor vehicle detection, classification, and tracking/re-identification. It enhances high-performance AI techniques such as quantization, knowledge distillation, pruning, and neural architecture search (NAS) to reduce server load.