PP-ShiTu: A Practical Lightweight Image Recognition System

TL;DR

PP-ShiTu is a practical, lightweight image recognition system that combines multiple modules and strategies like metric learning and model quantization to achieve high accuracy and fast inference across various domains.

Contribution

The paper introduces PP-ShiTu, a comprehensive lightweight image recognition system with integrated modules and strategies, demonstrating effectiveness across multiple real-world scenarios.

Findings

Effective across different image recognition domains

Achieves high accuracy with fast inference

Open-sourced models available for public use

Abstract

In recent years, image recognition applications have developed rapidly. A large number of studies and techniques have emerged in different fields, such as face recognition, pedestrian and vehicle re-identification, landmark retrieval, and product recognition. In this paper, we propose a practical lightweight image recognition system, named PP-ShiTu, consisting of the following 3 modules, mainbody detection, feature extraction and vector search. We introduce popular strategies including metric learning, deep hash, knowledge distillation and model quantization to improve accuracy and inference speed. With strategies above, PP-ShiTu works well in different scenarios with a set of models trained on a mixed dataset. Experiments on different datasets and benchmarks show that the system is widely effective in different domains of image recognition. All the above mentioned models are open-sourced and the code is available in the GitHub repository PaddleClas on PaddlePaddle.