PP-ShiTu: A Practical Lightweight Image Recognition System
This is an incremental improvement for developers needing efficient image recognition tools, as it combines existing techniques into a lightweight system.
The authors tackled the problem of building a practical lightweight image recognition system by proposing PP-ShiTu, which integrates modules like mainbody detection and feature extraction with strategies such as metric learning and model quantization, resulting in a system that works well across different scenarios and datasets.
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.