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

PP-ShiTu: A Practical Lightweight Image Recognition System