PP-ShiTu: A Practical Lightweight Image Recognition System
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 li...
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Main Authors | , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
01.11.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2111.00775 |