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This repo is the mindspore version of READ, plz jump to https://git.openi.org.cn/OpenI/READ_pytorch for the pytorch version.
READ is an open source toolbox focused on unsupervised anomaly detection/localization tasks. By only training on the defect-free samples, READ is able to recognize defect samples or even localize anomalies on defect samples.
The purpose of this repo is to promote the research and application of unsupervised anomaly detection and localization algorithms. READ is designed to provide:
In addition, READ provides the benchmarks for validating novel unsupervised anomaly detection and localization algorithms for MVTec AD dataset.
pip install -U git+https://git.openi.org.cn/OpenI/READ_mindspore
Please follow the Installation document to get a detailed instruction.
Please follow the Getting Started document to run the provided demo tasks.
MVTec | SPADE-R50 | PaDiM-R50 | STPM-R18 | RIAD-R18 | PatchCore-R18 |
---|---|---|---|---|---|
Carpet | 0.908 | 0.998 | 0.970 | 0.622 | 0.852 |
Grid | 0.783 | 0.927 | 0.944 | 0.938 | 0.546 |
Leather | 0.980 | 1.0 | 0.999 | 0.990 | 0.945 |
Tile | 0.939 | 0.986 | 0.981 | 0.657 | 0.891 |
Wood | 0.985 | 0.998 | 0.990 | 0.899 | 0.984 |
All texture classes | 0.919 | 0.982 | 0.977 | 0.821 | 0.844 |
Bottle | 0.999 | 1.0 | 1.0 | 0.985 | 1.0 |
Cable | 0.795 | 0.978 | 0.921 | 0.719 | 0.923 |
Capsule | 0.897 | 0.902 | 0.777 | 0.648 | 0.919 |
Hazelnut | 1.0 | 0.945 | 0.998 | 0.746 | 0.999 |
Metal nut | 0.980 | 0.987 | 0.998 | 0.806 | 0.983 |
Pill | 0.855 | 0.947 | 0.942 | 0.732 | 0.953 |
Screw | 0.929 | 0.844 | 0.774 | 0.452 | 0.885 |
Toothbrush | 0.958 | 0.964 | 0.922 | 0.467 | 1.0 |
Transistor | 0.868 | 0.993 | 0.907 | 0.847 | 0.989 |
Zipper | 0.870 | 0.859 | 0.949 | 0.957 | 0.930 |
All object classes | 0.915 | 0.942 | 0.919 | 0.736 | 0.997 |
All classes | 0.916 | 0.955 | 0.938 | 0.764 | 0.946 |
MVTec | SPADE-R50 | PaDiM-R50 | STPM-R18 | RIAD-R18 | Patchcore-R18 |
---|---|---|---|---|---|
Carpet | 0.985 | 0.982 | 0.988 | 0.910 | 0.972 |
Grid | 0.976 | 0.958 | 0.975 | 0.978 | 0.892 |
Leather | 0.992 | 0.987 | 0.992 | 0.983 | 0.984 |
Tile | 0.938 | 0.936 | 0.962 | 0.744 | 0.914 |
Wood | 0.955 | 0.942 | 0.957 | 0.797 | 0.908 |
All texture classes | 0.969 | 0.961 | 0.975 | 0.882 | 0.934 |
Bottle | 0.975 | 0.980 | 0.983 | 0.976 | 0.971 |
Cable | 0.944 | 0.980 | 0.944 | 0.840 | 0.973 |
Capsule | 0.988 | 0.984 | 0.966 | 0.876 | 0.977 |
Hazelnut | 0.992 | 0.981 | 0.988 | 0.928 | 0.981 |
Metal nut | 0.979 | 0.965 | 0.946 | 0.767 | 0.965 |
Pill | 0.954 | 0.973 | 0.974 | 0.833 | 0.980 |
Screw | 0.993 | 0.984 | 0.975 | 0.767 | 0.985 |
Toothbrush | 0.991 | 0.987 | 0.988 | 0.853 | 0.983 |
Transistor | 0.839 | 0.981 | 0.808 | 0.973 | 0.973 |
Zipper | 0.978 | 0.978 | 0.984 | 0.958 | 0.973 |
All object classes | 0.963 | 0.979 | 0.956 | 0.877 | 0.976 |
All classes | 0.965 | 0.973 | 0.962 | 0.879 | 0.962 |
This project is released under the Open-Intelligence Open Source License V1.1.
Please contact me if there is any question (Chao Zhang chao.zhang46@tcl.com).
Machine Vision Group, TCL Corporate Research(HK) Co., Ltd is the main developer of READ.
基于TCL在智能制造上缺陷检测的成功经验,TCL集团工业研究院开源了第一个工业视觉无监督异常检测框架,具有算法丰富、开箱即用、精度保证等特点。
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