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source link: https://github.com/openvinotoolkit/anomalib
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A library for benchmarking, developing and deploying deep learning anomaly detection algorithms
Introduction
Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on image-based anomaly detection, where the goal of the algorithm is to identify anomalous images, or anomalous pixel regions within images in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!
Key features:
- The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.
- PyTorch Lightning based model implementations to reduce boilerplate code and limit the implementation efforts to the bare essentials.
- All models can be exported to OpenVINO Intermediate Representation (IR) for accelerated inference on intel hardware.
- A set of inference tools for quick and easy deployment of the standard or custom anomaly detection models.
Getting Started
To get an overview of all the devices where anomalib
as been tested thoroughly, look at the Supported Hardware section in the documentation.
PyPI Install
You can get started with anomalib
by just using pip.
pip install anomalib
Local Install
It is highly recommended to use virtual environment when installing anomalib. For instance, with anaconda, anomalib
could be installed as,
yes | conda create -n anomalib_env python=3.8 conda activate anomalib_env git clone https://github.com/openvinotoolkit/anomalib.git cd anomalib pip install -e .
Training
By default python tools/train.py
runs PADIM model MVTec leather
dataset.
python tools/train.py # Train PADIM on MVTec leather
Training a model on a specific dataset and category requires further configuration. Each model has its own configuration
file, config.yaml
, which contains data, model and training configurable parameters. To train a specific model on a specific dataset and
category, the config file is to be provided:
python tools/train.py --model_config_path <path/to/model/config.yaml>
For example, to train PADIM you can use
python tools/train.py --model_config_path anomalib/models/padim/config.yaml
Alternatively, a model name could also be provided as an argument, where the scripts automatically finds the corresponding config file.
python tools/train.py --model padim
where the currently available models are:
Inference
Anomalib contains several tools that can be used to perform inference with a trained model. The script in tools/inference
contains an example of how the inference tools can be used to generate a prediction for an input image.
If the specified weight path points to a PyTorch Lightning checkpoint file (.ckpt
), inference will run in PyTorch. If the path points to an ONNX graph (.onnx
) or OpenVINO IR (.bin
or .xml
), inference will run in OpenVINO.
The following command can be used to run inference from the command line:
python tools/inference.py \ --model_config_path <path/to/model/config.yaml> \ --weight_path <path/to/weight/file> \ --image_path <path/to/image>
As a quick example:
python tools/inference.py \ --model_config_path anomalib/models/padim/config.yaml \ --weight_path results/padim/mvtec/bottle/weights/model.ckpt \ --image_path datasets/MVTec/bottle/test/broken_large/000.png
If you want to run OpenVINO model, ensure that compression
apply
is set to True
in the respective model config.yaml
.
optimization: compression: apply: true
Example OpenVINO Inference:
python tools/inference.py \ --model_config_path \ anomalib/models/padim/config.yaml \ --weight_path \ results/padim/mvtec/bottle/compressed/compressed_model.xml \ --image_path \ datasets/MVTec/bottle/test/broken_large/000.png \ --meta_data \ results/padim/mvtec/bottle/compressed/meta_data.json
Ensure that you provide path to
meta_data.json
if you want the normalization to be applied correctly.
Datasets
MVTec Dataset
Image-Level AUC
Model
Avg Carpet Grid Leather Tile Wood Bottle Cable Capsule Hazelnut Metal Nut Pill Screw Toothbrush Transistor Zipper
PatchCore Wide ResNet-50 0.980 0.984 0.959 1.000 1.000 0.989 1.000 0.990 0.982 1.000 0.994 0.924 0.960 0.933 1.000 0.982
PatchCore ResNet-18 0.973 0.970 0.947 1.000 0.997 0.997 1.000 0.986 0.965 1.000 0.991 0.916 0.943 0.931 0.996 0.953
CFlow Wide ResNet-50 0.962 0.986 0.962 1.0 0.999 0.993 1.0 0.893 0.945 1.0 0.995 0.924 0.908 0.897 0.943 0.984
PaDiM Wide ResNet-50 0.950 0.995 0.942 1.0 0.974 0.993 0.999 0.878 0.927 0.964 0.989 0.939 0.845 0.942 0.976 0.882
PaDiM ResNet-18 0.891 0.945 0.857 0.982 0.950 0.976 0.994 0.844 0.901 0.750 0.961 0.863 0.759 0.889 0.920 0.780
STFPM Wide ResNet-50 0.876 0.957 0.977 0.981 0.976 0.939 0.987 0.878 0.732 0.995 0.973 0.652 0.825 0.5 0.875 0.899
STFPM ResNet-18 0.893 0.954 0.982 0.989 0.949 0.961 0.979 0.838 0.759 0.999 0.956 0.705 0.835 0.997 0.853 0.645
DFM Wide ResNet-50 0.891 0.978 0.540 0.979 0.977 0.974 0.990 0.891 0.931 0.947 0.839 0.809 0.700 0.911 0.915 0.981
DFM ResNet-18 0.894 0.864 0.558 0.945 0.984 0.946 0.994 0.913 0.871 0.979 0.941 0.838 0.761 0.95 0.911 0.949
DFKDE Wide ResNet-50 0.774 0.708 0.422 0.905 0.959 0.903 0.936 0.746 0.853 0.736 0.687 0.749 0.574 0.697 0.843 0.892
DFKDE ResNet-18 0.762 0.646 0.577 0.669 0.965 0.863 0.951 0.751 0.698 0.806 0.729 0.607 0.694 0.767 0.839 0.866
Pixel-Level AUC
Model
Avg Carpet Grid Leather Tile Wood Bottle Cable Capsule Hazelnut Metal Nut Pill Screw Toothbrush Transistor Zipper
PatchCore Wide ResNet-50 0.980 0.988 0.968 0.991 0.961 0.934 0.984 0.988 0.988 0.987 0.989 0.980 0.989 0.988 0.981 0.983
PatchCore ResNet-18 0.976 0.986 0.955 0.990 0.943 0.933 0.981 0.984 0.986 0.986 0.986 0.974 0.991 0.988 0.974 0.983
CFlow Wide ResNet-50 0.971 0.986 0.968 0.993 0.968 0.924 0.981 0.955 0.988 0.990 0.982 0.983 0.979 0.985 0.897 0.980
PaDiM Wide ResNet-50 0.979 0.991 0.970 0.993 0.955 0.957 0.985 0.970 0.988 0.985 0.982 0.966 0.988 0.991 0.976 0.986
PaDiM ResNet-18 0.968 0.984 0.918 0.994 0.934 0.947 0.983 0.965 0.984 0.978 0.970 0.957 0.978 0.988 0.968 0.979
STFPM Wide ResNet-50 0.903 0.987 0.989 0.980 0.966 0.956 0.966 0.913 0.956 0.974 0.961 0.946 0.988 0.178 0.807 0.980
STFPM ResNet-18 0.951 0.986 0.988 0.991 0.946 0.949 0.971 0.898 0.962 0.981 0.942 0.878 0.983 0.983 0.838 0.972
Image F1 Score
Model
Avg Carpet Grid Leather Tile Wood Bottle Cable Capsule Hazelnut Metal Nut Pill Screw Toothbrush Transistor Zipper
PatchCore Wide ResNet-50 0.976 0.971 0.974 1.000 1.000 0.967 1.000 0.968 0.982 1.000 0.984 0.940 0.943 0.938 1.000 0.979
PatchCore ResNet-18 0.970 0.949 0.946 1.000 0.98 0.992 1.000 0.978 0.969 1.000 0.989 0.940 0.932 0.935 0.974 0.967
CFlow Wide ResNet-50 0.944 0.972 0.932 1.0 0.988 0.967 1.0 0.832 0.939 1.0 0.979 0.924 0.971 0.870 0.818 0.967
PaDiM Wide ResNet-50 0.951 0.989 0.930 1.0 0.960 0.983 0.992 0.856 0.982 0.937 0.978 0.946 0.895 0.952 0.914 0.947
PaDiM ResNet-18 0.916 0.930 0.893 0.984 0.934 0.952 0.976 0.858 0.960 0.836 0.974 0.932 0.879 0.923 0.796 0.915
STFPM Wide ResNet-50 0.926 0.973 0.973 0.974 0.965 0.929 0.976 0.853 0.920 0.972 0.974 0.922 0.884 0.833 0.815 0.931
STFPM ResNet-18 0.932 0.961 0.982 0.989 0.930 0.951 0.984 0.819 0.918 0.993 0.973 0.918 0.887 0.984 0.790 0.908
DFM Wide ResNet-50 0.918 0.960 0.844 0.990 0.970 0.959 0.976 0.848 0.944 0.913 0.912 0.919 0.859 0.893 0.815 0.961
DFM ResNet-18 0.919 0.895 0.844 0.926 0.971 0.948 0.977 0.874 0.935 0.957 0.958 0.921 0.874 0.933 0.833 0.943
DFKDE Wide ResNet-50 0.875 0.907 0.844 0.905 0.945 0.914 0.946 0.790 0.914 0.817 0.894 0.922 0.855 0.845 0.722 0.910
DFKDE ResNet-18 0.872 0.864 0.844 0.854 0.960 0.898 0.942 0.793 0.908 0.827 0.894 0.916 0.859 0.853 0.756 0.916
Reference
If you use this library and love it, use this to cite it
@misc{anomalib,
title={Anomalib: A Deep Learning Library for Anomaly Detection},
author={Samet Akcay and
Dick Ameln and
Ashwin Vaidya and
Barath Lakshmanan and
Nilesh Ahuja and
Utku Genc},
year={2022},
eprint={2202.08341},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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