![](https://raw.githubusercontent.com/ake020675/anomalib/development/docs/source/images/logos/anomalib-wide-blue.png)
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms
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.
To get an overview of all the devices where anomalib
as been tested thoroughly, look at the Supported Hardware section in the documentation.
You can get started with anomalib
by just using pip.
pip install anomalib
NOTE: Due to ongoing fast pace of development, we encourage you to use editable install until we release v0.2.5.
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 .
By default python tools/train.py
runs PADIM model on leather
category from the MVTec AD (CC BY-NC-SA 4.0) dataset.
python tools/train.py # Train PADIM on MVTec AD 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:
It is also possible to train on a custom folder dataset. To do so, data
section in config.yaml
is to be modified as follows:
dataset:
name: <name-of-the-dataset>
format: folder
path: <path/to/folder/dataset>
normal: normal # name of the folder containing normal images.
abnormal: abnormal # name of the folder containing abnormal images.
task: segmentation # classification or segmentation
mask: <path/to/mask/annotations> #optional
extensions: null
split_ratio: 0.2 # ratio of the normal images that will be used to create a test split
seed: 0
image_size: 256
train_batch_size: 32
test_batch_size: 32
num_workers: 8
transform_config: null
create_validation_set: true
tiling:
apply: false
tile_size: null
stride: null
remove_border_count: 0
use_random_tiling: False
random_tile_count: 16
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 openvino
apply
is set to True
in the respective model config.yaml
.
optimization:
openvino:
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.
anomalib
supports MVTec AD (CC BY-NC-SA 4.0) and BeanTech (CC-BY-SA) for benchmarking and folder
for custom dataset training/inference.
MVTec AD dataset is one of the main benchmarks for anomaly detection, and is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
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 |
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 |
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 |
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}
}