![](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/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!
- 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.
Following is a guide on how to get started with anomalib
. For more details, look at the Documentation.
For getting started with a Jupyter Notebook, please refer to the Notebooks folder of this repository. Additionally, you can refer to a few created by the community:
by @bth5
by @innat
You can get started with anomalib
by just using pip.
pip install anomalib
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 --config <path/to/model/config.yaml>
For example, to train PADIM you can use
python tools/train.py --config src/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:
The pre-trained backbones come from PyTorch Image Models (timm), which are wrapped by FeatureExtractor
.
For more information, please check our documentation or the section about feature extraction in "Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide".
Tips:
-
Papers With Code has an interface to easily browse models available in timm: https://paperswithcode.com/lib/timm
-
You can also find them with the function
timm.list_models("resnet*", pretrained=True)
The backbone can be set in the config file, two examples below.
model:
name: cflow
backbone: wide_resnet50_2
pre_trained: true
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_dir: normal # name of the folder containing normal images.
abnormal_dir: abnormal # name of the folder containing abnormal images.
normal_test_dir: null # name of the folder containing normal test 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
image_size: 256
train_batch_size: 32
test_batch_size: 32
num_workers: 8
transform_config:
train: null
val: 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 includes multiple tools, including Lightning, Gradio, and OpenVINO inferencers, for performing inference with a trained model.
The following command can be used to run PyTorch Lightning inference from the command line:
python tools/inference/lightning_inference.py -h
As a quick example:
python tools/inference/lightning_inference.py \
--config src/anomalib/models/padim/config.yaml \
--weights results/padim/mvtec/bottle/run/weights/model.ckpt \
--input datasets/MVTec/bottle/test/broken_large/000.png \
--output results/padim/mvtec/bottle/images
Example OpenVINO Inference:
python tools/inference/openvino_inference.py \
--config src/anomalib/models/padim/config.yaml \
--weights results/padim/mvtec/bottle/run/openvino/model.bin \
--metadata results/padim/mvtec/bottle/run/openvino/metadata.json \
--input datasets/MVTec/bottle/test/broken_large/000.png \
--output results/padim/mvtec/bottle/images
Ensure that you provide path to
metadata.json
if you want the normalization to be applied correctly.
You can also use Gradio Inference to interact with the trained models using a UI. Refer to our guide for more details.
A quick example:
python tools/inference/gradio_inference.py \
--config src/anomalib/models/padim/config.yaml \
--weights results/padim/mvtec/bottle/run/weights/model.ckpt
It is possible to export your model to ONNX or OpenVINO IR
If you want to export your PyTorch model to an OpenVINO model, ensure that export_mode
is set to "openvino"
in the respective model config.yaml
.
optimization:
export_mode: "openvino" # options: openvino, onnx
To run hyperparameter optimization, use the following command:
python tools/hpo/sweep.py \
--model padim --model_config ./path_to_config.yaml \
--sweep_config tools/hpo/sweep.yaml
For more details refer the HPO Documentation
To gather benchmarking data such as throughput across categories, use the following command:
python tools/benchmarking/benchmark.py \
--config <relative/absolute path>/<paramfile>.yaml
Refer to the Benchmarking Documentation for more details.
Anomablib is integrated with various libraries for experiment tracking such as Comet, tensorboard, and wandb through pytorch lighting loggers.
Below is an example of how to enable logging for hyper-parameters, metrics, model graphs, and predictions on images in the test data-set
visualization:
log_images: True # log images to the available loggers (if any)
mode: full # options: ["full", "simple"]
logging:
logger: [comet, tensorboard, wandb]
log_graph: True
For more information, refer to the Logging Documentation
Note: Set your API Key for Comet.ml via comet_ml.init()
in interactive python or simply run export COMET_API_KEY=<Your API Key>
This project showcases an end-to-end training and inference pipeline build on top of Anomalib. It provides a web-based UI for uploading MVTec style datasets and training them on the available Anomalib models. It also has sections for calling inference on individual images as well as listing all the images with their predictions in the database.
You can view the project on Github For more details see the Discussion forum
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).
Note: These metrics are collected with image size of 256 and seed
42
. This common setting is used to make model comparisons fair.
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 | 1.000 | 0.999 | 0.993 | 1.0 | 0.893 | 0.945 | 1.0 | 0.995 | 0.924 | 0.908 | 0.897 | 0.943 | 0.984 |
CFA | Wide ResNet-50 | 0.956 | 0.978 | 0.961 | 0.990 | 0.999 | 0.994 | 0.998 | 0.979 | 0.872 | 1.000 | 0.995 | 0.946 | 0.703 | 1.000 | 0.957 | 0.967 |
CFA | ResNet-18 | 0.930 | 0.953 | 0.947 | 0.999 | 1.000 | 1.000 | 0.991 | 0.947 | 0.858 | 0.995 | 0.932 | 0.887 | 0.625 | 0.994 | 0.895 | 0.919 |
PaDiM | Wide ResNet-50 | 0.950 | 0.995 | 0.942 | 1.000 | 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 |
DFM | Wide ResNet-50 | 0.943 | 0.855 | 0.784 | 0.997 | 0.995 | 0.975 | 0.999 | 0.969 | 0.924 | 0.978 | 0.939 | 0.962 | 0.873 | 0.969 | 0.971 | 0.961 |
DFM | ResNet-18 | 0.936 | 0.817 | 0.736 | 0.993 | 0.966 | 0.977 | 1.000 | 0.956 | 0.944 | 0.994 | 0.922 | 0.961 | 0.89 | 0.969 | 0.939 | 0.969 |
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 | 0.500 | 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 |
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 |
GANomaly | 0.421 | 0.203 | 0.404 | 0.413 | 0.408 | 0.744 | 0.251 | 0.457 | 0.682 | 0.537 | 0.270 | 0.472 | 0.231 | 0.372 | 0.440 | 0.434 |
Model | Avg | Carpet | Grid | Leather | Tile | Wood | Bottle | Cable | Capsule | Hazelnut | Metal Nut | Pill | Screw | Toothbrush | Transistor | Zipper | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CFA | Wide ResNet-50 | 0.983 | 0.980 | 0.954 | 0.989 | 0.985 | 0.974 | 0.989 | 0.988 | 0.989 | 0.985 | 0.992 | 0.988 | 0.979 | 0.991 | 0.977 | 0.990 |
CFA | ResNet-18 | 0.979 | 0.970 | 0.973 | 0.992 | 0.978 | 0.964 | 0.986 | 0.984 | 0.987 | 0.987 | 0.981 | 0.981 | 0.973 | 0.990 | 0.964 | 0.978 |
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 |
CFA | Wide ResNet-50 | 0.962 | 0.961 | 0.957 | 0.995 | 0.994 | 0.983 | 0.984 | 0.962 | 0.946 | 1.000 | 0.984 | 0.952 | 0.855 | 1.000 | 0.907 | 0.975 |
CFA | ResNet-18 | 0.946 | 0.956 | 0.946 | 0.973 | 1.000 | 1.000 | 0.983 | 0.907 | 0.938 | 0.996 | 0.958 | 0.920 | 0.858 | 0.984 | 0.795 | 0.949 |
CFlow | Wide ResNet-50 | 0.944 | 0.972 | 0.932 | 1.000 | 0.988 | 0.967 | 1.000 | 0.832 | 0.939 | 1.000 | 0.979 | 0.924 | 0.971 | 0.870 | 0.818 | 0.967 |
PaDiM | Wide ResNet-50 | 0.951 | 0.989 | 0.930 | 1.000 | 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 |
DFM | Wide ResNet-50 | 0.950 | 0.915 | 0.870 | 0.995 | 0.988 | 0.960 | 0.992 | 0.939 | 0.965 | 0.971 | 0.942 | 0.956 | 0.906 | 0.966 | 0.914 | 0.971 |
DFM | ResNet-18 | 0.943 | 0.895 | 0.871 | 0.978 | 0.958 | 0.900 | 1.000 | 0.935 | 0.965 | 0.966 | 0.942 | 0.956 | 0.914 | 0.966 | 0.868 | 0.964 |
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 |
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 |
GANomaly | 0.834 | 0.864 | 0.844 | 0.852 | 0.836 | 0.863 | 0.863 | 0.760 | 0.905 | 0.777 | 0.894 | 0.916 | 0.853 | 0.833 | 0.571 | 0.881 |
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}
}