A library for benchmarking, developing and deploying deep learning anomaly detection algorithms

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Key Features • Getting Started • Docs • License

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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.

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Getting Started

Following is a guide on how to get started with anomalib. For more details, look at the Documentation.

Jupyter Notebooks

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:

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=3.10
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 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:

• CFA
• CFlow
• DFKDE
• DFM
• DRAEM
• EfficientAd
• FastFlow
• GANomaly
• PADIM
• PatchCore
• Reverse Distillation
• STFPM

Feature extraction & (pre-trained) backbones

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

Custom Dataset

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:

Configuration for Custom Dataset

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
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  test_split_mode: from_dir # options: [from_dir, synthetic]
  val_split_mode: same_as_test # options: [same_as_test, from_test, sythetic]
  val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
  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

By placing the above configuration to the dataset section of the config.yaml file, the model will be trained on the custom dataset.

Inference

Anomalib includes multiple inferencing scripts, including Torch, Lightning, Gradio, and OpenVINO inferencers to perform inference using the trained/exported model. In this section, we will go over how to use these scripts to perform inference.

PyTorch Inference

# To get help about the arguments, run:
python tools/inference/torch_inference.py --help

# Example Torch inference command:
python tools/inference/torch_inference.py \
    --weights results/padim/mvtec/bottle/run/weights/torch/model.pt \
    --input datasets/MVTec/bottle/test/broken_large/000.png \
    --output results/padim/mvtec/bottle/images

Lightning Inference

# To get help about the arguments, run:
python tools/inference/lightning_inference.py --help

# Example Lightning inference command:
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

OpenVINO Inference

To run the OpenVINO inference, you need to first export the PyTorch model to an OpenVINO model. ensure that export_mode is set to "openvino" in the respective model config.yaml.

# Example config.yaml for OpenVINO
optimization:
  export_mode: "openvino" # options: openvino, onnx

# To get help about the arguments, run:
python tools/inference/openvino_inference.py --help

# Example OpenVINO inference command:
python tools/inference/openvino_inference.py \
    --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.

Gradio Inference

You can also use Gradio Inference to interact with the trained models using a UI. Refer to our guide for more details.

# To get help about the arguments, run:
python tools/inference/gradio_inference.py --help

# Example Gradio inference command:
python tools/inference/gradio_inference.py \
    --weights results/padim/mvtec/bottle/run/weights/model.ckpt \
    --metadata results/padim/mvtec/bottle/run/openvino/metadata.json  \ # Optional
    --share  # Optional to share the UI

Hyperparameter Optimization

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

Benchmarking

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.

Experiment Management

Anomalib 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>

Community Projects

1. Web-based Pipeline for Training and Inference

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

Datasets

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

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.

Image-Level AUC

Model		Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
EfficientAd	PDN-S	0.982	0.982	1.000	0.997	1.000	0.986	1.000	0.952	0.950	0.952	0.979	0.987	0.960	0.997	0.999	0.994
EfficientAd	PDN-M	0.975	0.972	0.998	1.000	0.999	0.984	0.991	0.945	0.957	0.948	0.989	0.926	0.975	1.000	0.965	0.971
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

Pixel-Level AUC

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
EfficientAd	PDN-S	0.960	0.963	0.937	0.976	0.907	0.868	0.983	0.983	0.980	0.976	0.978	0.986	0.985	0.962	0.956	0.961
EfficientAd	PDN-M	0.957	0.948	0.937	0.976	0.906	0.867	0.976	0.986	0.957	0.977	0.984	0.978	0.986	0.964	0.947	0.960
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
EfficientAd	PDN-S	0.970	0.966	1.000	0.995	1.000	0.975	1.000	0.907	0.956	0.897	0.978	0.982	0.944	0.984	0.988	0.983
EfficientAd	PDN-M	0.966	0.977	0.991	1.000	0.994	0.967	0.984	0.922	0.969	0.884	0.984	0.952	0.955	1.000	0.929	0.979
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

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}
}

Contributing

For those who would like to contribute to the library, see CONTRIBUTING.md for details.

Thank you to all of the people who have already made a contribution - we appreciate your support!