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anomalib-0.4.0rc2
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مشاهده بیشترتوضیحات
anomalib - Anomaly Detection Library
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | anomalib-0.4.0rc2 |
| نام | anomalib |
| نسخه کتابخانه | 0.4.0rc2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Intel OpenVINO |
| ایمیل نویسنده | help@openvino.intel.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/anomalib/ |
| مجوز | Copyright (c) Intel - All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License")See LICENSE file for more details. |
<div align="center">
<img src="docs/source/images/logos/anomalib-wide-blue.png" width="600px">
**A library for benchmarking, developing and deploying deep learning anomaly detection algorithms**
---
[Key Features](#key-features) •
[Getting Started](#getting-started) •
[Docs](https://openvinotoolkit.github.io/anomalib) •
[License](https://github.com/openvinotoolkit/anomalib/blob/main/LICENSE)
[]()
[]()
[]()
[](https://www.comet.com/site/products/ml-experiment-tracking/?utm_source=anomalib&utm_medium=referral)
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[](https://github.com/openvinotoolkit/anomalib/actions/workflows/docs.yml)
[](https://pepy.tech/project/anomalib)
</div>
---
# 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**](https://www.pytorchlightning.ai/) based model implementations to reduce boilerplate code and limit the implementation efforts to the bare essentials.
- All models can be exported to [**OpenVINO**](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) Intermediate Representation (IR) for accelerated inference on intel hardware.
- A set of [inference tools](#inference) 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](https://openvinotoolkit.github.io/anomalib/#supported-hardware) section in the documentation.
## Jupyter Notebooks
For getting started with a Jupyter Notebook, please refer to the [Notebooks](./notebooks) folder of this repository. Additionally, you can refer to a few created by the community:
<a href="https://colab.research.google.com/drive/1K4a4z2iZGBNhWdmt9Aqdld7kTAxBfAmi?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> by [@bth5](https://github.com/bth5)
<a target="_blank" href="https://www.kaggle.com/code/ipythonx/mvtec-ad-anomaly-detection-with-anomalib-library"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" /></a> by [@innat](https://github.com/innat)
## PyPI Install
You can get started with `anomalib` by just using pip.
```bash
pip install anomalib
```
## Local Install
It is highly recommended to use virtual environment when installing anomalib. For instance, with [anaconda](https://www.anaconda.com/products/individual), `anomalib` could be installed as,
```bash
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
## ⚠️ Anomalib < v.0.4.0
By default [`python tools/train.py`](https://github.com/openvinotoolkit/anomalib/blob/main/tools/train.py)
runs [PADIM](https://arxiv.org/abs/2011.08785) model on `leather` category from the [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) dataset.
```bash
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`](https://github.com/openvinotoolkit/anomalib/blob/main/configs/model/padim.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:
```bash
python tools/train.py --config <path/to/model/config.yaml>
```
For example, to train [PADIM](anomalib/models/padim) you can use
```bash
python tools/train.py --config 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.
```bash
python tools/train.py --model padim
```
where the currently available models are:
- [CFlow](anomalib/models/cflow)
- [DFM](anomalib/models/dfm)
- [DFKDE](anomalib/models/dfkde)
- [FastFlow](anomalib/models/fastflow)
- [PatchCore](anomalib/models/patchcore)
- [PADIM](anomalib/models/padim)
- [STFPM](anomalib/models/stfpm)
- [GANomaly](anomalib/models/ganomaly)
## Feature extraction & (pre-trained) backbones
The pre-trained backbones come from [PyTorch Image Models (timm)](https://github.com/rwightman/pytorch-image-models), 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"](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055#b83b:~:text=ready%20to%20train!-,Feature%20Extraction,-timm%20models%20also>).
Tips:
- Papers With Code has an interface to easily browse models available in timm: [https://paperswithcode.com/lib/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.
Anomalib < v.0.4.0
```yaml
model:
name: cflow
backbone: wide_resnet50_2
pre_trained: true
Anomalib > v.0.4.0 Beta - Subject to Change
```
Anomalib >= v.0.4.0
```yaml
model:
class_path: anomalib.models.Cflow
init_args:
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:
```yaml
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 > v.0.4.0 Beta - Subject to Change
We introduce a new CLI approach that uses [PyTorch Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html). To train a model using the new CLI, one would call the following:
```bash
anomalib fit --config <path/to/new/config/file>
```
For instance, to train a [PatchCore](https://github.com/openvinotoolkit/anomalib/tree/main/anomalib/models/patchcore) model, the following command would be run:
```bash
anomalib fit --config ./configs/model/patchcore.yaml
```
The new CLI approach offers a lot more flexibility, details of which are explained in the [documentation](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html).
# Inference
## ⚠️ Anomalib < v.0.4.0
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:
```bash
python tools/inference/lightning_inference.py -h
```
As a quick example:
```bash
python tools/inference/lightning_inference.py \
--config anomalib/models/padim/config.yaml \
--weights results/padim/mvtec/bottle/weights/model.ckpt \
--input datasets/MVTec/bottle/test/broken_large/000.png \
--output results/padim/mvtec/bottle/images
```
Example OpenVINO Inference:
```bash
python tools/inference/openvino_inference.py \
--config anomalib/models/padim/config.yaml \
--weights results/padim/mvtec/bottle/openvino/openvino_model.bin \
--meta_data results/padim/mvtec/bottle/openvino/meta_data.json \
--input datasets/MVTec/bottle/test/broken_large/000.png \
--output results/padim/mvtec/bottle/images
```
> Ensure that you provide path to `meta_data.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](https://openvinotoolkit.github.io/anomalib/guides/inference.html#gradio-inference) for more details.
A quick example:
```bash
python tools/inference/gradio_inference.py \
--config ./anomalib/models/padim/config.yaml \
--weights ./results/padim/mvtec/bottle/weights/model.ckpt
```
## Exporting Model to ONNX or OpenVINO IR
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`.
```yaml
optimization:
export_mode: "openvino" # options: openvino, onnx
```
# Hyperparameter Optimization
To run hyperparameter optimization, use the following command:
```bash
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](https://openvinotoolkit.github.io/anomalib/guides/hyperparameter_optimization.html)
# Benchmarking
To gather benchmarking data such as throughput across categories, use the following command:
```bash
python tools/benchmarking/benchmark.py \
--config <relative/absolute path>/<paramfile>.yaml
```
Refer to the [Benchmarking Documentation](https://openvinotoolkit.github.io/anomalib/guides/benchmarking.html) for more details.
# Experiment Management
Anomablib is integrated with various libraries for experiment tracking such as Comet, tensorboard, and wandb through [pytorch lighting loggers](https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html).
Below is an example of how to enable logging for hyper-parameters, metrics, model graphs, and predictions on images in the test data-set
```yaml
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](https://openvinotoolkit.github.io/anomalib/guides/logging.html)
Note: Set your API Key for [Comet.ml](https://www.comet.com/signup?utm_source=anomalib&utm_medium=referral) via `comet_ml.init()` in interactive python or simply run `export COMET_API_KEY=<Your API Key>`
# Datasets
`anomalib` supports MVTec AD [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) and BeanTech [(CC-BY-SA)](https://creativecommons.org/licenses/by-sa/4.0/legalcode) for benchmarking and `folder` for custom dataset training/inference.
## [MVTec AD Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad)
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)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## 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 |
| 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 |
| ------------- | ------------------ | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :--------: | :--------: | :-------: |
| **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 |
| 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 🤗
```tex
@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}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.1.0 | albumentations |
| >=3.31.7 | comet-ml |
| >=0.3.2 | einops |
| >=2.9.4 | gradio |
| ==0.4.0 | imgaug |
| >=4.3 | jsonargparse[signatures] |
| >=0.6.6 | kornia |
| >=3.4.3 | matplotlib |
| >=2.1.1 | omegaconf |
| >=4.5.3.56 | opencv-python |
| >=1.1.0 | pandas |
| <1.7.0,>=1.6.0 | pytorch-lightning |
| ==0.5.4 | timm |
| <=0.9.3,>=0.9.1 | torchmetrics |
| <=0.13.0,>=0.9.1 | torchvision |
| <=0.13.0,>=0.9.1 | torchtext |
| ==0.12.17 | wandb |
| ==2022.9.29 | furo |
| - | myst-parser |
| - | pandoc |
| >=4.1.2 | sphinx |
| - | sphinx-autoapi |
| ==0.1.8 | sphinxemoji |
| >=0.8.9 | nbsphinx |
| ==0.7.1 | defusedxml |
| ==2.26.0 | requests |
| ~=2.5 | networkx |
| ==2.1.0 | nncf |
| ==1.10.1 | onnx |
| ==2022.1.0 | openvino-dev |
| ==0.7.1 | defusedxml |
| ==2.26.0 | requests |
| ~=2.5 | networkx |
| ==2.1.0 | nncf |
| ==1.10.1 | onnx |
| ==2022.1.0 | openvino-dev |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl anomalib-0.4.0rc2:
pip install anomalib-0.4.0rc2.whl
نصب پکیج tar.gz anomalib-0.4.0rc2:
pip install anomalib-0.4.0rc2.tar.gz