Metadata-Version: 2.1
Name: anomalib
Version: 1.0.1
Summary: anomalib - Anomaly Detection Library
Home-page: 
Author: Intel OpenVINO
Author-email: help@openvino.intel.com
License: 
                                         Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright (C) 2020-2021 Intel Corporation
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Provides-Extra: loggers
Provides-Extra: core
Provides-Extra: notebooks
Provides-Extra: openvino
Provides-Extra: full
Provides-Extra: dev
License-File: LICENSE

<div align="center">

<img src="https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/_static/images/logos/anomalib-wide-blue.png" width="600px">

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

---

[Key Features](#key-features) •
[Docs](https://anomalib.readthedocs.io/en/latest/) •
[Notebooks](notebooks) •
[License](LICENSE)

[![python](https://img.shields.io/badge/python-3.7%2B-green)]()
[![pytorch](https://img.shields.io/badge/pytorch-1.8.1%2B-orange)]()
[![openvino](https://img.shields.io/badge/openvino-2022.3.0-purple)]()

[![Pre-Merge Checks](https://github.com/openvinotoolkit/anomalib/actions/workflows/pre_merge.yml/badge.svg)](https://github.com/openvinotoolkit/anomalib/actions/workflows/pre_merge.yml)
[![Documentation Status](https://readthedocs.org/projects/anomalib/badge/?version=latest)](https://anomalib.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/openvinotoolkit/anomalib/branch/main/graph/badge.svg?token=Z6A07N1BZK)](https://codecov.io/gh/openvinotoolkit/anomalib)
[![Downloads](https://static.pepy.tech/personalized-badge/anomalib?period=total&units=international_system&left_color=grey&right_color=green&left_text=PyPI%20Downloads)](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 visual anomaly detection, where the goal of the algorithm is to detect and/or localize anomalies within images or videos in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!

<p align="center">
  <img src="https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/_static/images/readme.png" width="1000">
</p>

## Key features

- Simple and modular API and CLI for training, inference, benchmarking, and hyperparameter optimization.
- The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.
- [**Lightning**](https://www.lightning.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](tools) for quick and easy deployment of the standard or custom anomaly detection models.

# 📦 Installation

Anomalib provides two ways to install the library. The first is through PyPI, and the second is through a local installation. PyPI installation is recommended if you want to use the library without making any changes to the source code. If you want to make changes to the library, then a local installation is recommended.

<details>
<summary>Install from PyPI</summary>
Installing the library with pip is the easiest way to get started with anomalib.

```bash
pip install anomalib
```

This will install Anomalib CLI using the [installer](requirements/installer.txt) dependencies. Anomalib CLI is a command line interface for training, inference, benchmarking, and hyperparameter optimization. If you want to use the library as a Python package, you can install the library with the following command:

```bash
# Get help for the installation arguments
anomalib install -h

# Install the full package
anomalib install

# Install with verbose output
anomalib install -v

# Install the core package option only to train and evaluate models via Torch and Lightning
anomalib install --option core

# Install with OpenVINO option only. This is useful for edge deployment as the wheel size is smaller.
anomalib install --option openvino
```

</details>

<details>
<summary>Install from source</summary>
To install from source, you need to clone the repository and install the library using pip via editable mode.

```bash
# Use of virtual environment is highly recommended
# Using conda
yes | conda create -n anomalib_env python=3.10
conda activate anomalib_env

# Or using your favorite virtual environment
# ...

# Clone the repository and install in editable mode
git clone https://github.com/openvinotoolkit/anomalib.git
cd anomalib
pip install -e .
```

This will install Anomalib CLI using the [installer](requirements/installer.txt) dependencies. Anomalib CLI is a command line interface for training, inference, benchmarking, and hyperparameter optimization. If you want to use the library as a Python package, you can install the library with the following command:

```bash
# Get help for the installation arguments
anomalib install -h

# Install the full package
anomalib install

# Install with verbose output
anomalib install -v

# Install the core package option only to train and evaluate models via Torch and Lightning
anomalib install --option core

# Install with OpenVINO option only. This is useful for edge deployment as the wheel size is smaller.
anomalib install --option openvino
```

</details>

# 🧠 Training

Anomalib supports both API and CLI-based training. The API is more flexible and allows for more customization, while the CLI training utilizes command line interfaces, and might be easier for those who would like to use anomalib off-the-shelf.

<details>
<summary>Training via API</summary>

```python
# Import the required modules
from anomalib.data import MVTec
from anomalib.models import Patchcore
from anomalib.engine import Engine

# Initialize the datamodule, model and engine
datamodule = MVTec()
model = Patchcore()
engine = Engine()

# Train the model
engine.fit(datamodule=datamodule, model=model)
```

</details>

<details>
<summary>Training via CLI</summary>

```bash
# Get help about the training arguments, run:
anomalib train -h

# Train by using the default values.
anomalib train --model Patchcore --data anomalib.data.MVTec

# Train by overriding arguments.
anomalib train --model Patchcore --data anomalib.data.MVTec --data.category transistor

# Train by using a config file.
anomalib train --config <path/to/config>
```

</details>

# 🤖 Inference

Anomalib includes multiple inferencing scripts, including Torch, Lightning, Gradio, and OpenVINO inferencers to perform inference using the trained/exported model. Here we show an inference example using the Lightning inferencer. For other inferencers, please refer to the [Inference Documentation](https://anomalib.readthedocs.io).

<details>
<summary>Inference via API</summary>

The following example demonstrates how to perform Lightning inference by loading a model from a checkpoint file.

```python
# Assuming the datamodule, model and engine is initialized from the previous step,
# a prediction via a checkpoint file can be performed as follows:
predictions = engine.predict(
    datamodule=datamodule,
    model=model,
    ckpt_path="path/to/checkpoint.ckpt",
)
```

</details>

<details>
<summary>Inference via CLI</summary>

```bash
# To get help about the arguments, run:
anomalib predict -h

# Predict by using the default values.
anomalib predict --model anomalib.models.Patchcore \
                 --data anomalib.data.MVTec \
                 --ckpt_path <path/to/model.ckpt>

# Predict by overriding arguments.
anomalib predict --model anomalib.models.Patchcore \
                 --data anomalib.data.MVTec \
                 --ckpt_path <path/to/model.ckpt>
                 --return_predictions

# Predict by using a config file.
anomalib predict --config <path/to/config> --return_predictions
```

</details>

# ⚙️ Hyperparameter Optimization

Anomalib supports hyperparameter optimization (HPO) using [wandb](https://wandb.ai/) and [comet.ml](https://www.comet.com/). For more details refer the [HPO Documentation](https://openvinotoolkit.github.io/anomalib/tutorials/hyperparameter_optimization.html)

<details>
<summary>HPO via API</summary>

```python
# To be enabled in v1.1
```

</details>

<details>
<summary>HPO via CLI</summary>

The following example demonstrates how to perform HPO for the Patchcore model.

```bash
anomalib hpo --backend WANDB  --sweep_config tools/hpo/configs/wandb.yaml
```

</details>

# 🧪 Experiment Management

Anomalib 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). For more information, refer to the [Logging Documentation](https://openvinotoolkit.github.io/anomalib/tutorials/logging.html)

<details>
<summary>Experiment Management via API</summary>

```python
# To be enabled in v1.1
```

</details>

<details>
<summary>Experiment Management via CLI</summary>

Below is an example of how to enable logging for hyper-parameters, metrics, model graphs, and predictions on images in the test data-set.

You first need to modify the `config.yaml` file to enable logging. The following example shows how to enable logging:

```yaml
# Place the experiment management config here.
```

```bash
# Place the Experiment Management CLI command here.
```

</details>

# 📊 Benchmarking

Anomalib provides a benchmarking tool to evaluate the performance of the anomaly detection models on a given dataset. The benchmarking tool can be used to evaluate the performance of the models on a given dataset, or to compare the performance of multiple models on a given dataset.

Each model in anomalib is benchmarked on a set of datasets, and the results are available in `src/anomalib/models/<type>/<model_name>/README.md`. For example, the MVTec AD results for the Patchcore model are available in the corresponding [README.md](src/anomalib/models/image/patchcore/README.md#mvtec-ad-dataset) file.

<details>
<summary>Benchmarking via API</summary>

```python
# To be enabled in v1.1
```

</details>

<details>
<summary>Benchmarking via CLI</summary>

To run the benchmarking tool, run the following command:

```bash
anomalib benchmark --config tools/benchmarking/benchmark_params.yaml
```

</details>

# ✍️ Reference

If you use this library and love it, use this to cite it 🤗

```tex
@inproceedings{akcay2022anomalib,
  title={Anomalib: A deep learning library for anomaly detection},
  author={Akcay, Samet and Ameln, Dick and Vaidya, Ashwin and Lakshmanan, Barath and Ahuja, Nilesh and Genc, Utku},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={1706--1710},
  year={2022},
  organization={IEEE}
}
```

# 👥 Contributing

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

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

<a href="https://github.com/openvinotoolkit/anomalib/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=openvinotoolkit/anomalib" />
</a>
