Metadata-Version: 2.1
Name: mmaction2
Version: 0.10.0
Summary: OpenMMLab Action Understanding Toolbox and Benchmark
Home-page: https://github.com/open-mmlab/mmaction2
Maintainer: MMAction2 Authors
Maintainer-email: openmmlab@gmail.com
License: Apache License 2.0
Description: <div align="center">
          <img src="docs/imgs/mmaction2_logo.png" width="500"/>
        </div>
        
        ## Introduction
        
        [![Documentation](https://readthedocs.org/projects/mmaction2/badge/?version=latest)](https://mmaction2.readthedocs.io/en/latest/)
        [![actions](https://github.com/open-mmlab/mmaction2/workflows/build/badge.svg)](https://github.com/open-mmlab/mmaction2/actions)
        [![codecov](https://codecov.io/gh/open-mmlab/mmaction2/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmaction2)
        [![PyPI](https://img.shields.io/pypi/v/mmaction2)](https://pypi.org/project/mmaction2/)
        [![LICENSE](https://img.shields.io/github/license/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/blob/master/LICENSE)
        [![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
        [![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
        
        MMAction2 is an open-source toolbox for action understanding based on PyTorch.
        It is a part of the [OpenMMLab](http://openmmlab.org/) project.
        
        The master branch works with **PyTorch 1.3+**.
        
        <div align="center">
          <img src="docs/imgs/mmaction2_overview.gif" width="450px"/><br>
            Action Recognition Results on Kinetics-400
        </div>
        <div align="center">
          <img src="docs/imgs/spatio-temporal-det.gif" width="800px"/><br>
            Spatio-Temporal Action Detection Results on AVA-2.1
        </div>
        
        ### Major Features
        
        - **Modular design**
        
          We decompose the action understanding framework into different components and one can easily construct a customized
          action understanding framework by combining different modules.
        
        - **Support for various datasets**
        
          The toolbox directly supports multiple datasets, UCF101, Kinetics-[400/600/700], Something-Something V1&V2, Moments in Time, Multi-Moments in Time, THUMOS14, etc.
        
        - **Support for multiple action understanding frameworks**
        
          MMAction2 implements popular frameworks for action understanding:
        
          - For action recognition, various algorithms are implemented, including TSN, TSM, TIN, R(2+1)D, I3D, SlowOnly, SlowFast, CSN, Non-local, etc.
        
          - For temporal action localization, we implement BSN, BMN, SSN.
        
        - **Well tested and documented**
        
          We provide detailed documentation and API reference, as well as unittests.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Changelog
        
        v0.10.0 was released in 05/01/2021. Please refer to [changelog.md](docs/changelog.md) for details and release history.
        
        ## Benchmark
        
        | Model  |input| io backend | batch size x gpus | MMAction2 (s/iter) | MMAction (s/iter) | Temporal-Shift-Module (s/iter) | PySlowFast (s/iter) |
        | :--- | :---------------:|:---------------:| :---------------:| :---------------:  | :--------------------: | :----------------------------: | :-----------------: |
        | [TSN](/configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py)| 256p rawframes |Memcached| 32x8|**[0.32](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/tsn_256p_rawframes_memcahed_32x8.zip)** | [0.38](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction/tsn_256p_rawframes_memcached_32x8.zip)| [0.42](https://download.openmmlab.com/mmaction/benchmark/recognition/temporal_shift_module/tsn_256p_rawframes_memcached_32x8.zip)| x |
        | [TSN](/configs/recognition/tsn/tsn_r50_video_1x1x3_100e_kinetics400_rgb.py)| 256p dense-encoded video |Disk| 32x8|**[0.61](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/tsn_256p_fast_videos_disk_32x8.zip)**| x | x | TODO |
        |[I3D heavy](/configs/recognition/i3d/i3d_r50_video_heavy_8x8x1_100e_kinetics400_rgb.py)|256p videos|Disk |8x8| **[0.34](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/i3d_heavy_256p_videos_disk_8x8.zip)** | x | x | [0.44](https://download.openmmlab.com/mmaction/benchmark/recognition/pyslowfast/pysf_i3d_r50_8x8_video.log) |
        | [I3D](/configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py)|256p rawframes|Memcached|8x8| **[0.43](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/i3d_256p_rawframes_memcahed_8x8.zip)** | [0.56](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction/i3d_256p_rawframes_memcached_8x8.zip) | x | x |
        | [TSM](/configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py) |256p rawframes|Memcached| 8x8|**[0.31](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/tsm_256p_rawframes_memcahed_8x8.zip)** | x | [0.41](https://download.openmmlab.com/mmaction/benchmark/recognition/temporal_shift_module/tsm_256p_rawframes_memcached_8x8.zip) | x |
        | [Slowonly](/configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py)|256p videos|Disk|8x8 | **[0.32](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/slowonly_256p_videos_disk_8x8.zip)** | TODO | x | [0.34](https://download.openmmlab.com/mmaction/benchmark/recognition/pyslowfast/pysf_slowonly_r50_4x16_video.log) |
        | [Slowfast](/configs/recognition/slowfast/slowfast_r50_video_4x16x1_256e_kinetics400_rgb.py)|256p videos|Disk|8x8 | **[0.69](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/slowfast_256p_videos_disk_8x8.zip)** | x | x | [1.04](https://download.openmmlab.com/mmaction/benchmark/recognition/pyslowfast/pysf_slowfast_r50_4x16_video.log) |
        | [R(2+1)D](/configs/recognition/r2plus1d/r2plus1d_r34_video_8x8x1_180e_kinetics400_rgb.py)|256p videos |Disk| 8x8|**[0.45](https://download.openmmlab.com/mmaction/benchmark/recognition/mmaction2/r2plus1d_256p_videos_disk_8x8.zip)** | x | x | x |
        
        Details can be found in [benchmark](docs/benchmark.md).
        
        ## ModelZoo
        
        Supported methods for action recognition:
        
        - [x] [TSN](configs/recognition/tsn/README.md)
        - [x] [TSM](configs/recognition/tsm/README.md)
        - [x] [TSM Non-Local](configs/recognition/i3d)
        - [x] [R(2+1)D](configs/recognition/r2plus1d/README.md)
        - [x] [I3D](configs/recognition/i3d/README.md)
        - [x] [I3D Non-Local](configs/recognition/i3d/README.md)
        - [x] [SlowOnly](configs/recognition/slowonly/README.md)
        - [x] [SlowFast](configs/recognition/slowfast/README.md)
        - [x] [CSN](configs/recognition/csn/README.md)
        - [x] [TIN](configs/recognition/tin/README.md)
        - [x] [TPN](configs/recognition/tpn/README.md)
        - [x] [C3D](configs/recognition/c3d/README.md)
        - [x] [X3D](configs/recognition/x3d/README.md)
        - [x] [OmniSource](configs/recognition/omnisource/README.md)
        - [x] [MultiModality: Audio](configs/recognition_audio/resnet/README.md)
        
        Supported methods for action localization:
        
        - [x] [BMN](configs/localization/bmn/README.md)
        - [x] [BSN](configs/localization/bsn/README.md)
        - [x] [SSN](configs/localization/ssn/README.md)
        
        Supported methods for spatio-temporal action detection:
        
        - [x] [SlowOnly+Fast R-CNN](configs/detection/ava/README.md)
        - [x] [SlowFast+Fast R-CNN](configs/detection/ava/README.md)
        
        Results and models are available in the *README.md* of each method's config directory.
        A summary can be found in the [**model zoo**](https://mmaction2.readthedocs.io/en/latest/recognition_models.html) page.
        
        ## Installation
        
        Please refer to [install.md](docs/install.md) for installation.
        
        ## Data Preparation
        
        Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
        The supported datasets are listed in [supported_datasets.md](docs/supported_datasets.md)
        
        ## Get Started
        
        Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMAction2.
        There are also tutorials for [learn about configs](docs/tutorials/1_config.md), [finetuning models](docs/tutorials/2_finetune.md), [adding new dataset](docs/tutorials/3_new_dataset.md), [designing data pipeline](docs/tutorials/4_data_pipeline.md), [adding new modules](docs/tutorials/5_new_modules.md), [exporting model to onnx](docs/tutorials/6_export_model.md) and [customizing runtime settings](docs/tutorials/7_customize_runtime.md).
        
        A Colab tutorial is also provided. You may preview the notebook [here](demo/mmaction2_tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb) on Colab.
        
        ## FAQ
        
        Please refer to [FAQ](docs/faq.md) for frequently asked questions.
        
        ## Contributing
        
        We appreciate all contributions to improve MMAction2. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
        
        ## Acknowledgement
        
        MMAction2 is an open source project that is contributed by researchers and engineers from various colleges and companies.
        We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
        We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.
        
Keywords: computer vision,action understanding
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: optional
