mirror of https://github.com/open-mmlab/mmpose
68 lines
3.3 KiB
Markdown
68 lines
3.3 KiB
Markdown
# Overview
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This chapter will introduce you to the overall framework of MMPose and provide links to detailed tutorials.
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## What is MMPose
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MMPose is a Pytorch-based pose estimation open-source toolkit, a member of the [OpenMMLab Project](https://github.com/open-mmlab). It contains a rich set of algorithms for 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, fashion landmark detection and animal pose estimation as well as related components and modules, below is its overall framework.
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MMPose consists of **8** main components:
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- **apis** provides high-level APIs for model inference
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- **structures** provides data structures like bbox, keypoint and PoseDataSample
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- **datasets** supports various datasets for pose estimation
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- **transforms** contains a lot of useful data augmentation transforms
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- **codecs** provides pose encoders and decoders: an encoder encodes poses (mostly keypoints) into learning targets (e.g. heatmaps), and a decoder decodes model outputs into pose predictions
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- **models** provides all components of pose estimation models in a modular structure
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- **pose_estimators** defines all pose estimation model classes
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- **data_preprocessors** is for preprocessing the input data of the model
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- **backbones** provides a collection of backbone networks
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- **necks** contains various neck modules
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- **heads** contains various prediction heads that perform pose estimation
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- **losses** contains various loss functions
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- **engine** provides runtime components related to pose estimation
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- **hooks** provides various hooks of the runner
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- **evaluation** provides metrics for evaluating model performance
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- **visualization** is for visualizing skeletons, heatmaps and other information
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## How to Use this Guide
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We have prepared detailed guidelines for all types of users:
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1. For installation instrunctions:
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- [Installation](./installation.md)
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2. For the basic usage of MMPose:
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- [A 20-minute Tour to MMPose](./guide_to_framework.md)
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- [Demos](./demos.md)
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- [Inference](./user_guides/inference.md)
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- [Configs](./user_guides/configs.md)
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- [Prepare Datasets](./user_guides/prepare_datasets.md)
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- [Train and Test](./user_guides/train_and_test.md)
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- [Deployment](./user_guides/how_to_deploy.md)
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- [Model Analysis](./user_guides/model_analysis.md)
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- [Dataset Annotation and Preprocessing](./user_guides/dataset_tools.md)
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3. For developers who wish to develop based on MMPose:
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- [Learn about Codecs](./advanced_guides/codecs.md)
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- [Dataflow in MMPose](./advanced_guides/dataflow.md)
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- [Implement New Models](./advanced_guides/implement_new_models.md)
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- [Customize Datasets](./advanced_guides/customize_datasets.md)
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- [Customize Data Transforms](./advanced_guides/customize_transforms.md)
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- [Customize Optimizer](./advanced_guides/customize_optimizer.md)
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- [Customize Logging](./advanced_guides/customize_logging.md)
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- [Migration Guide](./migration.md)
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4. For researchers and developers who are willing to contribute to MMPose:
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- [Contribution Guide](./contribution_guide.md)
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5. For some common issues, we provide a FAQ list:
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- [FAQ](./faq.md)
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