overview.md 3.2 KB
Overview
This chapter will introduce you to the overall framework of MMPose and provide links to detailed tutorials.
What is MMPose
MMPose is a Pytorch-based pose estimation open-source toolkit, a member of the OpenMMLab Project. 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.
MMPose consists of 8 main components:
- apis provides high-level APIs for model inference
- structures provides data structures like bbox, keypoint and PoseDataSample
- datasets supports various datasets for pose estimation
- transforms contains a lot of useful data augmentation transforms
- 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
- models provides all components of pose estimation models in a modular structure
- pose_estimators defines all pose estimation model classes
- data_preprocessors is for preprocessing the input data of the model
- backbones provides a collection of backbone networks
- necks contains various neck modules
- heads contains various prediction heads that perform pose estimation
- losses contains various loss functions
- engine provides runtime components related to pose estimation
- hooks provides various hooks of the runner
- evaluation provides metrics for evaluating model performance
- visualization is for visualizing skeletons, heatmaps and other information
How to Use this Guide
We have prepared detailed guidelines for all types of users:
For installation instrunctions:
For the basic usage of MMPose:
For developers who wish to develop based on MMPose:
For researchers and developers who are willing to contribute to MMPose:
For some common issues, we provide a FAQ list: