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: