You can use tools/train.py to train a model on a single machine with a CPU and optionally a GPU. Here is the full usage of the script. |
Install MMPose¶. We recommend to use a conda environment to install mmpose and its dependencies. And compilers nvcc and gcc are required. |
To train a model on a customized dataset with MMPose, there are usually three steps: Support the dataset in MMPose; Create a config; Perform training and ... |
MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project. The main branch works with PyTorch 1.8+. |
In this document, we will give a guide on the process of preparing datasets for the MMPose. Various aspects of dataset preparation will be discussed. |
Train a model. MMPose implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel respectively. We ... |
Inference with an MMPose model. MMPose provides high-level APIs for model inference and training. |
Comparison of MMPose models before and after training, evaluated on PoseFES for 13 KPs. Top diagram shows evaluation AP on Sc1 and bottom diagram shows ... |
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