• Linux (Windows is not officially supported)
  • Python 3.6+
  • PyTorch 1.3+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • mmcv (Please install the latest version of mmcv-full)
  • Numpy
  • cv2
  • json_tricks
  • xtcocotools


  • mmdet (to run pose demos)
  • mmtrack (to run pose tracking demos)

Install MMPose

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install PyTorch and torchvision following the official instructions, e.g.,

conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

E.g.2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.

c. Install mmcv, we recommend you to install the pre-build mmcv as below.

pip install mmcv-full -f{cu_version}/{torch_version}/index.html

Please replace {cu_version} and {torch_version} in the url to your desired one. For example, to install the latest mmcv-full with CUDA 11 and PyTorch 1.7.0, use the following command:

pip install mmcv-full -f

If it compiles during installation, then please check that the cuda version and pytorch version **exactly”” matches the version in the mmcv-full installation command. For example, pytorch 1.7.0 and 1.7.1 are treated differently. See here for different versions of MMCV compatible to different PyTorch and CUDA versions.

Optionally you can choose to compile mmcv from source by the following command

git clone
cd mmcv
MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full, which contains cuda ops, will be installed after this step
# OR pip install -e .  # package mmcv, which contains no cuda ops, will be installed after this step
cd ..

Or directly run

pip install mmcv-full
# alternative: pip install mmcv

Important: You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

d. Clone the mmpose repository

git clone
cd mmpose

e. Install build requirements and then install mmpose

pip install -r requirements.txt
python develop


  1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
  2. Following the above instructions, mmpose is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
  3. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.
  4. If you have mmcv installed, you need to firstly uninstall mmcv, and then install mmcv-full.

A from-scratch setup script

Here is a full script for setting up mmpose with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT).

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y

# install the latest mmcv-full or mmcv, here we take mmcv-full as example
pip install mmcv-full

# install mmpose
git clone
cd mmpose
pip install -r requirements.txt
python develop

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7.
docker build -f ./docker/Dockerfile --rm -t mmpose .

Important: Make sure you’ve installed the nvidia-container-toolkit.

Run the following cmd:

docker run --gpus all\
 --shm-size=8g \
 -it -v {DATA_DIR}:/mmpose/data mmpose

Developing with multiple MMPose versions

The train and test scripts already modify the PYTHONPATH to ensure the script use the MMPose in the current directory.

To use the default MMPose installed in the environment rather than that you are working with, you can remove the following line in those scripts.

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH