• Python 3.6+
  • PyTorch 1.3+
  • mmcv 1.1.4+

Install MMClassification

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 pacakge, you can use more CUDA versions such as 9.0.

c. Clone the mmclassification repository.

git clone
cd mmclassification

d. Install build requirements and then install mmclassification.

pip install -e .  # or "python develop"


  1. Following the above instructions, mmclassification 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).
  2. If you would like to use opencv-python-headless instead of opencv-python,

you can install it before installing mmcv.

Using multiple MMClassification versions

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

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

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