• Python 3.6+

  • PyTorch 1.5+

  • MMCV

The compatible MMClassification and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.

MMClassification version MMCV version
master mmcv>=1.3.16, <=1.5.0
0.18.0 mmcv>=1.3.16, <=1.5.0
0.17.0 mmcv>=1.3.8, <=1.5.0
0.16.0 mmcv>=1.3.8, <=1.5.0
0.15.0 mmcv>=1.3.8, <=1.5.0
0.15.0 mmcv>=1.3.8, <=1.5.0
0.14.0 mmcv>=1.3.8, <=1.5.0
0.13.0 mmcv>=1.3.8, <=1.5.0
0.12.0 mmcv>=1.3.1, <=1.5.0
0.11.1 mmcv>=1.3.1, <=1.5.0
0.11.0 mmcv>=1.3.0
0.10.0 mmcv>=1.3.0
0.9.0 mmcv>=1.1.4
0.8.0 mmcv>=1.1.4
0.7.0 mmcv>=1.1.4
0.6.0 mmcv>=1.1.4


Since the master branch is under frequent development, the mmcv version dependency may be inaccurate. If you encounter problems when using the master branch, please try to update mmcv to the latest version.

Install MMClassification

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.8 -y
conda activate open-mmlab

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

conda install pytorch torchvision -c pytorch


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 1.5.1, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch

E.g.2 If you have CUDA 11.3 installed under /usr/local/cuda and would like to install PyTorch 1.10.0., you need to install the prebuilt PyTorch with CUDA 11.3.

conda install pytorch==1.10.0 torchvision==0.11.1 cudatoolkit=11.3 -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 MMClassification repository.

Release version

We recommend you to install MMClassification with MIM.

pip install git+
mim install mmcls

MIM can automatically install OpenMMLab projects and their requirements, and it can also help us to train, parameter search and pretrain model download.

Or, you can install MMClassification with pip:

pip install mmcls

Develop version

First, clone the MMClassification repository.

git clone
cd mmclassification

And then, install build requirements and install MMClassification.

pip install -e .  # or "python develop"


Following 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).

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 mmcls:torch1.6.0-cuda10.1-cudnn7 .


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

Run a container built from mmcls image with command:

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/workspace/mmclassification/data mmcls:torch1.6.0-cuda10.1-cudnn7 /bin/bash

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
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