• Linux or macOS (Windows is in experimental support)

  • Python 3.6+

  • PyTorch 1.3+

  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)

  • GCC 5+

  • MMCV

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

MMDetection version MMCV version
master mmcv-full>=1.3.3, <1.4.0
2.13.0 mmcv-full>=1.3.3, <1.4.0
2.12.0 mmcv-full>=1.3.3, <1.4.0
2.11.0 mmcv-full>=1.2.4, <1.4.0
2.10.0 mmcv-full>=1.2.4, <1.4.0
2.9.0 mmcv-full>=1.2.4, <1.4.0
2.8.0 mmcv-full>=1.2.4, <1.4.0
2.7.0 mmcv-full>=1.1.5, <1.4.0
2.6.0 mmcv-full>=1.1.5, <1.4.0
2.5.0 mmcv-full>=1.1.5, <1.4.0
2.4.0 mmcv-full>=1.1.1, <1.4.0
2.3.0 mmcv-full==1.0.5
2.3.0rc0 mmcv-full>=1.0.2
2.2.1 mmcv==0.6.2
2.2.0 mmcv==0.6.2
2.1.0 mmcv>=0.5.9, <=0.6.1
2.0.0 mmcv>=0.5.1, <=0.5.8

Note: 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.


  1. You can simply install mmdetection with the following commands: pip install mmdet

  2. Create a conda virtual environment and activate it.

    conda create -n open-mmlab python=3.7 -y
    conda activate open-mmlab
  3. 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.

  4. Install mmcv-full, we recommend you to install the pre-build package 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

    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 will be installed after this step
    cd ..

    Or directly run

    pip install mmcv-full
  5. Clone the MMDetection repository.

    git clone
    cd mmdetection
  6. Install build requirements and then install MMDetection.

    pip install -r requirements/build.txt
    pip install -v -e .  # or "python develop"


a. Following the above instructions, MMDetection is installed on dev mode , any local modifications made to the code will take effect without the need to reinstall it.

b. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

c. Some dependencies are optional. Simply running pip install -v -e . will only install the minimum runtime requirements. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional]). Valid keys for the extras field are: all, tests, build, and optional.

Install with CPU only

The code can be built for CPU only environment (where CUDA isn’t available).

In CPU mode you can run the demo/ for example. However some functionality is gone in this mode:

  • Deformable Convolution

  • Modulated Deformable Convolution

  • ROI pooling

  • Deformable ROI pooling

  • CARAFE: Content-Aware ReAssembly of FEatures

  • SyncBatchNorm

  • CrissCrossAttention: Criss-Cross Attention

  • MaskedConv2d

  • Temporal Interlace Shift

  • nms_cuda

  • sigmoid_focal_loss_cuda

  • bbox_overlaps

So if you try to run inference with a model containing above ops you will get an error. The following table lists the related methods that cannot inference on CPU due to dependency on these operators

Operator Model
Deformable Convolution/Modulated Deformable Convolution DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS
MaskedConv2d Guided Anchoring
SyncBatchNorm ResNeSt

Notice: MMDetection does not support training with CPU for now.

Another option: Docker Image

We provide a Dockerfile to build an image. Ensure that you are using docker version >=19.03.

# build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmdetection docker/

Run it with

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

A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda.

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

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y

# install the latest mmcv
pip install mmcv-full==latest+torch1.6.0+cu101 -f

# install mmdetection
git clone
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .

Developing with multiple MMDetection versions

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

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

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


To verify whether MMDetection and the required environment are installed correctly, we can run sample Python code to initialize a detector and run inference a demo image:

from mmdet.apis import init_detector, inference_detector

config_file = 'configs/faster_rcnn/'
# download the checkpoint from model zoo and put it in `checkpoints/`
# url:
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
device = 'cuda:0'
# init a detector
model = init_detector(config_file, checkpoint_file, device=device)
# inference the demo image
inference_detector(model, 'demo/demo.jpg')

The above code is supposed to run successfully upon you finish the installation.