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Prerequisites

  • 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

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

MMDetection version MMCV version
master mmcv-full>=1.3.17, <1.5.0
2.20.0 mmcv-full>=1.3.17, <1.5.0
2.19.1 mmcv-full>=1.3.17, <1.5.0
2.19.0 mmcv-full>=1.3.17, <1.5.0
2.18.0 mmcv-full>=1.3.17, <1.4.0
2.17.0 mmcv-full>=1.3.14, <1.4.0
2.16.0 mmcv-full>=1.3.8, <1.4.0
2.15.1 mmcv-full>=1.3.8, <1.4.0
2.15.0 mmcv-full>=1.3.8, <1.4.0
2.14.0 mmcv-full>=1.3.8, <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.

Installation

Prepare environment

  1. Create a conda virtual environment and activate it.

    conda create -n openmmlab python=3.7 -y
conda activate openmmlab

  2. 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.

Install MMDetection

It is recommended to install MMDetection with MIM, which automatically handle the dependencies of OpenMMLab projects, including mmcv and other python packages.

pip install openmim
mim install mmdet

Or you can still install MMDetection manually:

  1. Install mmcv-full.

    pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{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 11.0 and PyTorch 1.7.0, use the following command:

    pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html

    See here for different versions of MMCV compatible to different PyTorch and CUDA versions.

    Optionally you can compile mmcv from source if you need to develop both mmcv and mmdet. Refer to the guide for details.

  2. Install MMDetection.

    You can simply install mmdetection with the following command:

    pip install mmdet

    or clone the repository and then install it:

    git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

  3. Install extra dependencies for Instaboost, Panoptic Segmentation, LVIS dataset, or Albumentations.

    # for instaboost
pip install instaboostfast
# for panoptic segmentation
pip install git+https://github.com/cocodataset/panopticapi.git
# for LVIS dataset
pip install git+https://github.com/lvis-dataset/lvis-api.git
# for albumentations
pip install albumentations>=0.3.2 --no-binary imgaug,albumentations

Note:

a. When specifying -e or develop, MMDetection is installed on dev mode , any local modifications made to the code will take effect without reinstallation.

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.

d. If you would like to use albumentations, we suggest using pip install albumentations>=0.3.2 --no-binary imgaug,albumentations. If you simply use pip install albumentations>=0.3.2, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). We should not allow opencv-python and opencv-python-headless installed at the same time, because it might cause unexpected issues. Please refer to official documentation for more details.

Install without GPU support

MMDetection can be built for CPU only environment (where CUDA isn't available).

In CPU mode you can run the demo/webcam_demo.py 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

If you try to run inference with a model containing above ops, an error will be raised. The following table lists affected algorithms.

Operator Model
Deformable Convolution/Modulated Deformable Convolution DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS
MaskedConv2d Guided Anchoring
CARAFE CARAFE
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 openmmlab python=3.7 -y
conda activate openmmlab

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

# install the latest mmcv
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

# install mmdetection
git clone https://github.com/open-mmlab/mmdetection.git
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

Verification

To verify whether MMDetection is installed correctly, we can run the following sample code to initialize a detector and inference a demo image, but first we need to download config and checkpoint files.

mim download mmdet --config faster_rcnn_r50_fpn_1x_coco --dest .
from mmdet.apis import init_detector, inference_detector

config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
# url: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
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.