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Installation

Requirements

  • Linux (Windows is not officially supported)
  • Python 3.5+
  • PyTorch 1.1 or higher
  • CUDA 9.0 or higher
  • NCCL 2
  • GCC 4.9 or higher
  • mmcv

We have tested the following versions of OS and softwares:

  • OS: Ubuntu 16.04/18.04 and CentOS 7.2
  • CUDA: 9.0/9.2/10.0/10.1
  • NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
  • GCC(G++): 4.9/5.3/5.4/7.3

Install mmdetection

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

c. Clone the mmdetection repository.

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

d. Install build requirements and then install mmdetection. (We install pycocotools via the github repo instead of pypi because the pypi version is old and not compatible with the latest numpy.)

pip install -r requirements/build.txt
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
pip install -v -e .  # or "python setup.py develop"

Note:

  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, mmdetection 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. 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.

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.1, CUDA 10.0 and CUDNN 7.5
docker build -t mmdetection docker/

Prepare datasets

It is recommended to symlink the dataset root to $MMDETECTION/data. If your folder structure is different, you may need to change the corresponding paths in config files.

mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017
│   ├── cityscapes
│   │   ├── annotations
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── VOCdevkit
│   │   ├── VOC2007
│   │   ├── VOC2012

The cityscapes annotations have to be converted into the coco format using tools/convert_datasets/cityscapes.py:

pip install cityscapesscripts
python tools/convert_datasets/cityscapes.py ./data/cityscapes --nproc 8 --out_dir ./data/cityscapes/annotations

Current the config files in cityscapes use COCO pre-trained weights to initialize. You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training.

A from-scratch setup script

Here is a full script for setting up mmdetection 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

conda install -c pytorch pytorch torchvision -y
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
pip install -v -e .

mkdir data
ln -s $COCO_ROOT data

Using multiple MMDetection versions

If there are more than one mmdetection on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.

Another way is to insert the following code to the main scripts (train.py, test.py or any other scripts you run)

import os.path as osp
import sys
sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))

Or run the following command in the terminal of corresponding folder to temporally use the current one.

export PYTHONPATH=`pwd`:$PYTHONPATH