Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch.

MMDetection works on Linux, Windows and macOS. It requires Python 3.7+, CUDA 9.2+ and PyTorch 1.5+.

If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment and activate it.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch torchvision -c pytorch

On CPU platforms:

conda install pytorch torchvision cpuonly -c pytorch

Installation

We recommend that users follow our best practices to install MMDetection. However, the whole process is highly customizable. See Customize Installation section for more information.

Best Practices

Step 0. Install MMCV using MIM.

pip install -U openmim
mim install mmcv-full

Step 1. Install MMDetection.

Case a: If you develop and run mmdet directly, install it from source:

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.

Case b: If you use mmdet as a dependency or third-party package, install it with pip:

pip install mmdet

Verify the installation

To verify whether MMDetection is installed correctly, we provide some sample codes to run an inference demo.

Step 1. We need to download config and checkpoint files.

mim download mmdet --config yolov3_mobilenetv2_320_300e_coco --dest .

The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files yolov3_mobilenetv2_320_300e_coco.py and yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth in your current folder.

Step 2. Verify the inference demo.

Option (a). If you install mmdetection from source, just run the following command.

python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_320_300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg

You will see a new image result.jpg on your current folder, where bounding boxes are plotted on cars, benches, etc.

Option (b). If you install mmdetection with pip, open you python interpreter and copy&paste the following codes.

from mmdet.apis import init_detector, inference_detector

config_file = 'yolov3_mobilenetv2_320_300e_coco.py'
checkpoint_file = 'yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
inference_detector(model, 'demo/demo.jpg')

You will see a list of arrays printed, indicating the detected bounding boxes.

Customize Installation

CUDA versions

When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:

For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.

Please make sure the GPU driver satisfies the minimum version requirements. See this table for more information.

Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.

Install MMCV without MIM

MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.

To install MMCV with pip instead of MIM, please follow MMCV installation guides. This requires manually specifying a find-url based on PyTorch version and its CUDA version.

For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html

Install on CPU-only platforms

MMDetection can be built for CPU only environment. In CPU mode you can train (requires MMCV version >= 1.4.4), test or inference a model.

However some functionalities are gone in this mode:

Deformable Convolution
Modulated Deformable Convolution
ROI pooling
Deformable ROI pooling
CARAFE
SyncBatchNorm
CrissCrossAttention
MaskedConv2d
Temporal Interlace Shift
nms_cuda
sigmoid_focal_loss_cuda
bbox_overlaps

If you try to train/test/inference 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

Install on Google Colab

Google Colab usually has PyTorch installed, thus we only need to install MMCV and MMDetection with the following commands.

Step 1. Install MMCV using MIM.

!pip3 install openmim
!mim install mmcv-full

Step 2. Install MMDetection from the source.

!git clone https://github.com/open-mmlab/mmdetection.git
%cd mmdetection
!pip install -e .

Step 3. Verification.

import mmdet
print(mmdet.__version__)
# Example output: 2.23.0

Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.

Using MMDetection with Docker

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

# build an image with PyTorch 1.6, CUDA 10.1
# If you prefer other versions, just modified the Dockerfile
docker build -t mmdetection docker/

Run it with

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

Trouble shooting

If you have some issues during the installation, please first view the FAQ page. You may open an issue on GitHub if no solution is found.

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