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Get Started

Prerequisites

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

MMDetection works on Linux, Windows, and macOS. It requires Python 3.6+, CUDA 9.2+, and PyTorch 1.7+.

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

Best Practices

Step 0. Install MMEngine and MMCV using MIM.

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc1"
mim install "mmdet>=3.0.0rc0"

Note: In MMCV-v2.x, mmcv-full is rename to mmcv, if you want to install mmcv without CUDA ops, you can use mim install "mmcv-lite>=2.0.0rc1" to install the lite version.

Step 1. Install MMYOLO.

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

git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
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 MMYOLO as a dependency or third-party package, install it with MIM:

mim install "mmyolo"

Verify the installation

To verify whether MMYOLO 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 mmyolo --config yolov5_s-v61_syncbn_fast_8xb16-300e_coco --dest .

The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files yolov5_s-v61_syncbn_8xb16-300e_coco.py and yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth in your current folder.

Step 2. Verify the inference demo.

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

python demo/image_demo.py demo/demo.jpg yolov5_s-v61_syncbn_8xb16-300e_coco.py yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth --device cpu --out-file result.jpg

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

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

from mmdet.apis import init_detector, inference_detector
from mmyolo.utils import register_all_modules

register_all_modules()
config_file = 'yolov5_s-v61_syncbn_8xb16-300e_coco.py'
checkpoint_file = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.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 DetDataSample, and the predictions are in the pred_instance, indicating the detected bounding boxes, labels, and scores.

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 MMEngine without MIM

To install MMEngine with pip instead of MIM, please follow [MMEngine installation guides](https://mmengine.readthedocs.io/en/latest/get_started/installation.html).

For example, you can install MMEngine by the following command.

pip install mmengine

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 the PyTorch version and its CUDA version.

For example, the following command installs MMCV built for PyTorch 1.12.x and CUDA 11.6.

pip install "mmcv>=2.0.0rc1" -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.12.0/index.html

Install on CPU-only platforms

MMDetection can be built for the CPU-only environment. In CPU mode you can train (requires MMCV version >= 2.0.0rc1), test, or infer 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/infer a model containing the 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 MMEngine, MMCV, MMDetection, and MMYOLO with the following commands.

Step 1. Install MMEngine and MMCV using MIM.

!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.0rc1,<2.1.0"
!mim install "mmdet>=3.0.0.rc0"

Step 2. Install MMYOLO from the source.

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

Step 3. Verification.

import mmyolo
print(mmyolo.__version__)
# Example output: 0.1.0, or an another version.

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 MMYOLO 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 mmyolo docker/

Run it with

DATA_DIR=/path/to/your/dataset
docker run --gpus all --shm-size=8g -it -v ${DATA_DIR}:/mmyolo/data mmyolo

Troubleshooting

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.

Develop using multiple MMYOLO versions

The training and testing scripts have been modified in PYTHONPATH to ensure that the scripts use MMYOLO in the current directory.

To have the default MMYOLO installed in your environment instead of what is currently in use, you can remove the code that appears in the relevant script:

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