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Installation

Best Practices

Step 0. Install MMEngine and MMCV using MIM.

pip install -U openmim
mim install "mmengine>=0.6.0"
mim install "mmcv>=2.0.0rc4,<2.1.0"
mim install "mmdet>=3.0.0rc6,<3.1.0"

If you are currently in the mmyolo project directory, you can use the following simplified commands

cd mmyolo
pip install -U openmom
mim install -r requirements/mminstall.txt

Note:

a. 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.

b. If you would like to use albumentations, we suggest using pip install -r requirements/albu.txt or pip install -U albumentations --no-binary qudida,albumentations. If you simply use pip install albumentations==1.0.1, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). We recommended checking the environment after installing albumentation to ensure that opencv-python and opencv-python-headless are not installed at the same time, because it might cause unexpected issues if they both installed. Please refer to official documentation for more details.

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
# Install albumentations
pip install -r requirements/albu.txt
# Install MMYOLO
mim 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 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_fast_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_fast_8xb16-300e_coco.py \
                          yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth

# Optional parameters
# --out-dir ./output *The detection results are output to the specified directory. When args have action --show, the script do not save results. Default: ./output
# --device cuda:0    *The computing resources used, including cuda and cpu. Default: cuda:0
# --show             *Display the results on the screen. Default: False
# --score-thr 0.3    *Confidence threshold. Default: 0.3

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

Supported input types:

  • Single image, include jpg, jpeg, png, ppm, bmp, pgm, tif, tiff, webp.
  • Folder, all image files in the folder will be traversed and the corresponding results will be output.
  • URL, will automatically download from the URL and the corresponding results will be output.

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

config_file = 'yolov5_s-v61_syncbn_fast_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.

Using MMYOLO with Docker

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

Reminder: If you find out that your download speed is very slow, we suggest canceling the comments in the last two lines of Optional in the Dockerfile to obtain a rocket like download speed:

# (Optional)
RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list && \
    pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

Build Command：

# build an image with PyTorch 1.9, CUDA 11.1
# If you prefer other versions, just modified the Dockerfile
docker build -t mmyolo docker/

Run it with:

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

For other customized inatallation, see Customized Installation

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.