Install mmdetection under windows

mmdetection is a very useful toolkit for target detection. It provides a large number of pre-trained models. It is too troublesome to develop on the server, so I tried to install mmdetection on my windows computer, and finally ran out of the demo successfully, as follows:

The version information is as follows:

• Graphics card: RTX2070S
• System: Windows10
• miracles ： 11.1
• cudnn ： 8.1
• pytorch：1.8.0
• torchvision：0.9.0
• mmcv：1.3.9
• mmdetection：2.15.0

Install cuda and cudnn

Download cuda11.1 and install it. The download address is as follows:

CUDA Toolkit 11.1.0 | NVIDIA Developer

Download cudnn and copy it to the corresponding cuda directory. The download address is as follows:

cuDNN Archive | NVIDIA Developer

Those who are inconvenient to download can go to this address to download:

cuda11 and cudnn8.1.zip-Deep Learning Documentation Resources-CSDN Download

install mmdetection

• install pytorch

  Here we install pytorch 1.8.0

  pip3 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio===0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
  

  As a rule, your torchvision will generally be one version number higher than your torch.

• install mmcv

  mmcv is the underlying support of the mm series, including the installation of mmclassification and mmsegmentation. mmcv is used. There are two installation versions, one is a simplified version mmcvand the other is a complete version mmcv-full. The complete version contains a large number of operators. Under the GPU It is better to install this full version, the installation command is as follows:

  pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
  

• install mmdetection

  The versions of mmdetection and mmcv should pay attention to the correspondence. The correspondence here is:

  MMDetection version	MMCV version
  master	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

  In the above step, I installed mmcv 1.3.9, so I can directly install the latest version of mmdetection in this step, and perform the following series of operations:

  # install mmdetection
  git clone https://github.com/open-mmlab/mmdetection.git
  cd mmdetection
  pip install -r requirements/build.txt
  pip install -v -e .
  

test demo

First, you need to create a new checkpoints directory in the mmdetection directory, and download the weight of FasterRcnn to this folder. The download address is:

https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

On the command line, cd to the path of mmdetection and execute the following code

python demo/image_demo.py demo/demo.jpg configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

success!

In addition, if you need to run other model files, you can go to the following website to find the corresponding models and configuration files.

mmdetection/model_zoo.md at master · open-mmlab/mmdetection (github.com)