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今天开一个专栏，主要是从在服务器上安装Anaconda+Pycharm+Tensorflow+Pytorch开始，然后看【目标检测】相关的论文，相应地介绍详细的原理和实现，最后有时间的话再写一篇综述吧。目前主要是看Faster RCNN和YOLO v3-v5。
https://mmdetection.readthedocs.io/en/latest/tutorials/config.html
conda create -n open-mmlab python=3.7 -y
pip install torch-1.4.0-cp37-cp37m-manylinux1_x86_64.whl \Pillow-6.2.2-cp37-cp37m-manylinux1_x86_64.whl \six-1.14.0-py2.py3-none-any.whl \numpy-1.17.0-cp37-cp37m-manylinux1_x86_64.whl \opencv_python-4.2.0.34-cp37-cp37m-manylinux1_x86_64.whl \opencv_python_headless-4.2.0.34-cp37-cp37m-manylinux1_x86_64.whl \Shapely-1.7.0-cp37-cp37m-manylinux1_x86_64.whl \scipy-1.4.1-cp37-cp37m-manylinux1_x86_64.whl \Cython-0.29.16-cp37-cp37m-manylinux1_x86_64.whl \addict-2.2.1-py3-none-any.whl \imageio-2.8.0-py3-none-any.whl \python_dateutil-2.8.1-py2.py3-none-any.whl \cycler-0.10.0-py2.py3-none-any.whl \kiwisolver-1.3.1-cp37-cp37m-manylinux1_x86_64.whl \pyparsing-3.0.0a1-py3-none-any.whl \matplotlib-3.2.1-cp37-cp37m-manylinux1_x86_64.whl \PyWavelets-1.1.1-cp37-cp37m-manylinux1_x86_64.whl \networkx-2.4-py3-none-any.whl \decorator-4.4.2-py2.py3-none-any.whl \scikit_image-0.16.2-cp37-cp37m-manylinux1_x86_64.whl \torchvision-0.5.0-cp37-cp37m-linux_x86_64.whl \pytest_runner-5.2-py2.py3-none-any.whl \yapf-0.30.0-py2.py3-none-any.whl \imagecorruptions-1.1.0-py3-none-any.whl
cd PyYAML-5.3.1pip install -e .cd cocoapi-master/pycocotools/pip install -e .cd terminaltables-3.1.0/pip install -e .cd mmcv-0.4.4pip install -e .
pip install torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl torchvision-0.4.0-cp37-cp37m-manylinux1_x86_64.whl
sh clean.shrm -rf mmdet.egg-info/python setup.py develop
分布式训练测试命令：
CUDA_VISIBLE_DEVICES=4,5,6,7 tools/dist_train.sh configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py 4
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