yolov5快速安装环境

1.打开Anaconda Prompt，进入base 环境

2.在base环境下，创建yolov5的环境。

(base) C:\Users\Administrator>conda create -n yolov550 python=3.7 //yolov550为自己命名的环境名称，python=3.7 指定python的版本为3.7
(base) C:\Users\Administrator>conda activate yolov550   //激活yolov550 环境
(base) C:\Users\Administrator>conda activate yolov550
(yolov550) C:\Users\Administrator>E:
(yolov550) E:\>cd E:\Anaconda3\envs\yolov550\Lib\site-packages   //使用pip 安装需要进入当前环境名称的site_packages文件下。这样使用conda list 才能显示安装的包

3.使用requirements 安装环境

(yolov550) E:\Anaconda3\envs\yolov550\Lib\site-packages>pip install -r D:/liufq/yolov5prune-main/requirements.txt  //D:/liufq/yolov5prune-main/requirements.txt为requirements.txt的绝对路径

4.安装结果如下：

Successfully installed Pillow-8.3.2 PyYAML-5.4.1 absl-py-0.13.0 attr-0.3.1 attrs-21.2.0 cachetools-4.2.2 charset-normalizer-2.0.4 colorama-0.4.4 coremltools-4.1 cycler-0.10.0 cython-0.29.24 google-auth-1.35.0 google-auth-oauthlib-0.4.6 grpcio-1.39.0 idna-3.2 importlib-metadata-4.8.1 kiwisolver-1.3.2 markdown-3.3.4 matplotlib-3.4.3 mpmath-1.2.1 numpy-1.19.5 oauthlib-3.1.1 onnx-1.10.1 opencv-python-4.5.3.56 packaging-21.0 pandas-1.3.2 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pycocotools-2.0.2 pyparsing-2.4.7 python-dateutil-2.8.2 pytz-2021.1 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 scikit-learn-0.19.2 scipy-1.7.1 seaborn-0.11.2 six-1.16.0 sympy-1.8 tensorboard-2.6.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.0 thop-0.0.31.post2005241907 torch-1.9.0 torchvision-0.10.0 tqdm-4.62.2 typing-extensions-3.10.0.2 urllib3-1.26.6 werkzeug-2.0.1 zipp-3.5.0

5.该版本安装的为cpu的环境，如果需要使用GPU。需要删除cpu的torch、torchvison，重新安装cuda版本的torch、torchvision

(yolov550) E:\Anaconda3\envs\yolov550\Lib\site-packages>pip uninstall torch torchvision
Found existing installation: torch 1.9.0
Uninstalling torch-1.9.0:
  Would remove:
    e:\anaconda3\envs\yolov550\lib\site-packages\caffe2\*
    e:\anaconda3\envs\yolov550\lib\site-packages\torch-1.9.0.dist-info\*
    e:\anaconda3\envs\yolov550\lib\site-packages\torch\*
    e:\anaconda3\envs\yolov550\scripts\convert-caffe2-to-onnx.exe
    e:\anaconda3\envs\yolov550\scripts\convert-onnx-to-caffe2.exe
Proceed (y/n)? y
  Successfully uninstalled torch-1.9.0
Found existing installation: torchvision 0.10.0
Uninstalling torchvision-0.10.0:
  Would remove:
    e:\anaconda3\envs\yolov550\lib\site-packages\torchvision-0.10.0.dist-info\*
    e:\anaconda3\envs\yolov550\lib\site-packages\torchvision\*
Proceed (y/n)? y
  Successfully uninstalled torchvision-0.10.0

6.进入pytorch 官网，选择对应的操作系统和计算平台，如图。

pip3 install torch==1.9.0+cu102 torchvision==0.10.0+cu102 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

7.安装cuda 和cudnn，有前辈说先安装cuda和cudnn，再安装torch、torchvision。经测试先torch、torchvision不影响。

(yolov550) E:\Anaconda3\envs\yolov550\Lib\site-packages>conda install cudatoolkit==10.2.89 
(yolov550) E:\Anaconda3\envs\yolov550\Lib\site-packages>conda install cudnn==7.6.5

8.测试cuda 是否可用

(yolov550) E:\Anaconda3\envs\yolov550\Lib\site-packages>python
Python 3.7.11 (default, Jul 27 2021, 09:42:29) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.cuda.is_available())
True  //表示torch 调用cuda成功
>>> exit() //退出

9.测试例子程序

python detect.py --source ./data/images/ --weights ./weights/yolov5s.pt --conf 0.4