代码收藏家 yolov5环境配置
目录
背景
Windows系统下，()括号中为我安装的版本或者对版本解释
1、安装Anaconda3(我的版本)，配置好环境变量（不同版本环境变量文件可能不同）
2、安装电脑对应的显卡版本驱动(NVIDIA GeForce GTX 1050)
3、安装CUDA(10.2版本)，成功安装后再安装cuDNN(一定是对应于CUDA版本)
4、安装pytorch，配置pytorch环境，克隆yolov5包
1、Anaconda3安装
史上最全最详细的Anaconda安装教程
官网个人版：https://www.anaconda.com/products/distribution
镜像网站：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/
博主使用的版本是：
Anaconda3-5.2.0-Windows-x86_64.exe
为什么不用最新版的
Anaconda3-5.3.1-Windows-x86_64.exe
不知是版本原因还是什么原因，包括博主在内的一大堆使用这个最新版本在构建虚拟环境或者安装包时出现了这样蛋疼的错误
无法定位程序输入点 OPENSSL_sk_new_reserve 于动态链接库 E：\ProgramData\Anaconda3\Library\bin\libssl-1_1-x64.dll上
最后有博文指出回退3-5.2.0版本毛事木有
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版权声明：本文为CSDN博主「OSurer」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/wq_ocean_/article/details/103889237
(1)安装Anaconda3后,换源遇到的问题
在Anaconda Prompt终端中输入以下镜像源
#添加镜像源
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
#显示检索路径
conda config --set show_channel_urls yes
#显示镜像通道
conda config --show channels
#安装镜像
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
#配置好以上的镜像源，输入如下代码安装pytorch出错
#代码解释：创建一个名称为yolov5文件名的环境配置，名称可以自己取；python=3.8建立一个3.8版本的python环境
#该环境安装成功后位置在 D:\softwave\Anaconda3\envs\yolov5
conda create -n yolov5 python=3.8
#之后再输入 CUDA 10.2安装配置，同样安装出错
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
(2)处理方法
#删除之前的镜像源，恢复默认状态
conda config --remove-key channels
#找到下图文件位置
#如果不知道该文件位置可以打开Anaconda Prompt终端，就可以确定该文件位置
打开.condarc文件，默认时候如下
将.condarc文件内容修改成：
show_channel_urls: true
channels:
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
这便成功换源了
这里强调：在配置好pytorch环境之后再换源上面的代码，不然在
conda create -n pytorch1.8.0 python=3.8输入代码后会报错
所以在输入上面这段代码之前先不换源哦！！！！！！！！！！！
如果后续出现下载pytorch报错，说系统文件不存在，可以尝试使用这个置换.condarc文件
（删除 – http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge）
show_channel_urls: true
channels:
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
还有在换源之后，不要随便更新conda
conda update -n base -c defaults conda
上面这个命令，非必要不要执行，至少我执行就出问题了。因为这个更新使用的是conda默认的源，会和我们通过清华源安装的包冲突，这就会很麻烦
(3)Anaconda3环境变量配置
在系统变量path加入
C:\Windows\System32
D:\softwave\Anaconda3
D:\softwave\Anaconda3\Scripts
D:\softwave\Anaconda3\Library\mingw-w64\bin
D:\softwave\Anaconda3\Library\bin
在Anaconda Prompt终端输入
conda
结果如下表示环境配置成功
2、显卡驱动安装
从设备管理器找到自己的显卡型号，在驱动下载找到对应型号(NVIDIA GeForce GTX 1050)
NVIDIA 驱动下载：https://www.nvidia.cn/Download/index.aspx?lang=cn#
安装顺序按照NVIDIA安装包安装即可，安装路径最好默认，之后的环境变量好配置。
安装成功后，在cmd中输入：nvidia-smi
如果有错误：
‘nvidia-smi’ 不是内部或外部命令，也不是可运行的程序 或批处理文件。
把C:\Program Files\NVIDIA Corporation\NVSMI添加到环境变量的path中，记住不是系统变量，再重新打开cmd窗口。
(若无NVSMI文件，将NVSMI.zip解压到C:\Program Files\NVIDIA Corporation\即可
链接：https://pan.baidu.com/s/11zFYKpH0rYx9KMyuQt2rpQ
提取码：yz25)
红框内是显卡支持的最大CUDA版本,向下兼容，我安装的是CUDA 10.2版本。
3、安装CUDA
本人安装的10.2版本https://developer.nvidia.com/cuda-10.2-download-archive
官网地址：https://developer.nvidia.com/cuda-downloads
下载3个文件，后得到文件：cuda_10.2.89_441.22_win10.exe和2个同样是exe后缀的文件补丁包
(1)安装CUDA
安装时可以勾选Visual Studio Integration
(2) 安装cuDNN
cuDNN下载地址：https://developer.nvidia.com/rdp/cudnn-download
需要有账号(在cuDNN地址注册即可)
下载后得到文件：cudnn-windows-x86_64-8.7.0.84_cuda10-archive.zip
配置完CUDA环境后需要使用
(3)CUDA环境配置
计算机上点右键，打开属性->高级系统设置->环境变量，可以看到系统中多了CUDA_PATH和
CUDA_PATH_V10_2两个环境变量。(该变量是安装好CUDA自然形成的)
下面的配置都是【系统变量】
接下来，还要在系统中添加以下几个环境变量： 这是默认安装位置的路径:
CUDA_SDK_PATH = C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2
CUDA_LIB_PATH = %CUDA_PATH%\lib\x64 CUDA_BIN_PATH = %CUDA_PATH%\bin
CUDA_SDK_BIN_PATH = %CUDA_SDK_PATH%\bin\win64
CUDA_SDK_LIB_PATH = %CUDA_SDK_PATH%\common\lib\x64
在系统变量 Path 的末尾添加：
%CUDA_LIB_PATH%;%CUDA_BIN_PATH%;%CUDA_SDK_LIB_PATH%;%CUDA_SDK_BIN_PATH%;
继续添加如下5条（默认安装路径）：
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib\x64
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\CUPTI\lib64
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2\bin\win64
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2\common\lib\x64
复制cudnn文件
对于cudnn直接将其解开压缩包，然后需要将bin,include,lib中的文件复制粘贴到cuda的文件夹下
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
最后打开cmd，输入nvcc -V
如下图所示表示CUDA安装成功
显卡配置就算告一段落
4、安装pytorch，配置pytorch环境，克隆yolov5包
(1)安装pytorch
打开Anaconda Prompt终端
#代码解释：创建一个名称为yolov5文件名的环境配置，名称可以自己取；python=3.8建立一个3.8版本的python环境
#该环境安装成功后位置在 D:\softwave\Anaconda3\envs\yolov5
#输入
conda create -n yolov5 python=3.8
#成功安装后继续输入如下代码
conda activate yolov5
#表示在创建的yolov5环境下执行后续程序
#该环境地址前文所讲在Anaconda3安装路径下 D:\softwave\Anaconda3\envs\yolov5
#继续输入代码
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2
#注释：与conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch区别
#带-c pytorch表示默认安装，下载速度慢可能安装不成功
#不带-c pytorch表示从配置的源安装，速度快安装成功率高
#目的安装pytorch头文件，torchvision头文件，cudatoolkit等头文件，
所有安装好的文件在D:\softwave\Anaconda3\envs\yolov5\Lib\site-packages下
pytorch-1.6.0 |py3.7_cuda102_cudnn7_0
torchvision-0.7.0 | py37_cu102
#这只是其中2个关键安装包，表示下载的安装包是基于CUDA运行的，运行的时候Using CUDA
#所有的安装包都是为了CUDA
#而如果直接运行conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
#安装后的配置文件运行Using CPU，安装的pytorch可能是为了配置CPU的包
根据CUDA版本要求，你可以安装不同的pytorch版本
旧地址：https://pytorch.org/get-started/previous-versions/
新地址：https://pytorch.org/get-started/locally/
CUDA旧版本界面
CUDA新版本界面
(2)检测是否安装成功
import torch # 如果pytorch安装成功即可导入
print(torch.cuda.is_available()) # 查看CUDA是否可用
print(torch.cuda.device_count()) # 查看可用的CUDA数量
print(torch.version.cuda) # 查看CUDA的版本号
结果显示如下则安装成功
print(torch.cuda.is_available()) 显示 True
print(torch.cuda.device_count()) 显示 1
print(torch.version.cuda) 显示CUDA版本
(3)yolov5-v3.1源码安装配置
下载yolov5-v3.1源码和权重文件，地址：https://github.com/ultralytics/yolov5/releases/tag/v3.1
如果下载失败可以直接进网盘：
链接：https://pan.baidu.com/s/16aWKDBAiZPTrQJFIS_Pc7A
提取码：yz25
如需其他版本，下载地址：https://github.com/ultralytics/yolov5
下载好之后，解压yolov5源码安装包（解压地址要记住）
将yolov5s.pt，yolov5m.pt，yolov5l.pt，yolov5x.pt权重文件，放置在weights文件夹下(该文件在yolov5代码压缩包下)
(4)测试yolov5环境代码
打开Anaconda Prompt终端转移到yolov5-v3.1压缩包位置下，转移方法如下图(conda activate yolov5)
#执行代码
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
#代码是将yolov5代码包requirements.txt记事本下的头文件包下载到
D:\softwave\Anaconda3\envs\yolov5\Lib\site-packages下
最后运行代码段测试
(运行环境)conda activate yolov5
(运行地址)(yolov5)D:\yolov5-v3.1>
python detect.py --source ./inference/images/ --weights weights/yolov5s.pt --conf 0.4
#代码表示处理yolov5-v3.1源码下inference文件内的2张图片，图像识别
#结果如下
Using CUDA表示为显卡运算
时间为处理的图片时间
最后表示处理的图片位置output
#表示处理成功，所有的文件配置完成，环境搭建成功！！！！！！!！！
完整安装步骤
(base) C:\Users\asus>conda create -n yolov5 python=3.8
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.5.4
latest version: 23.1.0
Please update conda by running
$ conda update -n base conda
## Package Plan ##
environment location: D:\softwave\Anaconda3\envs\yolov5
added / updated specs:
- python=3.8
The following NEW packages will be INSTALLED:
bzip2: 1.0.8-h8ffe710_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ca-certificates: 2022.9.24-h5b45459_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libffi: 3.4.2-h8ffe710_5 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libsqlite: 3.40.0-hcfcfb64_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libzlib: 1.2.13-hcfcfb64_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl: 3.0.7-hcfcfb64_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pip: 22.3.1-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python: 3.8.13-hcf16a7b_0_cpython http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
setuptools: 65.5.1-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
sqlite: 3.40.0-hcfcfb64_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tk: 8.6.12-h8ffe710_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ucrt: 10.0.22621.0-h57928b3_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vc: 14.3-h3d8a991_9 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vs2015_runtime: 14.32.31332-h1d6e394_9 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wheel: 0.38.4-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz: 5.2.6-h8d14728_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: failed
ERROR conda.core.link:_execute(502): An error occurred while installing package 'http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge::setuptools-65.5.1-pyhd8ed1ab_0'.
FileNotFoundError(2, '系统找不到指定的文件。', None, 2, None)
Attempting to roll back.
Rolling back transaction: done
FileNotFoundError(2, '系统找不到指定的文件。', None, 2, None)
(base) C:\Users\asus>conda activate yolov5
(yolov5) C:\Users\asus>conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.5.4
latest version: 23.1.0
Please update conda by running
$ conda update -n base conda
## Package Plan ##
environment location: D:\softwave\Anaconda3\envs\yolov5
added / updated specs:
- cudatoolkit=10.2
- pytorch==1.6.0
- torchvision==0.7.0
The following packages will be downloaded:
package | build
---------------------------|-----------------
msys2-conda-epoch-20160418 | 1 2 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
xorg-libxdmcp-1.1.3 | hcd874cb_0 66 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-3.7.1 | h9460c21_1003 20.2 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lerc-4.0.0 | h63175ca_0 190 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libwebp-base-1.2.4 | h8ffe710_0 328 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libs-5.3.0 | 7 518 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
tbb-2021.7.0 | h91493d7_0 174 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapacke-3.9.0 | 16_win64_mkl 5.6 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
intel-openmp-2022.1.0 | h57928b3_3787 3.7 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
numpy-1.21.6 | py37h2830a78_0 5.3 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd-1.5.2 | h7755175_4 401 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
torchvision-0.7.0 | py37_cu102 6.4 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
libpng-1.6.38 | h19919ed_0 773 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libcblas-3.9.0 | 16_win64_mkl 5.6 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
blas-devel-3.9.0 | 16_win64_mkl 13 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
blas-2.116 | mkl 14 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl-include-2022.1.0 | h6a75c08_874 760 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-libwinpthread-git-5.0.0.4634.697f757| 2 30 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
jpeg-9e | h8ffe710_2 366 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python_abi-3.7 | 2_cp37m 4 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libs-core-5.3.0 | 7 213 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
mkl-devel-2022.1.0 | h57928b3_875 7.1 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytorch-1.6.0 |py3.7_cuda102_cudnn7_0 705.3 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
pillow-9.2.0 | py37h42a8222_2 45.4 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openjpeg-2.5.0 | hc9384bd_1 256 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapack-3.9.0 | 16_win64_mkl 5.6 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libdeflate-1.14 | hcfcfb64_0 73 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
freetype-2.12.1 | h546665d_0 506 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gmp-6.1.0 | 2 689 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
libxcb-1.13 | hcd874cb_1004 1.3 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libgfortran-5.3.0| 6 340 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
pthread-stubs-0.4 | hcd874cb_1001 6 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxau-1.0.9 | hcd874cb_0 57 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl-2022.1.0 | h6a75c08_874 182.7 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lcms2-2.14 | h90d422f_0 988 KB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas-3.9.0 | 16_win64_mkl 5.6 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff-4.4.0 | h8e97e67_4 1.1 MB http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
------------------------------------------------------------
Total: 1007.4 MB
The following NEW packages will be INSTALLED:
blas: 2.116-mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
blas-devel: 3.9.0-16_win64_mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cudatoolkit: 10.2.89-hb195166_10 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
freetype: 2.12.1-h546665d_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
intel-openmp: 2022.1.0-h57928b3_3787 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
jpeg: 9e-h8ffe710_2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lcms2: 2.14-h90d422f_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lerc: 4.0.0-h63175ca_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas: 3.9.0-16_win64_mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libcblas: 3.9.0-16_win64_mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libdeflate: 1.14-hcfcfb64_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapack: 3.9.0-16_win64_mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapacke: 3.9.0-16_win64_mkl http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libpng: 1.6.38-h19919ed_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff: 4.4.0-h8e97e67_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libwebp-base: 1.2.4-h8ffe710_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libxcb: 1.13-hcd874cb_1004 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libzlib: 1.2.13-hcfcfb64_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libgfortran: 5.3.0-6 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gcc-libs: 5.3.0-7 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gcc-libs-core: 5.3.0-7 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gmp: 6.1.0-2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-libwinpthread-git: 5.0.0.4634.697f757-2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
mkl: 2022.1.0-h6a75c08_874 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl-devel: 2022.1.0-h57928b3_875 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl-include: 2022.1.0-h6a75c08_874 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
msys2-conda-epoch: 20160418-1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
ninja: 1.11.0-h2d74725_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
numpy: 1.21.6-py37h2830a78_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openjpeg: 2.5.0-hc9384bd_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pillow: 9.2.0-py37h42a8222_2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pip: 22.3.1-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pthread-stubs: 0.4-hcd874cb_1001 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python: 3.7.1-h9460c21_1003 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python_abi: 3.7-2_cp37m http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytorch: 1.6.0-py3.7_cuda102_cudnn7_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
setuptools: 65.5.1-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tbb: 2021.7.0-h91493d7_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tk: 8.6.12-h8ffe710_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
torchvision: 0.7.0-py37_cu102 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
ucrt: 10.0.22621.0-h57928b3_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vc: 14.3-h3d8a991_9 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vs2015_runtime: 14.32.31332-h1d6e394_9 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wheel: 0.38.4-pyhd8ed1ab_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxau: 1.0.9-hcd874cb_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxdmcp: 1.1.3-hcd874cb_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz: 5.2.6-h8d14728_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd: 1.5.2-h7755175_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
Proceed ([y]/n)? y
Downloading and Extracting Packages
msys2-conda-epoch-20 | 2 KB | ######################################################################################################## | 100%
xorg-libxdmcp-1.1.3 | 66 KB | ######################################################################################################## | 100%
python-3.7.1 | 20.2 MB | ######################################################################################################## | 100%
lerc-4.0.0 | 190 KB | ######################################################################################################## | 100%
libwebp-base-1.2.4 | 328 KB | ######################################################################################################## | 100%
m2w64-gcc-libs-5.3.0 | 518 KB | ######################################################################################################## | 100%
tbb-2021.7.0 | 174 KB | ######################################################################################################## | 100%
liblapacke-3.9.0 | 5.6 MB | ######################################################################################################## | 100%
intel-openmp-2022.1. | 3.7 MB | ######################################################################################################## | 100%
numpy-1.21.6 | 5.3 MB | ######################################################################################################## | 100%
zstd-1.5.2 | 401 KB | ######################################################################################################## | 100%
torchvision-0.7.0 | 6.4 MB | ######################################################################################################## | 100%
libpng-1.6.38 | 773 KB | ######################################################################################################## | 100%
libcblas-3.9.0 | 5.6 MB | ######################################################################################################## | 100%
blas-devel-3.9.0 | 13 KB | ######################################################################################################## | 100%
blas-2.116 | 14 KB | ######################################################################################################## | 100%
mkl-include-2022.1.0 | 760 KB | ######################################################################################################## | 100%
m2w64-libwinpthread- | 30 KB | ######################################################################################################## | 100%
jpeg-9e | 366 KB | ######################################################################################################## | 100%
python_abi-3.7 | 4 KB | ######################################################################################################## | 100%
m2w64-gcc-libs-core- | 213 KB | ######################################################################################################## | 100%
mkl-devel-2022.1.0 | 7.1 MB | ######################################################################################################## | 100%
pytorch-1.6.0 | 705.3 MB | ####################################################################################################### | 100%
pillow-9.2.0 | 45.4 MB | ######################################################################################################## | 100%
openjpeg-2.5.0 | 256 KB | ######################################################################################################## | 100%
liblapack-3.9.0 | 5.6 MB | ######################################################################################################## | 100%
libdeflate-1.14 | 73 KB | ######################################################################################################## | 100%
freetype-2.12.1 | 506 KB | ######################################################################################################## | 100%
m2w64-gmp-6.1.0 | 689 KB | ######################################################################################################## | 100%
libxcb-1.13 | 1.3 MB | ######################################################################################################## | 100%
m2w64-gcc-libgfortra | 340 KB | ######################################################################################################## | 100%
pthread-stubs-0.4 | 6 KB | ######################################################################################################## | 100%
xorg-libxau-1.0.9 | 57 KB | ######################################################################################################## | 100%
mkl-2022.1.0 | 182.7 MB | ####################################################################################################### | 100%
lcms2-2.14 | 988 KB | ######################################################################################################## | 100%
libblas-3.9.0 | 5.6 MB | ######################################################################################################## | 100%
libtiff-4.4.0 | 1.1 MB | ######################################################################################################## | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: \ "By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html"
done
(yolov5) C:\Users\asus>d:
(yolov5) D:\>cd yolov5-v3.1
(yolov5) D:\yolov5-v3.1>pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting Cython
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/56/3a/e59db3769dee48409c759a88b62cd605324e05d396e10af0a065adc956ad/Cython-0.29.33-py2.py3-none-any.whl (987 kB)
Collecting matplotlib>=3.2.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/df/3f/6093a23565d0f50ce433f56223fcc34af6c912cd4331dc582ba29d9b5a17/matplotlib-3.5.3-cp37-cp37m-win_amd64.whl (7.2 MB)
---------------------------------------- 7.2/7.2 MB 3.3 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.18.5 in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from -r requirements.txt (line 6)) (1.21.6)
Collecting opencv-python>=4.1.2
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/80/5b/6eee3a1dc0f296904f44a13749f3b2cd29569c817aa931ead50c4d085d51/opencv_python-4.7.0.68-cp37-abi3-win_amd64.whl (38.2 MB)
Requirement already satisfied: pillow in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from -r requirements.txt (line 8)) (9.2.0)
Collecting PyYAML>=5.3
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d1/c0/4fe04181b0210ee2647cfbb89ecd10a36eef89f10d8aca6a192c201bbe58/PyYAML-6.0-cp37-cp37m-win_amd64.whl (153 kB)
---------------------------------------- 153.2/153.2 kB 4.6 MB/s eta 0:00:00
Collecting scipy>=1.4.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/40/69/4af412d078cef2298f7d90546fa0e03e65a032558bd85319239c72ae0c3c/scipy-1.7.3-cp37-cp37m-win_amd64.whl (34.1 MB)
---------------------------------------- 34.1/34.1 MB 3.8 MB/s eta 0:00:00
Collecting tensorboard>=2.2
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6f/77/e624b4916531721e674aa105151ffa5223fb224d3ca4bd5c10574664f944/tensorboard-2.11.2-py3-none-any.whl (6.0 MB)
Requirement already satisfied: torch>=1.6.0 in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from -r requirements.txt (line 12)) (1.6.0)
Requirement already satisfied: torchvision>=0.7.0 in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from -r requirements.txt (line 13)) (0.7.0)
Collecting tqdm>=4.41.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/47/bb/849011636c4da2e44f1253cd927cfb20ada4374d8b3a4e425416e84900cc/tqdm-4.64.1-py2.py3-none-any.whl (78 kB)
Collecting pyparsing>=2.2.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl (98 kB)
Collecting fonttools>=4.22.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e3/d9/e9bae85e84737e76ebbcbea13607236da0c0699baed0ae4f1151b728a608/fonttools-4.38.0-py3-none-any.whl (965 kB)
Collecting cycler>=0.10
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5c/f9/695d6bedebd747e5eb0fe8fad57b72fdf25411273a39791cde838d5a8f51/cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting kiwisolver>=1.0.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/03/93/11790e8e81b89acd3a1c8a6b501f8a05b1c41beee0990582699cdda29557/kiwisolver-1.4.4-cp37-cp37m-win_amd64.whl (54 kB)
---------------------------------------- 54.9/54.9 kB 178.6 kB/s eta 0:00:00
Collecting packaging>=20.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ed/35/a31aed2993e398f6b09a790a181a7927eb14610ee8bbf02dc14d31677f1c/packaging-23.0-py3-none-any.whl (42 kB)
Collecting python-dateutil>=2.7
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Requirement already satisfied: wheel>=0.26 in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from tensorboard>=2.2->-r requirements.txt (line 11)) (0.38.4)
Collecting requests<3,>=2.21.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d2/f4/274d1dbe96b41cf4e0efb70cbced278ffd61b5c7bb70338b62af94ccb25b/requests-2.28.2-py3-none-any.whl (62 kB)
Collecting markdown>=2.6.8
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/86/be/ad281f7a3686b38dd8a307fa33210cdf2130404dfef668a37a4166d737ca/Markdown-3.4.1-py3-none-any.whl (93 kB)
Collecting werkzeug>=1.0.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/c8/27/be6ddbcf60115305205de79c29004a0c6bc53cec814f733467b1bb89386d/Werkzeug-2.2.2-py3-none-any.whl (232 kB)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/74/69/5747a957f95e2e1d252ca41476ae40ce79d70d38151d2e494feb7722860c/tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)
Collecting google-auth<3,>=1.6.3
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fb/55/c6e13b79a16688069b214cf726ebe49725c0b936367f045464b1122de083/google_auth-2.16.0-py2.py3-none-any.whl (177 kB)
Requirement already satisfied: setuptools>=41.0.0 in d:\softwave\anaconda3\envs\yolov5\lib\site-packages (from tensorboard>=2.2->-r requirements.txt (line 11)) (65.5.1)
Collecting google-auth-oauthlib<0.5,>=0.4.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b1/0e/0636cc1448a7abc444fb1b3a63655e294e0d2d49092dc3de05241be6d43c/google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
Collecting tensorboard-plugin-wit>=1.6.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e0/68/e8ecfac5dd594b676c23a7f07ea34c197d7d69b3313afdf8ac1b0a9905a2/tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)
Collecting absl-py>=0.4
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/dd/87/de5c32fa1b1c6c3305d576e299801d8655c175ca9557019906247b994331/absl_py-1.4.0-py3-none-any.whl (126 kB)
Collecting protobuf<4,>=3.9.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/98/07/4c75a689fa173c12b92c9a64a82efad44797b9b2b784c8562f36ab28b551/protobuf-3.20.3-cp37-cp37m-win_amd64.whl (905 kB)
---------------------------------------- 905.1/905.1 kB 511.4 kB/s eta 0:00:00
Collecting grpcio>=1.24.3
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f0/59/84b9868896468cccbb644f9a4e3a25226f70e4e6b7e2dab503c81dfb8c59/grpcio-1.51.1-cp37-cp37m-win_amd64.whl (3.7 MB)
---------------------------------------- 3.7/3.7 MB 1.4 MB/s eta 0:00:00
Collecting future
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8f/2e/cf6accf7415237d6faeeebdc7832023c90e0282aa16fd3263db0eb4715ec/future-0.18.3.tar.gz (840 kB)
Preparing metadata (setup.py) ... done
Collecting colorama
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl (25 kB)
Collecting cachetools<6.0,>=2.0.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/db/14/2b48a834d349eee94677e8702ea2ef98b7c674b090153ea8d3f6a788584e/cachetools-5.3.0-py3-none-any.whl (9.3 kB)
Collecting six>=1.9.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting pyasn1-modules>=0.2.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/95/de/214830a981892a3e286c3794f41ae67a4495df1108c3da8a9f62159b9a9d/pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
Collecting rsa<5,>=3.1.4
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/49/97/fa78e3d2f65c02c8e1268b9aba606569fe97f6c8f7c2d74394553347c145/rsa-4.9-py3-none-any.whl (34 kB)
Collecting requests-oauthlib>=0.7.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6f/bb/5deac77a9af870143c684ab46a7934038a53eb4aa975bc0687ed6ca2c610/requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)
Collecting typing-extensions
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0b/8e/f1a0a5a76cfef77e1eb6004cb49e5f8d72634da638420b9ea492ce8305e8/typing_extensions-4.4.0-py3-none-any.whl (26 kB)
Collecting importlib-metadata>=4.4
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/26/a7/9da7d5b23fc98ab3d424ac2c65613d63c1f401efb84ad50f2fa27b2caab4/importlib_metadata-6.0.0-py3-none-any.whl (21 kB)
Collecting idna<4,>=2.5
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fc/34/3030de6f1370931b9dbb4dad48f6ab1015ab1d32447850b9fc94e60097be/idna-3.4-py3-none-any.whl (61 kB)
Collecting urllib3<1.27,>=1.21.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fe/ca/466766e20b767ddb9b951202542310cba37ea5f2d792dae7589f1741af58/urllib3-1.26.14-py2.py3-none-any.whl (140 kB)
Collecting certifi>=2017.4.17
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/71/4c/3db2b8021bd6f2f0ceb0e088d6b2d49147671f25832fb17970e9b583d742/certifi-2022.12.7-py3-none-any.whl (155 kB)
---------------------------------------- 155.3/155.3 kB 132.6 kB/s eta 0:00:00
Collecting charset-normalizer<4,>=2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fc/64/443267b7824283b3e0e33cee4240c079939a970c2c9a5a3164fc988d690b/charset_normalizer-3.0.1-cp37-cp37m-win_amd64.whl (94 kB)
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Collecting MarkupSafe>=2.1.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/39/8d/5c5ce72deb8567ab48a18fbd99dc0af3dd651b6691b8570947e54a28e0f3/MarkupSafe-2.1.2-cp37-cp37m-win_amd64.whl (16 kB)
Collecting zipp>=0.5
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/01/3c/9d84fc1dbac1c5103bf3cd994e4895642001f75eb2139bddbc02aa1906e5/zipp-3.12.0-py3-none-any.whl (6.6 kB)
Collecting pyasn1<0.5.0,>=0.4.6
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/62/1e/a94a8d635fa3ce4cfc7f506003548d0a2447ae76fd5ca53932970fe3053f/pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
Collecting oauthlib>=3.0.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl (151 kB)
Building wheels for collected packages: future
Building wheel for future (setup.py) ... done
Created wheel for future: filename=future-0.18.3-py3-none-any.whl size=492055 sha256=d9728aa33a2dbdf410b14c5817726ad95cca5497184b62b4a11ba0eedc44eaf6
Stored in directory: c:\users\asus\appdata\local\pip\cache\wheels\c8\ff\15\d835921035fec8b42e31c108329e4b200365ac8573bc5f56d8
Successfully built future
Installing collected packages: tensorboard-plugin-wit, pyasn1, charset-normalizer, zipp, urllib3, typing-extensions, tensorboard-data-server, six, scipy, rsa, PyYAML, pyparsing, pyasn1-modules, protobuf, packaging, opencv-python, oauthlib, MarkupSafe, idna, grpcio, future, fonttools, Cython, cycler, colorama, certifi, cachetools, absl-py, werkzeug, tqdm, requests, python-dateutil, kiwisolver, importlib-metadata, google-auth, requests-oauthlib, matplotlib, markdown, google-auth-oauthlib, tensorboard
Successfully installed Cython-0.29.33 MarkupSafe-2.1.2 PyYAML-6.0 absl-py-1.4.0 cachetools-5.3.0 certifi-2022.12.7 charset-normalizer-3.0.1 colorama-0.4.6 cycler-0.11.0 fonttools-4.38.0 future-0.18.3 google-auth-2.16.0 google-auth-oauthlib-0.4.6 grpcio-1.51.1 idna-3.4 importlib-metadata-6.0.0 kiwisolver-1.4.4 markdown-3.4.1 matplotlib-3.5.3 oauthlib-3.2.2 opencv-python-4.7.0.68 packaging-23.0 protobuf-3.20.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyparsing-3.0.9 python-dateutil-2.8.2 requests-2.28.2 requests-oauthlib-1.3.1 rsa-4.9 scipy-1.7.3 six-1.16.0 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tqdm-4.64.1 typing-extensions-4.4.0 urllib3-1.26.14 werkzeug-2.2.2 zipp-3.12.0
(yolov5) D:\yolov5-v3.1>python detect.py --source ./inference/images/ --weights weights/yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='1', img_size=640, iou_thres=0.45, save_conf=False, save_dir='inference/output', save_txt=False, source='./inference/images/', update=False, view_img=False, weights=['weights/yolov5s.pt'])
Traceback (most recent call last):
File "detect.py", line 172, in <module>
detect()
File "detect.py", line 27, in detect
device = select_device(opt.device)
File "D:\yolov5-v3.1\utils\torch_utils.py", line 33, in select_device
assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity
AssertionError: CUDA unavailable, invalid device 1 requested
(yolov5) D:\yolov5-v3.1>python detect.py --source ./inference/images/ --weights weights/yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='inference/output', save_txt=False, source='./inference/images/', update=False, view_img=False, weights=['weights/yolov5s.pt'])
Using CUDA device0 _CudaDeviceProperties(name='NVIDIA GeForce GTX 1050', total_memory=4095MB)
Fusing layers...
Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
image 1/2 D:\yolov5-v3.1\inference\images\bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.032s)
image 2/2 D:\yolov5-v3.1\inference\images\zidane.jpg: 384x640 2 persons, 1 ties, Done. (0.026s)
Results saved to inference\output
Done. (2.122s)
