cuda11.6配置torch环境(运行yolov5项目)
yolov5
yolov5 - Ultralytics YOLOv8的前身,是一个用于目标检测、图像分割和图像分类任务的先进模型。
项目地址:https://gitcode.com/gh_mirrors/yo/yolov5
免费下载资源
·
cuda11.6配置torch环境(运行yolov5项目)
从配置环境到运行项目
首先推荐一个b站的一个up视频,yolov5目标检测,这里up用的是cuda10.2,我用的是11.6,主要选择什么,大家都是依据自己的显卡(我这里是gtx 3060)。
安装Anaconda的安装
1.下载地址:Anaconda官网
具体安装教程这里不叙述了,可以看安装Anaconda教程
cuda(敲重点)
右键英伟达图标,打开英伟达控制面板,
点击帮助-点击系统信息-点击组件
找到cuda对应版本,比如我这里是11.6,所以去官网下载对应的11.6的版本。
cuda官网cuda官网
找到cuda11.6.x
下载下来,然后安装,安装教程cuda安装教程
然后注意,如果安装的是11.6版本的cuda,请选择11.6对于的CUDNN,然后继续看上面的教程。
下载torch(再次敲重点)
如果你之前Anaconda设置了清华源镜像,千万不要用conda install torch因为这里会给你cpu版本,也就是下这个包,你只能用cpu跑不能调用gpu。所以用pip install,这里给11.6版本cuda的安装torch的命令:
pip install torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu116
直接用就行。
安装好之后,进Anaconda的prompt进入虚拟环境,
代码:
import torch
print(torch.cuda.is_available())#cuda是否可用
然后我运行的项目就是开头b站视频里的项目。主要给大家看看防止绕弯路。
GitHub 加速计划 / yo / yolov5
49.31 K
16.02 K
下载
yolov5 - Ultralytics YOLOv8的前身,是一个用于目标检测、图像分割和图像分类任务的先进模型。
最近提交(Master分支:1 个月前 )
b163ff8d
* fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-FONTTOOLS-6133203
- https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321964
- https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321966
- https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321970
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6043904
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6182918
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6219984
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6219986
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6514866
- https://snyk.io/vuln/SNYK-PYTHON-REQUESTS-6928867
- https://snyk.io/vuln/SNYK-PYTHON-SETUPTOOLS-3180412
- https://snyk.io/vuln/SNYK-PYTHON-SETUPTOOLS-7448482
- https://snyk.io/vuln/SNYK-PYTHON-TORCH-6619806
- https://snyk.io/vuln/SNYK-PYTHON-TORCH-6649934
- https://snyk.io/vuln/SNYK-PYTHON-URLLIB3-7267250
- https://snyk.io/vuln/SNYK-PYTHON-WHEEL-3180413
* Update requirements.txt
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
---------
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io> 19 天前
c5bb4087
* fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-TQDM-6807582
- https://snyk.io/vuln/SNYK-PYTHON-ZIPP-7430899
* Update requirements.txt
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
---------
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io> 19 天前
更多推荐
已为社区贡献1条内容
所有评论(0)