Yolov5（最新版）环境配置及部署之环境配置（一) （详细教程）

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Yolov5 环境配置及部署之环境配置（一) （详细教程）

最近在学习yolov5，记录下过程。

一、环境配置进入Github官网https://github.com/ultralytics/yolov5/releases，选择版本为v5.0 在这里插入图片描述

2.下拉滚动条，找到Source code(zip)。点击下载到本地。在这里插入图片描述

3.解压Yolov5-5.0文件到指定目录，解压后文件如图所示在这里插入图片描述

4.打开Anaconda Prompt，创建名为Yolov550的环境。在官网上要求Python 版本大于等于3.6.0 (base) C:\Users\Administrator>conda create -n Yolov550

5.输入 y，回车。

Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate Yolov550 # # To deactivate an active environment, use # # $ conda deactivate

6.激活Yolov550环境，输入conda activate Yolov550。

(base) C:\Users\Administrator>conda activate Yolov5 (Yolov5) C:\Users\Administrator>

7.进入解压的Yolov5-5.0目录

(Yolov5) C:\Users\Administrator>M: (Yolov5) M:\Yolov5\yolov5\Version5\yolov5-5.0>

8.通过requirements.txt来安装环境

pip install -r requirements.txt

9.成功安装以下包

Successfully installed cachetools-4.2.2 chardet-4.0.0 google-auth-1.32.1 google-auth-oauthlib-0.4.4 idna-2.10 oauthlib-3.1.1 opencv-python-4.5.2.54 pyasn1-0.4.8 pyasn1-modules-0.2.8 pycocotools-2.0.2 requests-2.25.1 requests-oauthlib-1.3.0 rsa-4.7.2 tensorboard-2.5.0 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 urllib3-1.26.6

10.测试torch 、CUDA 是否正确安装。发现CUDA 没有安装

import torch >>> flag = torch.cuda.is_available() >>> print(flag) False >>>

11.删除环境，重新安装cuda。

deactivate Yolov550 conda remove -n Yolov550 --all

12.重新创建环境并且指定python版本为3.7

conda create -n Yolov550 python=3.7 conda activate Yolov550

13.安装CUDA和CUDNN

conda install cudatoolkit==10.2.89 conda install cudnn==7.6.5

14.安装剩下的依赖包

matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.2 Pillow PyYAML>=5.3.1 scipy>=1.4.1 tqdm>=4.41.0 tensorboard>=2.4.1 seaborn>=0.11.0 pandas coremltools>=4.1 onnx>=1.8.1 scikit-learn==0.19.2 # for coreml quantization thop # FLOPS computation pycocotools>=2.0 # COCO mAP

PS:遇到安装pycocotools 一直报错，请使用命令：

pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

15.安装torch 和 torchvision。进入官网找到和cuda对应的版本，获得安装命令。

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

在这里插入图片描述 16.最后安装完毕所有包的版本信息如下：

absl-py 0.13.0 pypi_0 pypi blas 1.0 mkl defaults ca-certificates 2021.7.5 haa95532_1 defaults cachetools 4.2.2 pypi_0 pypi certifi 2021.5.30 py37haa95532_0 defaults chardet 4.0.0 pypi_0 pypi cudatoolkit 10.2.89 h74a9793_1 defaults cudnn 7.6.5 cuda10.2_0 defaults cycler 0.10.0 py37_0 defaults cython 0.29.21 py37hd77b12b_0 defaults freetype 2.10.4 hd328e21_0 defaults google-auth 1.32.1 pypi_0 pypi google-auth-oauthlib 0.4.4 pypi_0 pypi grpcio 1.38.1 pypi_0 pypi icc_rt 2019.0.0 h0cc432a_1 defaults icu 58.2 ha925a31_3 defaults idna 2.10 pypi_0 pypi importlib-metadata 4.6.1 pypi_0 pypi intel-openmp 2021.2.0 haa95532_616 defaults jpeg 9b hb83a4c4_2 defaults kiwisolver 1.3.1 py37hd77b12b_0 defaults libpng 1.6.37 h2a8f88b_0 defaults libtiff 4.2.0 hd0e1b90_0 defaults libuv 1.40.0 he774522_0 defaults lz4-c 1.9.3 h2bbff1b_0 defaults markdown 3.3.4 pypi_0 pypi matplotlib 3.3.2 haa95532_0 defaults matplotlib-base 3.3.2 py37hba9282a_0 defaults mkl 2020.2 256 defaults mkl-service 2.3.0 py37h196d8e1_0 defaults mkl_fft 1.3.0 py37h46781fe_0 defaults mkl_random 1.1.1 py37h47e9c7a_0 defaults ninja 1.10.2 h6d14046_1 defaults numpy 1.21.0 pypi_0 pypi numpy-base 1.19.2 py37ha3acd2a_0 defaults oauthlib 3.1.1 pypi_0 pypi olefile 0.46 py37_0 defaults opencv-python 4.5.2.54 pypi_0 pypi openssl 1.1.1k h2bbff1b_0 defaults pandas 1.1.3 py37ha925a31_0 defaults pillow 8.0.1 py37h4fa10fc_0 defaults pip 21.1.3 py37haa95532_0 defaults protobuf 3.17.3 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pyparsing 2.4.7 pyhd3eb1b0_0 defaults pyqt 5.9.2 py37h6538335_2 defaults python 3.7.10 h6244533_0 defaults python-dateutil 2.8.1 pyhd3eb1b0_0 defaults pytorch 1.9.0 py3.7_cuda10.2_cudnn7_0 pytorch pytz 2021.1 pyhd3eb1b0_0 defaults pyyaml 5.3.1 py37he774522_1 defaults qt 5.9.7 vc14h73c81de_0 defaults requests 2.25.1 pypi_0 pypi requests-oauthlib 1.3.0 pypi_0 pypi rsa 4.7.2 pypi_0 pypi scipy 1.5.2 py37h9439919_0 defaults seaborn 0.11.0 py_0 defaults setuptools 52.0.0 py37haa95532_0 defaults sip 4.19.8 py37h6538335_0 defaults six 1.16.0 pyhd3eb1b0_0 defaults sqlite 3.36.0 h2bbff1b_0 defaults tensorboard 2.4.1 pypi_0 pypi tensorboard-plugin-wit 1.8.0 pypi_0 pypi tk 8.6.10 he774522_0 defaults torchaudio 0.9.0 py37 pytorch torchvision 0.10.0 py37_cu102 pytorch tornado 6.1 py37h2bbff1b_0 defaults tqdm 4.54.0 pyhd3eb1b0_0 defaults typing_extensions 3.10.0.0 pyh06a4308_0 defaults urllib3 1.26.6 pypi_0 pypi vc 14.2 h21ff451_1 defaults vs2015_runtime 14.27.29016 h5e58377_2 defaults werkzeug 2.0.1 pypi_0 pypi wheel 0.36.2 pyhd3eb1b0_0 defaults wincertstore 0.2 py37_0 defaults xz 5.2.5 h62dcd97_0 defaults yaml 0.2.5 he774522_0 defaults zipp 3.5.0 pypi_0 pypi zlib 1.2.11 h62dcd97_4 defaults zstd 1.4.9 h19a0ad4_0 defaults

17.测试CUDA 是否可用：输入python，进入python环境。print(torch.cuda.is_available())为True表示正确安装CUDA。

(Yolov550) C:\Users\Administrator>python Python 3.7.10 (default, Feb 26 2021, 13:06:18) [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 >>>

18.根据图像的尺寸及速度，选择合适的模型。yolov5各个模型参数如下图所示：在这里插入图片描述 19.代码测试

///从图片检测 python detect.py --source ./data/images/ --weights ./weights/yolov5s.pt --conf 0.4 ///从摄像头检测 python detect.py --source 0 --weights ./weights/yolov5s.pt --conf 0.4 ///加载自己训练模型检测 python detect.py --source 0 --weights ./模型地址 --conf 阈值 ///yolov5s.pt可以在Github官网上下载放到weights文件夹内，如果没有代码会自动下载

20.程序运行结果,正常运行。

(Yolov550) M:\Yolov5\yolov5\Version5\yolov5-5.0>python detect.py --source ./data/images/ --weights ./weights/yolov5s.pt --conf 0.4 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', nosave=False, project='runs/detect', save_conf=False, save_txt=False, source='./data/images/', update=False, view_img=False, weights=['./weights/yolov5s.pt']) YOLOv5 2021-4-12 torch 1.9.0 CUDA:0 (GeForce GTX 1660, 6144.0MB) Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients J:\WorkSoft\envs\Yolov550\lib\site-packages\torch\nn\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ..\c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) image 1/2 M:\Yolov5\yolov5\Version5\yolov5-5.0\data\images\bus.jpg: 640x480 4 persons, 1 bus, Done. (0.276s) image 2/2 M:\Yolov5\yolov5\Version5\yolov5-5.0\data\images\zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.044s) Results saved to runs\detect\exp2 Done. (0.962s)

在这里插入图片描述参考https://blog.csdn.net/qq_45389690/article/details/111306902

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Yolov5（最新版） 环境配置及部署之环境配置（一) （详细教程）

Yolov5（最新版）环境配置及部署之环境配置（一) （详细教程）