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YOLOv5 5.0 version is deployed on Opencv
Preface
The version must be consistent with mine, otherwise it is not guaranteed to run! ! !
1. YOLOv5 5.0
We choose YOLOv5 5.0 version for this task. In https://github.com/ultralytics/yolov5 , you can choose the corresponding version to download. Since most of the C++ code on GitHub is v5 5.0, v5 5.0 was chosen. (If you are proficient in the later process and deployment code, you can try the deployment of v5 7.0 version)
1. Download the code
Opening the above link directly here should be a page like this, which is the code of v5 7.0
We want to choose the v5 5.0 version, then we have to select Tags in the master, which contains the corresponding versions.
This is the code of v5 5.0, as shown in the figure below.
Then, we download the zip package of the code (you can also use git clone method (copy the code directly to the local)
and extract it to your own folder
2. Debugging training code
2.1. Configuration environment
Open the folder in PyCharm.
First, configure the required environment. The requirements.txt contains the minimum versions required for various packages. Copy pip install -r requirement.txt in the Terminal of PyCharm to start configuring the environment. (It is recommended to create a new virtual environment for yolov5 5.0 version to avoid the need to update the environment in other codes in the same environment.)
Various packages in our environment can be viewed in Anaconda Prompt. I remember that the numpy and onnx versions cannot be too high. If there is any problem, just follow my version. (If you encounter other version problems, please search on Baidu and most of them can be solved)
2.2. Training
Open the train.py file, and in if name == ' main ':, add the default value models/yolov5s.yaml to "-cfg". This is the yaml file of the yolov5s model. It will follow the settings in this yaml file later. Go build the network structure and download the yolov5s.pt file. (You can set up other models, just write the path name correctly.)
Right-click train, you can see that the yolov5s.pt file is downloading below, but if you look closely, you can see that it is downloading version v7.0. What we want is version 5.0. of. And we will see an error after the download is completed. The reason for the error is that our .pt file was downloaded incorrectly, so we need to find the corresponding file from the official website.
Go back to our official website address on GitHub. There is a release on the right side.
Scroll down and you will find the link to the 5.0 version. Click to enter.
At the bottom there will be the yolov5 5.0 version of yolov5s.pt. Download it and put it in the folder just now. . 
At this time, when we click train, an error will still be reported. You can see that it is because of a path problem and the downloaded coco128.zip is not decompressed (it is not clear whether the code is written incorrectly or it is my problem).
Since the default value of "--data" in train.py is data/coco128.yaml, let's open coco128.yaml and take a look. It is found that train and val point to the coco128 folder in the upper-level directory, then we manually decompress it and change the path to an absolute path.
Click train again, and an error will still be reported.
Modify this line in loss
. At the same time, delete the .cache cache file in the labels folder in the data set.
Then train can run! ! !
After training, best.pt and last.pt files will be generated
3. Export .onnx file
Open the export.py file in the models folder, modify the default value of "-weights" to the best.pt path after training, and then export it. The corresponding .onnx file will be generated in the best.pt folder.
2. C++ deployment
Here I found the C++ code directly on GitHub, and directly posted the link https://github.com/xiaojunjun65/YOLOv5-onnx . The author’s tutorial is at https://blog.csdn.net/xiaojunjun200211/article/details/121784214 , you can refer to
the C++ code by yourself, mainly modify the value of class_name, .onnx file path and image path, and then you can run it.
Summarize
It is recommended that students who have no deployment experience can first deploy version 5.0 according to this process, or when looking for code on GitHub, be sure to deploy according to the YOLOv5 version required by the author, and then deploy other versions after becoming proficient. The main problem is that if the version is inconsistent with the author, the detection box will not be displayed, the .onnx node will report errors, etc. I am a beginner and I don’t know the reason yet. If you want to learn more later, you can try the deployment of a higher version.
Friends are welcome to communicate and give advice, thank you! ! !
Thanks: https://blog.csdn.net/xiaojunjun200211/article/details/121784214