1. Download the source code

Download the Yolov7 source code in github and decompress
the YOLOv7 source code

Data annotation can refer to this article
https://blog.csdn.net/qq_39779449/article/details/110172948?spm=1001.2014.3001.5502
Label format
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2. Configuration environment

Overall configuration tree structure

Install Yolo v7 gpu environment

Create Yolov7 environment in Anconda insert image description here

Install the required packages

Activate the Yolov7 environment and install the various packages required by Yolov7, and use the Tsinghua mirror source to install the required packages (I tried it all, and the Tsinghua mirror is faster)

activate yolov7

pip install -r F:\objectdet\yolov7-main/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

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Check whether the environment installation is successful.

If you install it completely according to requirements.txt, you will find that the installed torch version is not the gpu version, so you have to reinstall the gpu version to use your gpu to train Yolov7

pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html -i  https://pypi.tuna.tsinghua.edu.cn/simple

After installation, check the package you installed through pip list, as shown in the figure below, congratulations, the installation is successful
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3. Make Yolo v7 train and test your own data set

Modify Yolo v7 configuration

In pycharm, select the environment required by Yolov7 just installed, insert image description here
modify the configuration environment,

and create a yaml file of your own data set

train: Modify to your own training set path
val: Modify to your own validation set path
test: Modify to your own test set path
nc: modify the number of categories of your own dataset
names: Modify to your own dataset category labels.

train your own dataset

Through pycharm training, modify the train.py file as follows, and then run insert image description here
can also be trained directly on the terminal by the following method

python train.py --workers 1 --device 0 --batch-size 8 --data data/data.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml

If the following interface appears, congratulations, the training is successful, wait slowly for your model training to complete
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Test your own dataset

Here we use the default parameters in the test.py file

data: Change to your own dataset configuration file path
img: input data size
batch: number of batch data
conf: confidence level
iou: threshold
weights: Change to your own training weight path
name: Change to your own path

python test.py --data data/data.yaml --img 640 --batch 8 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_val

Make predictions on a single dataset

Predict a single image` python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source images/
benign.jpg

4. Problems encountered and solutions

Can't run yolov7-e6e.yaml file
. According to the official paper, yolov7-e6e.yaml works best, so I plan to try yolov7-e6e.yaml, emmm, when training yolov7-e6e.yaml, the following error is reported. yolov7.yaml can run normally, but it will report an error if it is replaced with yolov7e6e.yaml. nc has been changed to the category of my data set, and it can be used for transfer learning. I checked a bunch of information on the Internet and couldn't find a relevant solution.
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At first I thought it was insufficient video memory, so I changed the batch-size to 1 and the img-size to 128, but it still didn’t work. I checked the author’s latest code and found that the yolov7-e6e.yaml and yolov7.yaml training files are different. The former is trained through train_aux.py, and the latter is trained through train.py.
Start training with train_aux.py, yes, and report an error again. . . One bug was solved and another bug appeared.
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Check the github issues section of the original author of yolov7, and found that loss.py was repaired, and then copied the loss.py file to replace my original loss file, and finally succeeded.
Summary: From experience, I will run the code of the big guy in the future. Try to use the latest code from the boss. You can avoid many detours.

yolov7: From matching environment to training your own data set