Open zengweigit opened 1 month ago
π Hello @zengweigit, thank you for your interest in YOLOv5 π! Please visit our βοΈ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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pip install ultralytics
@zengweigit hello,
Thank you for reaching out and providing details about your issue. To assist you effectively, we need a bit more information. Could you please provide a minimum reproducible code example? This will help us better understand the context and reproduce the issue on our end. You can refer to our guide on creating a minimum reproducible example here: Minimum Reproducible Example.
Additionally, please ensure that you are using the latest versions of torch
and the YOLOv5 repository. Sometimes, updates can resolve unexpected issues.
Regarding your specific problem with the prediction ratios, here are a few optimization suggestions:
If you can share more details or the code snippet, we can provide more targeted advice. Thank you for your cooperation, and we look forward to helping you resolve this issue.
I used the code from the https://github.com/ultralytics/yolov5/tree/v7.0 branch
The training command I executed was
python -m torch.distributed.run --nproc_per_node 2 train.py --data data/new_data.yaml --cfg models/new_yolov5s.yaml --weights pretrained/yolov5s.pt --epochs 100 --batch-size 256 --workers 16 --device 0,1 --cache ram --hyp data/hyps/hyp.scratch-med.yaml
new_data.yaml
new_yolov5s.yaml
What does class imbalance mean? Does it mean that the number of annotations for my no_helmet class is different from the number of annotations for the wrong_glove class? Or does it mean something else?
Hello @zengweigit,
Thank you for providing the details of your setup and the training command you used. Itβs great to see that you are using the code from the v7.0
branch.
To address your question about class imbalance: Yes, class imbalance refers to a situation where the number of annotations (or instances) for each class in your dataset is significantly different. For example, if you have many more annotations for the no_helmet
class compared to the wrong_glove
class, your model might become biased towards predicting the more frequent class.
You can modify the hyp.scratch-med.yaml
file to include class weights. Hereβs an example:
# Add class weights to the hyperparameters
cls: 0.5 # Class loss gain (original value)
cls_pw: [1.0, 2.0] # Class weights for each class, adjust as needed
Please ensure that you are using the latest versions of torch
and the YOLOv5 repository. Sometimes, updates can resolve unexpected issues. You can update your repository with:
git pull
If the issue persists, could you please provide a minimum reproducible code example? This will help us better understand the context and reproduce the issue on our end. You can refer to our guide on creating a minimum reproducible example here: Minimum Reproducible Example.
Feel free to reach out if you have any more questions or need further assistance. We're here to help! π
π Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
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Thank you for your contributions to YOLO π and Vision AI β
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Hello, I am using YOLOV5. When I train a custom model, the prediction ratio of no_helmet is 96 when I only have one category. After I add a wrong_glove category, the prediction ratio of no_helmet is only 90. The same dataset is used for both trainings. Can you give me some optimization suggestions?
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