yolov8 yaml文件的anchors 有详细的修改说明文档吗? about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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from ultralytics.

Hello! 😊 In YOLOv8, the anchors can indeed be optimized for your specific scenario, especially if your detection performance varies significantly between large and small objects. You can manually adjust the anchors in the YAML configuration file.

Here’s an example snippet of how you might set your anchors:

anchors:
  - [10,13, 16,30, 33,23]  # Small objects
  - [30,61, 62,45, 59,119]  # Medium objects
  - [116,90, 156,198, 373,326]  # Large objects

Ensure that the number and scale of anchors match the distribution of your object sizes. Smaller anchors are generally used for smaller objects, and larger anchors for larger objects.

If manual tuning seems complex, you can also re-calculate these automatically based on your dataset by using the autoanchor feature during training which examines your dataset and adapts the anchors accordingly:

yolo train --data your_data.yaml --cfg your_model.yaml --img-size 640 autoanchor 

Feel free to experiment with different anchor sizes to find what gives the best results for both large and small objects on your data. 🚀

from ultralytics.

hi！
I am also very interested in this issue
The autoanchor effect is relatively poor on my dataset.
so, How should I cancel autoanchor .

I tried set my anchors as your suggestion, but it can't cancel autoanchor .

from ultralytics.

Hello! 👋

To disable the autoanchor feature in YOLOv8, you can simply omit the autoanchor flag from your training command. If you've set your anchors manually in the YAML file, they should be used as specified unless overridden by the autoanchor recalibration.

Here's an example of how to train without using autoanchor:

yolo train --data your_data.yaml --cfg your_model.yaml --img-size 640

Just ensure that your your_model.yaml configuration file has the anchors explicitly defined as you want them. That should do the trick! If you encounter any more issues, feel free to ask. Happy training! 🚀

from ultralytics.