@huayue1126 hello there! 👋 about ultralytics

[github-actions]

👋 Hello @yaober, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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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

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

Hello @yaober! 👋

To calculate mIoU and mPA for your YOLOv8-seg model, you should indeed use the val mode as described. Ensure your dataset is properly configured in your your_dataset.yaml file to support these metrics. If the metrics still don't appear, it may be necessary to implement a custom script to calculate them by comparing the predicted masks against the ground truth.

Here's the command again for clarity:

yolo segment val model=yolov8n-seg.pt data=your_dataset.yaml

If you encounter any specific issues or errors during this process, please provide the error messages or further details, and I'll be glad to assist you further!

[yaober]

I do use the command line you provided. and I got the metrics like speed/mAP/BOX_Percision .......... while there is not mIOU. I also check the code of yolov8, I do see mIoU function, but it was not implemented when the val process. could you please check the source code again?

[glenn-jocher]

@yaober hello!

Thank you for your detailed feedback. It seems that while the mIoU function exists within the YOLOv8 codebase, it might not be automatically applied during the val process for segmentation models. I recommend manually invoking this function in your validation script or modifying the validation pipeline to include mIoU calculation explicitly.

If you need further guidance on how to modify the script or any other assistance, please let me know! I'm here to help. 😊