yolov11-seg怎么利用TP、TN、FP、FN添加mIoU指标

[luzhen00]

请问yolov11-seg怎么利用TP、TN、FP、FN添加mIoU指标啊

[UltralyticsAssistant]

👋 Hello @luzhen00, thank you for your interest in Ultralytics 🚀! We recommend checking out the Docs, which might help answer your question. You can find an overview of YOLOv8 Modes and details on Tasks supported by the library.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it, including details of your environment, any custom configurations, and error messages.

If this is a custom training or metric-related ❓ Question, such as your mIoU concern, please provide as much context as possible, such as code snippets, relevant logs, and an explanation of your intended implementation. This will allow the team to assist you more effectively.

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[glenn-jocher]

@luzhen00 感谢您的提问！YOLO11 的 mIoU 是通过交并比 (IoU) 计算得出的，对所有类别求平均后得到 mIoU。您可以根据 TP（True Positives）、FP（False Positives）、FN（False Negatives） 来手动计算 IoU 并扩展指标。如需修改代码以添加自定义计算，建议参考 val 模式中的验证逻辑。欢迎提交 PR 或进一步探讨！

[luzhen00]

我想知道具体在哪个文件修改哪个位置呢

[glenn-jocher]

@luzhen00 感谢您的提问！您可以查看代码库中的 val.py 文件或相关的 metrics.py 文件，这些文件中包含验证逻辑和 IoU 及 mIoU 的计算实现。建议从 ultralytics/utils/metrics.py 中的 box_iou 函数入手进行扩展。详情请参考 metrics 文档。