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mAP50计算与iou_thres的关系 #13476

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lqh964165950 opened this issue Dec 30, 2024 · 3 comments
Open
1 task done

mAP50计算与iou_thres的关系 #13476

lqh964165950 opened this issue Dec 30, 2024 · 3 comments
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@lqh964165950
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mAP50计算与iou_thres有什么关系,运行val.py文件时,改变iou_thres为0.5,精度上升了,召回率稍微下降了,这是为什么?
iou_thresh

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@lqh964165950 lqh964165950 added the question Further information is requested label Dec 30, 2024
@UltralyticsAssistant
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👋 Hello @lqh964165950, thank you for your interest in YOLOv5 🚀! The relationship between [email protected] and iou_thres is an important topic in evaluating object detection performance.

If this is a ❓ question regarding custom evaluations or training, please provide as much detail as possible, such as specific evaluation settings, modifications made to the val.py script, and any relevant logs or outputs, to help us interpret your observations.

If this is a 🐛 Bug Report, please include a minimum reproducible example (MRE) detailing the exact steps you followed, along with the environment and version information, to help us identify the cause.

Requirements

Ensure you're using Python>=3.8.0 with all requirements installed. To reinstall dependencies, you can run:

pip install -r requirements.txt

Environments

YOLOv5 supports various development environments, including Jupyter Notebooks, cloud platforms, and Docker. Please verify compatibility and ensure all dependencies are correctly installed to avoid potential issues.

Status

Make sure that your development environment is up-to-date. The YOLOv5 repository includes Continuous Integration (CI) testing to verify correctness across multiple operations, including training, validation, inference, export, and benchmarks on common operating systems.

This is an automated response to assist you promptly. An Ultralytics engineer will review your issue and provide further assistance soon. Let us know if you have additional information or clarifications in the meantime! 😊

@LZLwoaini
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MAP是所有类别AP的均值,AP由精确率和召回率确定,而IoU 阈值、confidence (置信度)阈值影响精确率和召回率的计算。

@pderrenger
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@LZLwoaini 您提到得很正确,mAP 是所有类别 AP 的均值,而 AP 是由精确率和召回率决定的。运行 val.py 时更改 IoU 阈值会影响精确率与召回率的平衡:提高 IoU 阈值(如 0.5),会倾向于更严格的匹配标准,通常会提高精确率但稍微降低召回率,因为检测框需要更高的重叠度才能被视为正确。您可以根据具体应用需求调整 IoU 阈值以优化结果。更多关于 mAP 和 IoU 的信息,请参考 Ultralytics Glossary: mAP

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