How to Accurately Calculate Object Detection Metrics Using Ultralytics?

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Hello everyone,

I am working with Ultralytics YOLO for object detection & I want some advice on how to accurately calculate & interpret the metrics for evaluating my models I want to understanding the best practice for calculating metrics like precision; recall & mAP using the tools & functions provided by Ultralytic.

I am training my models with the standard configuration, using a dataset that has been properly annotated. After training I use the evaluation scripts to generate metrics but I am not sure if I am interpreting the results correctly.

How to not sure accurate calculation of precision, recall & mAP.
Common pitfalls to avoid in metric calculation.
How to interpret these metrics to effectively improve model performance.

As well, I found these resources when doing research on this https://community.ultralytics.com/t/detecting-hand-signs-with-ultralyticswhat-is-splunk witch is good still need advice; if anyone have any resources, tutorials or personal experiences please share with me, It would be greatly appreciated!!

Thanks in advance… :smile:

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@faelannm a good place to start would be this guide YOLO Performance Metrics - Ultralytics YOLO Docs

mAP50-95 is considered a good all around metric to consider when looking at model performance. There are many metrics calculated for you when running validation, so there shouldn’t be a need to calculate these manually, unless you have a specific need/requirement to do so.

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