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computing precision and recall #853
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Hello @Nocoffeeanymore, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Jupyter Notebook , Docker Image, and Google Cloud Quickstart Guide for example environments. If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom model or data training question, please note Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
For more information please visit https://www.ultralytics.com. |
@Nocoffeeanymore you can set it to any value you want. |
Thank you very much. |
@Nocoffeeanymore conf is already sorted here. |
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
Hi all, How can i modify the train script to compute the precision vs recall curve and graph it ? |
@diego0718 that's a good question. This should really be a training output. All the required plotting code is here, you simply uncomment this to produce a PR curve: Lines 295 to 304 in 9eae82e
TODO: autogenerate PR curve on final test.py run. |
@diego0718 PR has been submitted for this feature addition, please review and comment at #1107 |
Thank u Glen for rapid response. Good work! |
❔Question
Why does author set a pr_score=0.1 and utilize "np.interp(-pr_score, -conf[i], precision[:, 0])" to compute precision as well as recall?
Could anyone give me some help? Thanks.
Additional context
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