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Metrics: Mean Average Recall (mAR) #1583
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Contribution guidelinesIf you would like to make a contribution, please check that no one else is assigned already. Then leave a comment such as "Hi, I would like to work on this issue". We're happy to answer any questions about the task even if you choose not to contribute. TestingPlease share a Google Colab with minimal code to test the new feature. We know it's additional work, but it will speed up the review process. You may use the Starter Template. The reviewer must test each change. Setting up a local environment to do this is time-consuming. Please ensure that Google Colab can be accessed without any issues (make it public). Thank you! 🙏 |
@onuralpszr, I've heard you already put some work towards this one. Shall I assign it to you? |
Thank you and yes |
Hey @onuralpszr, how's it going? Did you manage to get the mAR working in the end? |
After my work let me check and open pr |
Implemented via #1661 |
Metrics: Mean Average Recall (mAR)
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We'd like to expand our suite of metrics with a new one - Mean Average Recall (mAR). This would involve creating it, its accompanying results class, and briefly testing it.
Note it is different from
mAP
in that it don't use the precision-recall curve, but recall-IoU. This affectsMeanAverageRecallResult
:mAR@50
,mAR@75
, etc - only the global mainmAR
value.1.0
as the default if no value are provided.-1
is a consideration too, which should bring the same change tomAP
.Feel free to change the above if it feels better.
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