Metrics: Mean Average Recall (mAR)

[LinasKo]

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 affects MeanAverageRecallResult:

• The will not be mAR@50, mAR@75, etc - only the global main mAR value.
• Other frameworks threshold by max detections. I suggest we add this later if required.
• mAP currently implements a single averaging method, and F1 has 3 different ones (micro, macro, weighted). The choice between those is left to PR author - we can always add more later.
• The metric can report mAR values per-class. Do tell if that is difficult.
• I think we should have 1.0 as the default if no value are provided. -1 is a consideration too, which should bring the same change to mAP.

Feel free to change the above if it feels better.

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Helpful links:

• Contribution guide
• Metrics:
  • mAP metric: docs, code
  • F1 Score: docs, code
• Supervision Cheatsheet
• Colab Starter Template
• Prior metrics test Colab

[LinasKo]

Contribution guidelines

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Testing

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[LinasKo]

@onuralpszr, I've heard you already put some work towards this one. Shall I assign it to you?

[onuralpszr]

@onuralpszr, I've heard you already put some work towards this one. Shall I assign it to you?

Thank you and yes

[LinasKo]

Hey @onuralpszr, how's it going? Did you manage to get the mAR working in the end?

[onuralpszr]

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