Add BenchmarkEvaluator with basic precision/recall computation

[Muhammedswalihu]

Summary

This PR introduces a utility class BenchmarkEvaluator in supervision/metrics/benchmark.py to support benchmarking object detection results across different datasets or models.

Features

• Computes basic precision and recall
• Accepts Detections objects for ground truth and prediction
• Optional support for class mapping and IoU thresholding (future extensions)
• Includes a unit test at tests/metrics/test_benchmark.py

Motivation

Addresses Issue #1778: Improving object detection benchmarking process for unrelated datasets.

Let me know if you'd like me to extend this in future PRs with:

• mAP, F1, or per-class metrics
• Confusion matrix visualization
• Colab notebook example

Thanks for the opportunity to contribute!

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[Muhammedswalihu]

Hi @SkalskiP @onuralpszr — I've submitted this PR for the BenchmarkEvaluator (Issue #1778 ). Let me know if you'd like me to fix the pre-commit error or extend this further. Thanks for reviewing!

[soumik12345]

Hi @Muhammedswalihu, this seems like a really valuable feature!
Can you please replace the placeholder logic with a working one, provide a working example and testcases; and we can review the PR.

[Muhammedswalihu]

Hi @soumik12345 , thanks for the review!

I’ll go ahead and:

Replace the placeholder logic in BenchmarkEvaluator with full precision/recall/mAP computation,

Add a working demo example (maybe in a Colab notebook for clarity), and

Improve the test coverage with more edge cases and per-class evaluation.

Let me know if there’s anything specific you’d like to see included. Appreciate the opportunity — excited to take this further!

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[Muhammedswalihu]

Hi @soumik12345, I've added a Colab-style demo notebook BenchmarkEvaluator_Demo.ipynb!

It includes:

• How to import and use the BenchmarkEvaluator

• Per-class precision and recall visualization

• A visual example comparing predicted and ground truth bounding boxes

This should help users understand and adopt the module more easily.

Let me know if you'd like me to polish or extend this notebook further!

[soumik12345]

Hi @Muhammedswalihu, thanks for providing the PoC!
Please feel free to proceed with the actual implementation.
Also, there's no need to commit the notebook to supervision, you can just attach a colab notebook in a comment when the PR is ready for review with the complete logic.

[soumik12345]

The logic here is incomplete, please add the correct logic to compute precision and recall.

[soumik12345]

This too seems like a placeholder test; please proceed with the implementation and add comprehensive unit tests.