Add extensive pycocotools comparison tests with large synthetic datasets

[Copilot]

Motivation

Existing tests validate equality with pycocotools using only 1-2 small examples (3-9 annotations). Insufficient coverage across dataset scales and task types blocks production adoption.

Modification

New test module: test_extensive_pycocotools_comparison.py

• 12 parameterized tests validating bit-for-bit equality (tolerance 1e-10) across:
  • Object detection (bbox): 10/50/100 images, 50-1500 annotations
  • Instance segmentation (segm): Same scales with RLE masks
  • Keypoint detection: Same scales with 17 keypoints/instance
  • Edge cases: perfect predictions, low confidence, mixed object sizes

Synthetic data generation

• Realistic COCO-formatted datasets with proper size distributions (small/medium/large)
• Variable image dimensions, prediction noise, false positives
• Keypoint visibility/occlusion modeling

Documentation

• tests/README.md: Test suite organization and execution guide
• README.md: Added "Testing & Reliability" section

BC-breaking (Optional)

None. Tests only.

Use cases (Optional)

Validates drop-in replacement guarantee at scale. All existing 91 tests pass.

Checklist

1. ✅ Pre-commit/linting applied
2. ✅ 12 new tests added, all pass (91 existing + 12 new)
3. N/A - Test-only changes
4. ✅ Documentation updated (README.md, tests/README.md)

Original prompt

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This section details on the original issue you should resolve

<issue_title>Add more extensive tests checking equality with pycocotools</issue_title>
<issue_description>Hi, I help maintain the rf-detr repo . You have an open PR in this repo. In looking at tests in this Repo, we see that you check equality of results with pycocotools only with one example. We would like to see a larger test suite to ensure equality across a wider range of possible inputs. Would you be willing to provide either a test script or adding to your test suite here comparing the 2 methods using a full suite of model predictions on Coco for all task types?

Obviously, we would prefer that rf-detr and rf-detr-seg are used to generate the predictions jsons for objdet and instseg respectively, whereas we don't have a keypoints model ready to use, so a yolo-pose model could generate said predictions json.

We understand the value of faster evaluation and would like to use this repo, but the above test is a bit of a blocker for us. Otherwise we will do the above test when our team finds time to prioritize it.

Thanks for your PR and your work maintaining this repo!

cc: @Borda @isaacrob</issue_description>

<agent_instructions>Adding tests aligned with actual code base and in particular validation on larger batch</agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes Add more extensive tests checking equality with pycocotools #70

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[Copilot]

Pull request overview

This PR adds extensive validation tests to ensure faster_coco_eval produces identical results to pycocotools across large-scale synthetic datasets. The addition addresses concerns about insufficient test coverage by creating comprehensive parameterized tests spanning object detection, instance segmentation, and keypoint detection tasks with realistic COCO-formatted data.

Changes:

• Added 12 parameterized tests validating exact equality (tolerance 1e-10) across varying dataset scales (10/50/100 images, 50-1500 annotations)
• Implemented synthetic COCO dataset generation with realistic size distributions, RLE masks, and keypoint visibility modeling
• Created comprehensive documentation explaining test organization, execution, and validation criteria

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.

File	Description
tests/test_extensive_pycocotools_comparison.py	New test module with synthetic data generation and 12 parameterized tests validating equality across bbox, segmentation, and keypoint tasks
tests/README.md	New documentation detailing test suite organization, execution commands, and validation criteria
README.md	Added "Testing & Reliability" section highlighting comprehensive test coverage and pycocotools comparison

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[Copilot]

The variable i is declared in the loop but never used within the loop body. Consider using _ instead to indicate it's intentionally unused: for _ in range(num_keypoints):

Suggested change

	for i in range(num_keypoints):
	for _ in range(num_keypoints):

[Copilot]

The loop variable i is unused in the loop body. Replace with _ to indicate intentional discard: for _ in range(17):

Suggested change

	for i in range(17):
	for _ in range(17):

[Copilot]

The count '90+ automated tests' may become outdated as tests are added or removed. Consider using a more maintainable phrasing like 'Comprehensive automated test suite' or implement dynamic test counting if precision is important.

Suggested change

	- **90+ automated tests** covering all functionality
	- **Comprehensive automated test suite** covering all functionality

[Copilot]

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