Detection references are needlessly transforming masks
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- Python
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Description
Something we realized today with @pmeier: even for pure detection tasks where masks aren't needed, the detection training references are still using the masks from COCO, which means that:
- those masks are being decoded into images
- those masks get transformed all the time e.g. here
Both these things are completely wasteful since masks aren't needed for detection tasks. According to some simple benchmark this significantly hurts performance.
(Not sure if that applies to Keypoints too, would need to check)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with references/detection/transforms.py and trace how COCO masks enter the detection training references. Verify that detection-only training no longer decodes or repeatedly transforms masks, using the reported performance benchmark if available; also check whether the same issue affects keypoints before defining the final scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, performance
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100