Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
Objectness calculation and balancing factor w.r.t reg_loss and class_loss
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Description
Hello, since the Objectness loss (IoU in the case of YOLOX) should take into account all the predictions, not just the positives found with simOTA, how is it weighted the fact that uses all 13x13 + 26x26 + 52x52 predictions while reg_loss and class_loss use just positives ?
I can't even understand if it uses just the positives and their the IoU with respect to the assigned GT or if it uses also the negatives, how the target for the negative is calculated ? Thanks in advance for every answer.
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Research direction
Start by locating the YOLOX loss and simOTA implementation, then trace how objectness, reg_loss, and class_loss select predictions and construct targets. Compare positive and negative predictions across the 13x13, 26x26, and 52x52 grids. Done means documenting how negative targets are calculated and how objectness is weighted relative to the other losses.
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Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100