Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
YOLOX-tiny/Nano vs YOLOV4-tiny
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Description
According to your paper:
YOLOX-nano/tiny has less parameters than YOLOV4-tiny, and better COCO AP(%).
According to:
https://github.com/AlexeyAB/darknet/issues/7928
YOLOv4-tiny (3l): 38.6% AP, 182 fps (end-to-end inference)
What is the speed of YOLOX-tiny/Nano for inference compared with YOLOv4-tiny (3l)?
and the related mAP@0.5?
Thanks.
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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 the YOLOX paper and the linked Darknet issue, focusing on the YOLOX-tiny/Nano and YOLOv4-tiny (3l) inference and mAP figures. The work is complete when comparable speed and mAP@0.5 measurements are available with their evaluation conditions documented.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100