Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
请问为什么我训练出来的Yolox nano 模型parameter和Gflops与论文中的不匹配
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- Python
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Description
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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 by comparing the yolox_voc_s configuration changes for width 0.33 and depth 0.25 with the yolox_nano.pth model used for training. Then inspect how YOLOX reports parameter count and GFLOPs and compare those results with the paper's nano values. Done means explaining the 2.25M parameters and 2.91 GFLOPs, or identifying the configuration or checkpoint mismatch.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100