Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
anchor free 疑问
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- Dominant language
- Python
- Stars
- 10.6k
- Forks
- 2.5k
- PR merge metrics
- No merged PRs in 30d
Description
您好，最近在深深入研究这项工作，也很感谢您的巨大贡献，请问yolox这种anchor free机制在loss计算的时候如何确保浅层特征预测小目标而深层特征预测大目标的呢？
Contributor guide
No contributing guide indexed for this repository
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 documentation and the object-detection materials linked by the project, then trace how the anchor-free mechanism assigns targets and computes loss across feature levels. A useful result would be a clear explanation of how small and large targets are handled, supported by references to the relevant YOLOX implementation or documentation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100