Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
Background images
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- Dominant language
- Python
- Stars
- 10.6k
- Forks
- 2.5k
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Description
Background images are images with no objects that are added to a dataset to reduce False Positives (FP). When training Yolox, which percentage of backgound in our dataset could improve performance.
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
No file, test, or entry point is named. Start by reviewing the YOLOX training documentation at yolox.readthedocs.io and the issue's question about background-image percentages. Done would require a project-supported recommendation, including the conditions under which that percentage improves false-positive performance.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100