Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
Bad accuracy when training on custom data
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 10.6k
- Forks
- 2.5k
- PR merge metrics
- No merged PRs in 30d
Description
Hi, I get bad training results when using YOLOX-s.
I have a custom dataset with 30 classes and approximately 500 images. Each image contains multiple class objects, so the dataset size should be enough. No matter what epoch and batch size I use, COCOAP50 is always around 0.30 and COCOAP50_95 is 0.40.
For the reference, I have used the same dataset to train YOLOv7 and YOLOv8 and the accuracy is close to 90%.
Any suggestions on how to make YOLOX training better? (Apart from the basics - epoch size, batch size, dataset augmentation)
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
Begin by reproducing YOLOX-s training on the reported 30-class, approximately 500-image custom dataset and record COCOAP50 and COCOAP50_95. Compare the training configuration and evaluation results with the reported YOLOv7 and YOLOv8 runs; done requires identifying a reproducible cause or actionable configuration issue and validating improved accuracy.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100