lightly-ai / lightly-ai/lightly-train
Custom distillation model using DinoVol3 does not achieve the required accuracy after int8 quantization
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.7k
- Forks
- 116
- Avg merge
- 2d 21h
- Merged PRs (30d)
- 6
Description
I customized my own model according to the tutorial and trained it using distillation with dinov3/vits16 as the teacher. However, after exporting the model as ONNX and performing int8 quantization, the model accuracy dropped significantly.
This issue does not occur with models that have not undergone distillation.
Contributor guide
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 source file or test is named. Start by reproducing the tutorial's custom distillation flow with dinov3/vits16, then compare accuracy before and after ONNX export and int8 quantization. Done means identifying why the distilled model loses accuracy or documenting a verified fix with a regression check.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100