lightly-ai / lightly-ai/lightly-train
[QUESTION] distill dinov3 to repvit
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- Python
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Description
Hello, I'm trying to distill Dinov3 into a Repvit model using lightly-train. Referring to your [documentation](https://docs.lightly.ai/train/stable/pretrain_distill/models/custom_models.html#custom-models) on how to distill a custom model, I found that the `forward_features` function might return multiple layers of features, and these features might have inconsistent resolutions, making them impossible to concatenate into a single tensor. In this case, how can I supervise the multi-layer features?
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
Start with the custom-model distillation documentation linked in the issue and inspect how the model's forward_features output is handled. Compare the DINOv3 and RepViT feature outputs, then document whether multi-layer features with different resolutions are supported and how supervision should be configured.
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
- 25/100