neuropoly / neuropoly/SmaugLab
Inference transforms
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 8
- Forks
- 0
- Avg merge
- 5d 2h
- Merged PRs (30d)
- 11
Description
Implement an easy way to incoporate transforms into the inference of a trained model.
- make N transforms
- maybe only apply one TA transform for inference?
At the end, probably the average of these predictions should be the final prediction.
Optionally: use transforms also for validation set (but maybe with different logic than training?) to ensure the best validation checkpoint actually has OOD performance?
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
The issue names no files, tests, or entry points. First locate the trained-model inference path and the existing transform or data-augmentation logic, then clarify how multiple transformed predictions should be combined and whether validation transforms are in scope. Done means inference supports the agreed transform behavior and its output is verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100