mlfoundations / mlfoundations/open-diffusion
Evaluation metrics
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 133
- Forks
- 10
- PR merge metrics
- No merged PRs in 30d
Description
Would be great to have (optional) model evaluation.
Possibilities:
- CLIP score (e.g. on a reference set of captions like the ones from Parti)
- FID, or inception distance in general where we could use other models like CLIP to extract features, as Inception is ImageNet specific
- Possibily the recent ImageReward https://arxiv.org/abs/2304.05977, which relies on a model trained on human rankings and is quite easy to use, they are also planning to make the ranking dataset bigger
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
The issue proposes optional CLIP, FID, or ImageReward evaluation but names no files, entry points, or tests. Start by reviewing the repository's Python training flow and resolving which metric, inputs, configuration, and validation are in scope; it is done when the selected evaluation is optional and its behavior is tested.
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