LAION-AI / LAION-AI/CLIP_benchmark
Support ImageNet-X
Open
Nobody has claimed this yet.
new datasets
- Dominant language
- Python
- Stars
- 814
- Forks
- 103
- PR merge metrics
- No merged PRs in 30d
Description
for robustness https://facebookresearch.github.io/imagenetx/site/home
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 by reading the ImageNet-X project page linked in the issue and the CLIP_benchmark repository's existing dataset integrations. Determine the required evaluation inputs and outputs for ImageNet-X, then verify that the benchmark can run it and report robustness results consistently with its other evaluations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 32/100