mlfoundations / mlfoundations/datacomp
ImageNet 21k based filtered dataset
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- Dominant language
- Python
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- 792
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Description
Image-based filtering. We select a subset of examples whose visual content overlaps with ImageNet
classes. After applying English language (fasttext) and caption length filtering, we cluster the
image embeddings extracted by the OpenAI ViT-L/14 model for each image into 100K groups using
Faiss [ 75]. We then find the nearest neighbor group for every ImageNet training example, and keep
examples belonging to these groups. We apply this procedure using either ImageNet-21K (14M
images) or ImageNet-1K (1.2M images), forming two subsets.
In the paper, regarding the composition of "Image filters", it mentions that either ImageNet-21K or ImageNet-1K can be used. Looking into the code however, especially for the Datacomp 1B, it looks like only IN1K is used. Is there a version of the Datacomp 1B with IN21K?
Contributor guide
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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 inspecting the Datacomp 1B filtering code and its ImageNet-1K selection path. Check whether the repository provides an ImageNet-21K variant or configuration; done means confirming that variant's availability, or documenting what is missing to produce it.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100