REPOGEO REPORT · LITE

mlfoundations/datacomp

Default branch main · commit 4a8df199 · scanned 6/11/2026, 11:08:42 AM

GitHub: 782 stars · 65 forks

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface mlfoundations/datacomp, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Clarify the project's role as a dataset curation benchmark in the opening sentence

Why:

CURRENT

# DataComp

[[ Paper ]](https://arxiv.org/abs/2304.14108) [[ Website ]](http://datacomp.ai/) [[ Blog ]](https://laion.ai/blog/datacomp/)

Welcome to our competition. This repository contains the participant tooling necessary to download data from our pool, train CLIP models, evaluate them on downstream tasks and submit to our leaderboard.

COPY-PASTE FIX

# DataComp

[[ Paper ]](https://arxiv.org/abs/2304.14108) [[ Website ]](http://datacomp.ai/) [[ Blog ]](https://laion.ai/blog/datacomp/)

Welcome to DataComp, the benchmark and competition for designing and evaluating multimodal image-text datasets for pre-training foundation models like CLIP. This repository provides the tooling for participants to download data, train models, evaluate on downstream tasks, and submit to our leaderboard.

mediumreadme#2

Add a clear statement about the project's license(s) to the README

Why:

COPY-PASTE FIX

## License

This project is licensed under [specify license(s) here, e.g., "a custom license based on Apache 2.0 and MIT for different components"]. Please refer to the [LICENSE](LICENSE) file for full details.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface mlfoundations/datacomp

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LAION-5B

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LAION-5B · recommended 2×
Conceptual Captions (CC3M/CC12M) · recommended 1×
COCO (Common Objects in Context) · recommended 1×
Visual Genome · recommended 1×
Scrapy · recommended 1×

CATEGORY QUERY
How can I curate effective image-text datasets for training large multimodal models?
you: not recommended
AI recommended (in order):
1. LAION-5B
2. Conceptual Captions (CC3M/CC12M)
3. COCO (Common Objects in Context)
4. Visual Genome
5. Scrapy
6. Beautiful Soup
7. Perplexity AI
8. Google Search API
9. OpenCV
10. Pillow (PIL Fork)
11. SpaCy
12. NLTK
13. Sentence-BERT
14. OpenAI Embeddings
15. CLIP model
16. Labelbox
17. Scale AI
18. Amazon Mechanical Turk (MTurk)
19. Albumentations
20. imgaug
21. TextAttack
22. Easy Data Augmentation (EDA)
23. CLIP Score
24. FID (Frechet Inception Distance)
25. Inception Score
AI recommended 25 alternatives but never named mlfoundations/datacomp. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What platforms help evaluate and compare different image-text dataset pre-training approaches?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Hugging Face Datasets
3. Hugging Face Hub
4. PyTorch Lightning
5. Weights & Biases
6. TensorBoard
7. MLflow
8. OpenCLIP
9. LAION-5B
10. LAION-Aesthetics
AI recommended 10 alternatives but never named mlfoundations/datacomp. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of mlfoundations/datacomp?
pass
AI named mlfoundations/datacomp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mlfoundations/datacomp in production, what risks or prerequisites should they evaluate first?
pass
AI named mlfoundations/datacomp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo mlfoundations/datacomp solve, and who is the primary audience?
pass
AI named mlfoundations/datacomp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of mlfoundations/datacomp. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/mlfoundations/datacomp.svg)](https://repogeo.com/en/r/mlfoundations/datacomp)

HTML

<a href="https://repogeo.com/en/r/mlfoundations/datacomp"><img src="https://repogeo.com/badge/mlfoundations/datacomp.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

mlfoundations/datacomp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite