REPOGEO REPORT · LITE

apple/ml-mobileclip

Default branch main · commit aecfb545 · scanned 5/26/2026, 8:58:21 PM

GitHub: 1,530 stars · 119 forks

AI VISIBILITY SCORE

28 /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

2 / 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 apple/ml-mobileclip, 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

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

OVERALL DIRECTION

hightopics#1

Add specific topics to improve categorization

Why:

COPY-PASTE FIX

mobile-ai, clip, image-text-retrieval, on-device-ml, computer-vision, deep-learning, cvpr-2024, tmlr-2025, reinforced-learning, multimodal-ai, coreml

highreadme#2

Add a concise introductory sentence to the README

Why:

CURRENT

# MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

This is the official repository of
MobileCLIP2: Improving Multi-Modal Reinforced Training. (TMLR August 2025 <mark>Featured</mark>)Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T Toshev, Oncel Tuzel, Hadi Pouransari.MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training. (CVPR 2024)Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.*

COPY-PASTE FIX

# MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

This repository provides the official, mobile-optimized implementation of MobileCLIP and MobileCLIP2, enabling fast image-text models for on-device inference and real-time zero-shot image classification.

This is the official repository of
MobileCLIP2: Improving Multi-Modal Reinforced Training. (TMLR August 2025 <mark>Featured</mark>)Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T Toshev, Oncel Tuzel, Hadi Pouransari.MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training. (CVPR 2024)Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.*

mediumreadme#3

Add a section to the README clarifying the existing license

Why:

COPY-PASTE FIX

## License

This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on the applicable licenses.

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 apple/ml-mobileclip

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

MobileCLIP

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

MobileCLIP · recommended 1×
OpenCLIP · recommended 1×
BLIP-2 · recommended 1×
MiniGPT-4 · recommended 1×
CLIP · recommended 1×

CATEGORY QUERY
Looking for efficient image-text models suitable for on-device inference or mobile applications.
you: not recommended
AI recommended (in order):
1. MobileCLIP
2. OpenCLIP
3. BLIP-2
4. MiniGPT-4
5. CLIP
6. ALBEF
AI recommended 6 alternatives but never named apple/ml-mobileclip. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to train performant multi-modal models using large-scale reinforced learning datasets?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Accelerate
3. PyTorch Lightning
4. RLlib
5. Acme
6. TF-Agents
7. JAX
8. Flax
9. Haiku
AI recommended 9 alternatives but never named apple/ml-mobileclip. 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 apple/ml-mobileclip?
pass
AI named apple/ml-mobileclip explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts apple/ml-mobileclip in production, what risks or prerequisites should they evaluate first?
pass
AI named apple/ml-mobileclip 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 apple/ml-mobileclip solve, and who is the primary audience?
pass
AI did not name apple/ml-mobileclip — likely talking about a different project
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 apple/ml-mobileclip. 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/apple/ml-mobileclip.svg)](https://repogeo.com/en/r/apple/ml-mobileclip)

HTML

<a href="https://repogeo.com/en/r/apple/ml-mobileclip"><img src="https://repogeo.com/badge/apple/ml-mobileclip.svg" alt="RepoGEO" /></a>

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apple/ml-mobileclip — 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