RRepoGEO

REPOGEO REPORT · LITE

keras-team/keras-hub

Default branch master · commit aecde4ae · scanned 6/2/2026, 3:51:15 AM

GitHub: 981 stars · 336 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 keras-team/keras-hub, 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
  • highreadme#1
    Reposition the README's opening paragraph to emphasize its role as a multi-framework model hub

    Why:

    CURRENT
    **KerasHub** is a pretrained modeling library that aims to be simple, flexible, and fast. The library provides Keras 3 implementations of popular model architectures, paired with a collection of pretrained checkpoints available on Kaggle Models.
    COPY-PASTE FIX
    **KerasHub** is the official multi-framework hub for pretrained Keras 3 models, offering a simple, flexible, and fast way to access and use popular model architectures. It provides Keras 3 implementations paired with a comprehensive collection of pretrained checkpoints available on Kaggle Models.
  • mediumtopics#2
    Add specific topics related to 'model hub' or 'model repository'

    Why:

    CURRENT
    cv, deep-learning, jax, keras, llm, machine-learning, natural-language-processing, nlp, python, pytorch, tensorflow
    COPY-PASTE FIX
    cv, deep-learning, jax, keras, llm, machine-learning, natural-language-processing, nlp, python, pytorch, tensorflow, model-hub, model-repository, pretrained-models
  • lowreadme#3
    Add a brief section or sentence comparing KerasHub to other major model hubs

    Why:

    COPY-PASTE FIX
    Consider adding a sentence like: 'While other platforms like Hugging Face Transformers and TensorFlow Hub offer broad model collections, KerasHub provides a deeply integrated, multi-framework experience specifically optimized for Keras 3 users, ensuring seamless compatibility and performance.'

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 keras-team/keras-hub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Hub · recommended 2×
  3. TensorFlow Hub · recommended 2×
  4. Keras Applications · recommended 2×
  5. Open Model Zoo (Intel) · recommended 1×
  • CATEGORY QUERY
    Need a unified source for pretrained deep learning models across multiple frameworks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Hub
    3. TensorFlow Hub
    4. Keras Applications
    5. Open Model Zoo (Intel)
    6. ONNX Model Zoo
    7. Model Zoo (fast.ai)

    AI recommended 7 alternatives but never named keras-team/keras-hub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's an easy way to integrate pretrained models for various machine learning tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Hub
    3. TensorFlow Hub
    4. Keras Applications
    5. OpenCV
    6. Scikit-learn

    AI recommended 6 alternatives but never named keras-team/keras-hub. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 keras-team/keras-hub?
    pass
    AI named keras-team/keras-hub explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts keras-team/keras-hub in production, what risks or prerequisites should they evaluate first?
    pass
    AI named keras-team/keras-hub 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 keras-team/keras-hub solve, and who is the primary audience?
    pass
    AI named keras-team/keras-hub 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 keras-team/keras-hub. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/keras-team/keras-hub.svg)](https://repogeo.com/en/r/keras-team/keras-hub)
HTML
<a href="https://repogeo.com/en/r/keras-team/keras-hub"><img src="https://repogeo.com/badge/keras-team/keras-hub.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

keras-team/keras-hub — 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