RRepoGEO

REPOGEO REPORT · LITE

leondgarse/keras_cv_attention_models

Default branch main · commit 687943d8 · scanned 6/15/2026, 4:57:23 PM

GitHub: 627 stars · 97 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 leondgarse/keras_cv_attention_models, 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 H1 to specify category and value

    Why:

    CURRENT
    # ___Keras_cv_attention_models___
    COPY-PASTE FIX
    # Keras CV Attention Models: A Comprehensive Collection of State-of-the-Art Computer Vision Models
  • mediumabout#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/leondgarse/keras_cv_attention_models
  • mediumreadme#3
    Add a concise introductory paragraph to README

    Why:

    CURRENT
    The first substantive content after the H1 is a warning about Keras 3.x compatibility.
    COPY-PASTE FIX
    (Insert this text immediately after the H1, before any warnings or roadmaps)
`This repository offers a comprehensive and frequently updated collection of state-of-the-art computer vision models, including a wide range of attention-based and novel architectures, all implemented in Keras and TensorFlow. It is designed for researchers and developers working on various computer vision tasks such as image recognition, object detection, and generative AI, providing ready-to-use models and tools for training and evaluation.`

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 leondgarse/keras_cv_attention_models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rwightman/pytorch-image-models
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. rwightman/pytorch-image-models · recommended 1×
  2. Keras Applications · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. MMDetection / MMSegmentation / MMClassification (OpenMMLab) · recommended 1×
  5. pytorch/vision · recommended 1×
  • CATEGORY QUERY
    Need a comprehensive collection of modern computer vision models for a high-level deep learning library.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    2. Keras Applications
    3. Hugging Face Transformers (huggingface/transformers)
    4. MMDetection / MMSegmentation / MMClassification (OpenMMLab)
    5. TorchVision Models (pytorch/vision)
    6. TensorFlow Hub

    AI recommended 6 alternatives but never named leondgarse/keras_cv_attention_models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a toolkit for advanced object detection or generative AI models in a flexible deep learning environment.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Hugging Face Transformers
    4. MMDetection
    5. Keras
    6. JAX

    AI recommended 6 alternatives but never named leondgarse/keras_cv_attention_models. 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 leondgarse/keras_cv_attention_models?
    pass
    AI named leondgarse/keras_cv_attention_models explicitly

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

  • If a team adopts leondgarse/keras_cv_attention_models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named leondgarse/keras_cv_attention_models 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 leondgarse/keras_cv_attention_models solve, and who is the primary audience?
    pass
    AI did not name leondgarse/keras_cv_attention_models — 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?

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leondgarse/keras_cv_attention_models — 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