RRepoGEO

REPOGEO REPORT · LITE

BR-IDL/PaddleViT

Default branch develop · commit 5ac7d89d · scanned 5/29/2026, 10:12:16 PM

GitHub: 1,238 stars · 328 forks

AI VISIBILITY SCORE
33 /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
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 BR-IDL/PaddleViT, 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 README opening to explicitly state PaddlePaddle focus

    Why:

    CURRENT
    :robot: PaddlePaddle Visual Transformers (`PaddleViT` or `PPViT`) is a collection of vision models beyond convolution.
    COPY-PASTE FIX
    :robot: PaddleViT is the definitive collection of state-of-the-art Visual Transformer and MLP models, specifically optimized for the PaddlePaddle 2.0+ deep learning framework.
  • mediumreadme#2
    Add a dedicated 'Why PaddleViT?' or 'PaddlePaddle Advantage' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Why PaddleViT? The PaddlePaddle Advantage ##` followed by text explaining its unique position for PaddlePaddle users, contrasting it with other frameworks.
  • lowtopics#3
    Expand topics to include more specific PaddlePaddle-related terms

    Why:

    CURRENT
    classification, computer-vision, cv, deep-learning, detection, encoder-decoder, gan, mlp, object-detection, paddlepaddle, segmentation, semantic-segmentation, transformer, vit
    COPY-PASTE FIX
    classification, computer-vision, cv, deep-learning, detection, encoder-decoder, gan, mlp, object-detection, paddlepaddle, paddlepaddle-library, paddlepaddle-models, segmentation, semantic-segmentation, transformer, vit

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 BR-IDL/PaddleViT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ViT (Vision Transformer)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ViT (Vision Transformer) · recommended 1×
  2. Swin Transformer · recommended 1×
  3. ConvNeXt · recommended 1×
  4. MAE (Masked Autoencoders) · recommended 1×
  5. DeiT (Data-efficient Image Transformers) · recommended 1×
  • CATEGORY QUERY
    Seeking modern vision transformer models for a Python-centric deep learning platform.
    you: not recommended
    AI recommended (in order):
    1. ViT (Vision Transformer)
    2. Swin Transformer
    3. ConvNeXt
    4. MAE (Masked Autoencoders)
    5. DeiT (Data-efficient Image Transformers)
    6. DINO (Self-supervised Vision Transformers)

    AI recommended 6 alternatives but never named BR-IDL/PaddleViT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find advanced MLP models for various computer vision tasks in a deep learning library?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Keras (keras-team/keras)
    4. TensorFlow Models (tensorflow/models)
    5. torchvision.models (pytorch/vision)
    6. PaddlePaddle (PaddleClas) (PaddlePaddle/PaddleClas)

    AI recommended 6 alternatives but never named BR-IDL/PaddleViT. 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 BR-IDL/PaddleViT?
    pass
    AI did not name BR-IDL/PaddleViT — 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?

  • If a team adopts BR-IDL/PaddleViT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BR-IDL/PaddleViT 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 BR-IDL/PaddleViT solve, and who is the primary audience?
    pass
    AI named BR-IDL/PaddleViT explicitly

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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BR-IDL/PaddleViT — 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