RRepoGEO

REPOGEO REPORT · LITE

airockchip/rknn_model_zoo

Default branch main · commit bad6c733 · scanned 7/1/2026, 4:07:13 AM

GitHub: 2,577 stars · 446 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 airockchip/rknn_model_zoo, 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
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A collection of pre-converted and optimized deep learning models and deployment examples specifically for Rockchip RKNN NPUs, supporting Python and C APIs.
  • mediumreadme#2
    Refine the README's opening paragraph to emphasize Rockchip NPU specificity

    Why:

    CURRENT
    `RKNN Model Zoo` is developed based on the RKNPU SDK toolchain and provides deployment examples for current mainstream algorithms. Include the process of `exporting the RKNN model` and using `Python API` and `CAPI` to infer the RKNN model.
    COPY-PASTE FIX
    `RKNN Model Zoo` is a specialized collection of pre-converted and optimized deep learning models, along with comprehensive deployment examples, designed exclusively for Rockchip RKNN NPUs. It leverages the RKNPU SDK toolchain to provide ready-to-use models and demonstrates their inference using both Python and C APIs, specifically targeting embedded AI applications on Rockchip hardware.

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 airockchip/rknn_model_zoo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. OpenVINO Toolkit · recommended 2×
  4. PyTorch Mobile · recommended 2×
  5. NVIDIA TensorRT · recommended 2×
  • CATEGORY QUERY
    How can I deploy deep learning models on embedded NPU hardware efficiently?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. OpenVINO Toolkit
    4. Apache TVM
    5. PyTorch Mobile
    6. NVIDIA TensorRT
    7. Qualcomm AI Engine Direct
    8. ARM NN

    AI recommended 8 alternatives but never named airockchip/rknn_model_zoo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide C and Python APIs for NPU model inference and conversion?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. ONNX Runtime
    3. TensorFlow Lite
    4. PyTorch Mobile
    5. Qualcomm Neural Processing SDK (SNPE)
    6. NVIDIA TensorRT
    7. Arm NN

    AI recommended 7 alternatives but never named airockchip/rknn_model_zoo. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 airockchip/rknn_model_zoo?
    pass
    AI did not name airockchip/rknn_model_zoo — 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 airockchip/rknn_model_zoo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named airockchip/rknn_model_zoo 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 airockchip/rknn_model_zoo solve, and who is the primary audience?
    pass
    AI named airockchip/rknn_model_zoo explicitly

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

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airockchip/rknn_model_zoo — 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