RRepoGEO

REPOGEO REPORT · LITE

airockchip/rknn-toolkit2

Default branch master · commit 59a913d1 · scanned 5/23/2026, 3:11:53 AM

GitHub: 3,013 stars · 347 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
30 /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
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 airockchip/rknn-toolkit2, 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 repository description

    Why:

    COPY-PASTE FIX
    Official software development kit (SDK) for converting, optimizing, and deploying AI models on Rockchip NPU platforms.
  • mediumreadme#2
    Reposition the README H1 to specify category and unique value

    Why:

    CURRENT
    # Description
  RKNN software stack can help users to quickly deploy AI models to Rockchip chips.
    COPY-PASTE FIX
    # RKNN-Toolkit2: Official SDK for Rockchip NPU AI Model Deployment
  RKNN-Toolkit2 is the official software development kit (SDK) designed by Rockchip to help users quickly convert, optimize, and deploy AI models onto Rockchip NPU (Neural Processing Unit) platforms.

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-toolkit2
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. NVIDIA TensorRT · recommended 2×
  4. Arm NN · recommended 2×
  5. OpenVINO Toolkit · recommended 1×
  • CATEGORY QUERY
    Need a toolkit to convert and deploy trained AI models on embedded NPU platforms.
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorFlow Lite
    3. ONNX Runtime
    4. TVM
    5. NVIDIA TensorRT
    6. Arm NN
    7. Qualcomm Neural Processing SDK (SNPE)

    AI recommended 7 alternatives but never named airockchip/rknn-toolkit2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What C/C++ and Python APIs exist for AI inference on embedded neural processing units?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. Arm NN
    4. TensorFlow Lite for Microcontrollers
    5. NXP eIQ Machine Learning Software
    6. Qualcomm AI Engine Direct SDK
    7. Edge TPU runtime library
    8. NVIDIA TensorRT

    AI recommended 8 alternatives but never named airockchip/rknn-toolkit2. 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-toolkit2?
    pass
    AI named airockchip/rknn-toolkit2 explicitly

    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-toolkit2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named airockchip/rknn-toolkit2 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-toolkit2 solve, and who is the primary audience?
    pass
    AI named airockchip/rknn-toolkit2 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-toolkit2 — 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