REPOGEO REPORT · LITE

airockchip/rknn_model_zoo

Default branch main · commit bad6c733 · scanned 5/19/2026, 6:32:08 PM

GitHub: 2,480 stars · 415 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

3 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

A collection of pre-converted and optimized deep learning models with deployment examples for various Rockchip NPU platforms (RK3562, RK3566, RK3568, RK3576, RK3588, RV1126B, RV1103, RV1106, RV1109, RV1126, RK1808), including Python and C API inference.

hightopics#2

Add relevant topics to the repository

Why:

COPY-PASTE FIX

rockchip, npu, deep-learning, model-zoo, embedded-ai, rknn, inference, computer-vision, edge-ai, rk3588, rk3568, rk3562, rk3566, rk3576, rv1126b, rv1103, rv1106, rv1109, rv1126, rk1808

mediumreadme#3

Refine the README's opening paragraph for clarity

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 comprehensive collection of pre-converted and optimized deep learning models, providing ready-to-use deployment examples for various Rockchip NPU platforms. It includes detailed processes for exporting RKNN models and performing inference using both Python and C APIs, targeting developers working with Rockchip edge AI 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

Of all named tools, what % are you?

Top rival

Rockchip RKNN-Toolkit2

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Rockchip RKNN-Toolkit2 · recommended 1×
Tengine · recommended 1×
OpenCV · recommended 1×
YOLOv5/YOLOv8 · recommended 1×
MNN · recommended 1×

CATEGORY QUERY
Where can I find examples for deploying deep learning models on Rockchip NPU hardware?
you: not recommended
AI recommended (in order):
1. Rockchip RKNN-Toolkit2
2. Tengine
3. OpenCV
4. YOLOv5/YOLOv8
5. MNN
AI recommended 5 alternatives but never named airockchip/rknn_model_zoo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools convert and infer neural network models using C or Python APIs for embedded devices?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite
2. ONNX Runtime
3. OpenVINO Toolkit
4. PyTorch Mobile
5. Apache TVM
6. Edge Impulse
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?

Embed your GEO score

Drop this badge into the README of airockchip/rknn_model_zoo. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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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