RRepoGEO

REPOGEO REPORT · LITE

NVIDIA-AI-IOT/nanosam

Default branch main · commit 65363361 · scanned 6/8/2026, 8:07:45 AM

GitHub: 888 stars · 77 forks

AI VISIBILITY SCORE
35 /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
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 NVIDIA-AI-IOT/nanosam, 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
    Refine the README's opening sentence to clarify its role as a solution

    Why:

    CURRENT
    NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.
    COPY-PASTE FIX
    NanoSAM is a **complete solution** for real-time Segment Anything (SAM) inference, specifically optimized for NVIDIA Jetson Orin Platforms with NVIDIA TensorRT. It provides a highly efficient, distilled model for edge AI applications.
  • hightopics#2
    Expand topics to include broader edge AI and computer vision terms

    Why:

    CURRENT
    jetson-orin, jetson-orin-nano, nvidia, real-time, segment-anything, tensorrt
    COPY-PASTE FIX
    jetson-orin, jetson-orin-nano, nvidia, real-time, segment-anything, tensorrt, edge-ai, embedded-vision, computer-vision, deep-learning-model, inference
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://developer.nvidia.com/embedded/jetson-orin-nano (or a more specific project page if available)

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 NVIDIA-AI-IOT/nanosam
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MediaPipe
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MediaPipe · recommended 1×
  2. TensorFlow Lite · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. OpenVINO Toolkit · recommended 1×
  5. NVIDIA TensorRT · recommended 1×
  • CATEGORY QUERY
    Need a performant real-time segmentation solution for low-power devices.
    you: not recommended
    AI recommended (in order):
    1. MediaPipe
    2. TensorFlow Lite
    3. ONNX Runtime
    4. OpenVINO Toolkit
    5. NVIDIA TensorRT

    AI recommended 5 alternatives but never named NVIDIA-AI-IOT/nanosam. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a distilled segmentation model for high-speed inference on constrained hardware.
    you: not recommended
    AI recommended (in order):
    1. NanoDet-Plus
    2. MobileNetV3
    3. DeepLabv3+
    4. U-Net
    5. PP-LiteSeg
    6. YOLOv8
    7. EfficientNet-Lite
    8. ESNet

    AI recommended 8 alternatives but never named NVIDIA-AI-IOT/nanosam. 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 NVIDIA-AI-IOT/nanosam?
    pass
    AI named NVIDIA-AI-IOT/nanosam explicitly

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

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

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

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NVIDIA-AI-IOT/nanosam — 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