RRepoGEO

REPOGEO REPORT · LITE

NVlabs/describe-anything

Default branch main · commit 153ad3d3 · scanned 5/21/2026, 6:18:15 PM

GitHub: 1,488 stars · 90 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
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 NVlabs/describe-anything, 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
    Add a 'Why Describe Anything?' section to the README

    Why:

    COPY-PASTE FIX
    Add a section immediately after the TL;DR, e.g., "## Why Describe Anything?", explaining that while general VLMs provide overall captions, DAM excels at *detailed, localized descriptions for user-defined regions* in both images and videos, going beyond global summaries to provide fine-grained understanding.
  • mediumtopics#2
    Expand repository topics with specific keywords

    Why:

    CURRENT
    describe-anything, detailed-localized-captioning, large-multimodal-models, vision-language-model
    COPY-PASTE FIX
    describe-anything, detailed-localized-captioning, large-multimodal-models, vision-language-model, image-captioning, video-captioning, localized-captioning, region-description, segmentation-based-ai
  • lowcomparison#3
    Add a 'Comparison to Existing Work' section in the README

    Why:

    COPY-PASTE FIX
    Create a new section in the README, e.g., "## Comparison to Existing Work", briefly explaining how Describe Anything extends beyond general image/video captioning or VLMs by focusing on detailed, localized descriptions for user-defined regions, contrasting it with models that provide global captions or only detect objects.

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 NVlabs/describe-anything
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
salesforce/BLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. salesforce/BLIP · recommended 2×
  2. facebookresearch/detectron2 · recommended 1×
  3. microsoft/VinVL · recommended 1×
  4. facebookresearch/M4C · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How can I generate detailed textual descriptions for specific areas within an image?
    you: not recommended
    AI recommended (in order):
    1. Detectron2 (facebookresearch/detectron2)
    2. VinVL (microsoft/VinVL)
    3. M4C (facebookresearch/M4C)
    4. Hugging Face Transformers (huggingface/transformers)
    5. BLIP-2 (salesforce/BLIP)
    6. InstructBLIP (salesforce/BLIP)
    7. OpenAI CLIP (openai/CLIP)
    8. OpenCLIP (mlfoundations/open_clip)
    9. Google Cloud Vision AI
    10. Microsoft Azure AI Vision

    AI recommended 10 alternatives but never named NVlabs/describe-anything. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide localized video captioning from user-defined regions or masks?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Video AI
    2. AWS Rekognition
    3. OpenCV
    4. Hugging Face Transformers
    5. Azure Video Analyzer
    6. Azure Custom Vision
    7. DeepMotion

    AI recommended 7 alternatives but never named NVlabs/describe-anything. 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 NVlabs/describe-anything?
    pass
    AI did not name NVlabs/describe-anything — 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 NVlabs/describe-anything in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVlabs/describe-anything 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 NVlabs/describe-anything solve, and who is the primary audience?
    pass
    AI named NVlabs/describe-anything explicitly

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

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NVlabs/describe-anything — 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