REPOGEO REPORT · LITE

xinyu1205/recognize-anything

Default branch main · commit 7cb804a8 · scanned 6/18/2026, 12:57:48 PM

GitHub: 3,670 stars · 323 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

40 /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

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 xinyu1205/recognize-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

Strengthen README's opening statement to highlight core capabilities

Why:

CURRENT

This project aims to develop a series of open-source and strong fundamental image recognition models.

COPY-PASTE FIX

Recognize Anything Model (RAM) is a suite of open-source foundation models for advanced image recognition, including RAM++ for high-accuracy open-set recognition of any category, and Tag2Text for simultaneous image tagging and comprehensive captioning.

mediumtopics#2

Add broader, descriptive topics for better categorization

Why:

CURRENT

recognize-anything, tag2text-iclr2024

COPY-PASTE FIX

recognize-anything, tag2text-iclr2024, image-recognition, open-vocabulary, image-tagging, image-captioning, foundation-model, computer-vision, multimodal

lowreadme#3

Add explicit comparison points to existing 'Highlight' section

Why:

COPY-PASTE FIX

Under the 'Superior Image Recognition Capability' highlight, add a bullet point or sentence like: 'Unlike general-purpose models such as CLIP or DINOv2, RAM++ is specifically designed for high-accuracy open-set recognition across diverse categories. Tag2Text further differentiates by offering simultaneous detailed captioning alongside tagging, a capability beyond models like BLIP-2.'

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 xinyu1205/recognize-anything

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DINOv2

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

DINOv2 · recommended 1×
CLIP · recommended 1×
Vision Transformer (ViT) · recommended 1×
ConvNeXt · recommended 1×
EfficientNetV2 · recommended 1×

CATEGORY QUERY
What are good open-source models for recognizing diverse image categories accurately?
you: not recommended
AI recommended (in order):
1. DINOv2
2. CLIP
3. Vision Transformer (ViT)
4. ConvNeXt
5. EfficientNetV2
6. Swin Transformer
7. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
AI recommended 7 alternatives but never named xinyu1205/recognize-anything. This is the gap to close.
Show full AI answer
CATEGORY QUERY
I need a model that can generate both tags and detailed captions for images.
you: not recommended
AI recommended (in order):
1. Salesforce BLIP-2
2. Google Cloud Vision AI
3. Microsoft Azure Computer Vision
4. OpenAI CLIP
5. OpenCLIP
6. Hugging Face Transformers
7. ViT (Vision Transformer)
8. ImageGPT
9. ViLT (Vision-and-Language Transformer)
10. BART
11. T5
AI recommended 11 alternatives but never named xinyu1205/recognize-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 xinyu1205/recognize-anything?
pass
AI named xinyu1205/recognize-anything explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts xinyu1205/recognize-anything in production, what risks or prerequisites should they evaluate first?
pass
AI named xinyu1205/recognize-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 xinyu1205/recognize-anything solve, and who is the primary audience?
pass
AI named xinyu1205/recognize-anything 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 xinyu1205/recognize-anything. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/xinyu1205/recognize-anything.svg)](https://repogeo.com/en/r/xinyu1205/recognize-anything)

HTML

<a href="https://repogeo.com/en/r/xinyu1205/recognize-anything"><img src="https://repogeo.com/badge/xinyu1205/recognize-anything.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

xinyu1205/recognize-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