REPOGEO REPORT · LITE
DirtyHarryLYL/Transformer-in-Vision
Default branch main · commit 84c67642 · scanned 5/29/2026, 9:12:35 PM
GitHub: 1,344 stars · 141 forks
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 DirtyHarryLYL/Transformer-in-Vision, 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.
- highreadme#1Reposition README H1 to clarify it's a curated list/resource
Why:
CURRENT# Transformer-in-Vision Recent Transformer-based CV and related works. Welcome to comment/contribute!
COPY-PASTE FIX# Transformer-in-Vision: A Curated List of Recent Transformer-based CV and Related Works This repository serves as a comprehensive, curated collection of recent Transformer-based computer vision (CV) models, papers, and related resources. Welcome to comment/contribute!
- highlicense#2Add a LICENSE file to the repository
Why:
COPY-PASTE FIX(Create a LICENSE file, e.g., MIT or Apache-2.0, and add it to the repository root.)
- mediumhomepage#3Add a homepage URL to the repository settings
Why:
COPY-PASTE FIX(Add a relevant URL, e.g., a project page, a related blog post, or even the GitHub repo URL itself if no external page exists, to the 'Homepage' field in the repository settings.)
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.
- Papers With Code · recommended 1×
- Hugging Face Models · recommended 1×
- Awesome-Transformers-in-Vision · recommended 1×
- The Gradient · recommended 1×
- Distill.pub · recommended 1×
- CATEGORY QUERYWhere can I find a curated list of recent transformer models for computer vision tasks?you: not recommendedAI recommended (in order):
- Papers With Code
- Hugging Face Models
- Awesome-Transformers-in-Vision
- The Gradient
- Distill.pub
AI recommended 5 alternatives but never named DirtyHarryLYL/Transformer-in-Vision. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the latest transformer architectures for multi-modal deep learning in computer vision?you: not recommendedAI recommended (in order):
- Flamingo
- CoCa
- BLIP-2
- PaLI-X
- LLaVA
- GIT
AI recommended 6 alternatives but never named DirtyHarryLYL/Transformer-in-Vision. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 DirtyHarryLYL/Transformer-in-Vision?passAI did not name DirtyHarryLYL/Transformer-in-Vision — 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 DirtyHarryLYL/Transformer-in-Vision in production, what risks or prerequisites should they evaluate first?passAI named DirtyHarryLYL/Transformer-in-Vision 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 DirtyHarryLYL/Transformer-in-Vision solve, and who is the primary audience?passAI did not name DirtyHarryLYL/Transformer-in-Vision — 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?
Embed your GEO score
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DirtyHarryLYL/Transformer-in-Vision — 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