REPOGEO REPORT · LITE

PINTO0309/PINTO_model_zoo

Default branch main · commit ee9f21f9 · scanned 5/21/2026, 4:17:02 PM

GitHub: 4,164 stars · 639 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 PINTO0309/PINTO_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

highreadme#1

Reposition README opening to clarify model zoo purpose

Why:

CURRENT

A repository for storing models that have been inter-converted between various frameworks.

COPY-PASTE FIX

PINTO_model_zoo is a comprehensive collection of *pre-converted and optimized* deep learning models, ready for efficient deployment across various frameworks and edge devices. Unlike conversion tools, this repository provides ready-to-use models for your projects.

mediumreadme#2

Add explicit mention of model optimization and edge deployment benefits

Why:

COPY-PASTE FIX

Integrate a sentence early in the README, such as: 'These models are extensively optimized, including quantization, for high-performance deployment on resource-constrained edge devices like Raspberry Pi, EdgeTPU, and mobile platforms.'

lowexamples#3

Create a dedicated section for sample code contributions

Why:

CURRENT

I have been working on quantization of various models as a hobby, but I have skipped the work of making sample code to check the operation because it takes a long time. I welcome a pull request from volunteers to provide sample code. :smile:

COPY-PASTE FIX

Add a new section header in the README: `## Sample Code and Examples` followed by a call for contributions and guidelines, e.g., 'We welcome pull requests from volunteers to provide sample code demonstrating the use of these models. Please refer to our contribution guidelines for details.'

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 PINTO0309/PINTO_model_zoo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ONNX

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

ONNX · recommended 1×
onnx-tensorflow · recommended 1×
onnxruntime · recommended 1×
tf2onnx · recommended 1×
MMdnn · recommended 1×

CATEGORY QUERY
How to convert deep learning models between PyTorch, TensorFlow, and ONNX formats?
you: not recommended
AI recommended (in order):
1. ONNX
2. onnx-tensorflow
3. onnxruntime
4. tf2onnx
5. MMdnn
6. TensorFlow SavedModel
7. TensorFlow Serving
8. TFLite
9. TorchScript
AI recommended 9 alternatives but never named PINTO0309/PINTO_model_zoo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find pre-trained models optimized for TensorFlow Lite and EdgeTPU deployment?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite Model Zoo
2. Coral Models
3. MediaPipe
4. TensorFlow Hub
5. Open Model Zoo (openvinotoolkit/open_model_zoo)
AI recommended 5 alternatives but never named PINTO0309/PINTO_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
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 PINTO0309/PINTO_model_zoo?
pass
AI named PINTO0309/PINTO_model_zoo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts PINTO0309/PINTO_model_zoo in production, what risks or prerequisites should they evaluate first?
pass
AI named PINTO0309/PINTO_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 PINTO0309/PINTO_model_zoo solve, and who is the primary audience?
pass
AI named PINTO0309/PINTO_model_zoo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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PINTO0309/PINTO_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