REPOGEO REPORT · LITE

margaretmz/awesome-tensorflow-lite

Default branch main · commit d20e763a · scanned 5/23/2026, 6:07:52 PM

GitHub: 1,377 stars · 191 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

22 /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

1 / 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 margaretmz/awesome-tensorflow-lite, 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

Emphasize 'curated list' in README's opening paragraph

Why:

CURRENT

TensorFlow Lite is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices. It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo. This is an awesome list of TensorFlow Lite models with sample apps, helpful tools and learning resources -

COPY-PASTE FIX

This is the **Awesome TensorFlow Lite** list, a comprehensive and curated directory of TensorFlow Lite models, sample applications, helpful tools, and learning resources. TensorFlow Lite is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices, currently running on more than 4 billion devices! This list showcases what the community has built, puts samples side-by-side for easy reference, and shares knowledge.

mediumhomepage#2
Add a homepage URL to the repository settings
Why:
COPY-PASTE FIX
```
https://github.com/margaretmz/awesome-tensorflow-lite
```

mediumtopics#3

Add 'edge-ai' and 'on-device-ml' topics

Why:

CURRENT

android, awesome, awesome-list, computer-vision, deep-learning, flutter, ios, keras-tutorials, mediapipe, mlkit, mobile, model-zoo, sample-app, tensorflow, tensorflow-keras, tensorflow-lite, tensorflow-models, tfhub, tflite, tflite-models

COPY-PASTE FIX

android, awesome, awesome-list, computer-vision, deep-learning, edge-ai, flutter, ios, keras-tutorials, mediapipe, mlkit, mobile, model-zoo, on-device-ml, sample-app, tensorflow, tensorflow-keras, tensorflow-lite, tensorflow-models, tfhub, tflite, tflite-models

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 margaretmz/awesome-tensorflow-lite

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

TensorFlow Lite Model Zoo

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

TensorFlow Lite Model Zoo · recommended 1×
PyTorch Mobile · recommended 1×
TorchVision Models · recommended 1×
ONNX Model Zoo · recommended 1×
Apple Core ML Models · recommended 1×

CATEGORY QUERY
Where can I find pre-trained deep learning models optimized for mobile applications?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite Model Zoo
2. PyTorch Mobile
3. TorchVision Models
4. ONNX Model Zoo
5. Apple Core ML Models
6. MediaPipe Models
7. Hugging Face Transformers
AI recommended 7 alternatives but never named margaretmz/awesome-tensorflow-lite. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking sample applications and learning resources for on-device computer vision.
you: not recommended
AI recommended (in order):
1. Apple Core ML
2. Vision Frameworks
3. TensorFlow Lite (tensorflow/tensorflow)
4. MediaPipe (google/mediapipe)
5. OpenCV (opencv/opencv)
6. ML Kit
7. PyTorch Mobile (pytorch/pytorch)
AI recommended 7 alternatives but never named margaretmz/awesome-tensorflow-lite. 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 margaretmz/awesome-tensorflow-lite?
pass
AI did not name margaretmz/awesome-tensorflow-lite — 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 margaretmz/awesome-tensorflow-lite in production, what risks or prerequisites should they evaluate first?
pass
AI named margaretmz/awesome-tensorflow-lite 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 margaretmz/awesome-tensorflow-lite solve, and who is the primary audience?
pass
AI did not name margaretmz/awesome-tensorflow-lite — 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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MARKDOWN (README)

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HTML

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Pro

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margaretmz/awesome-tensorflow-lite — 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