RRepoGEO

REPOGEO REPORT · LITE

he-y/Awesome-Pruning

Default branch master · commit 45ac58b4 · scanned 5/19/2026, 9:18:08 AM

GitHub: 2,493 stars · 331 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
35 /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
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 he-y/Awesome-Pruning, 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 emphasize academic survey nature

    Why:

    CURRENT
    A curated list of neural network pruning and related resources.
    COPY-PASTE FIX
    This is a curated list of academic papers, research surveys, and comprehensive resources specifically focused on neural network pruning.
  • hightopics#2
    Add specific topics to clarify content type

    Why:

    CURRENT
    awesome-list, model-acceleration, model-compression, pruning
    COPY-PASTE FIX
    awesome-list, neural-network-pruning, model-compression-survey, deep-learning-research, academic-papers, machine-learning-optimization, survey-papers
  • mediumlicense#3
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the content of the MIT License.

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 he-y/Awesome-Pruning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 1×
  2. PyTorch Mobile · recommended 1×
  3. PyTorch Quantization Toolkit · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. TensorFlow Model Optimization Toolkit · recommended 1×
  • CATEGORY QUERY
    How to reduce the size and improve inference speed of deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. PyTorch Quantization Toolkit
    4. ONNX Runtime
    5. TensorFlow Model Optimization Toolkit
    6. PyTorch Pruning
    7. NVIDIA's Automatic Mixed Precision (AMP)
    8. TensorRT
    9. Hugging Face Transformers
    10. DistilBERT
    11. DistilRoBERTa
    12. PyTorch
    13. TensorFlow
    14. AutoML
    15. Google Cloud AutoML
    16. Microsoft Azure Machine Learning
    17. EfficientNet
    18. MobileNet
    19. SqueezeNet
    20. OpenVINO Toolkit
    21. Apache TVM
    22. ONNX

    AI recommended 22 alternatives but never named he-y/Awesome-Pruning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey of neural network model compression techniques?
    you: not recommended
    AI recommended (in order):
    1. A Survey of Model Compression and Acceleration for Deep Neural Networks
    2. Deep Learning Model Compression: A Comprehensive Survey
    3. Model Compression and Acceleration for Deep Neural Networks: A Survey
    4. A Survey on Deep Neural Network Compression: From Model to Hardware
    5. Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
    6. Neural Network Pruning: A Survey
    7. Quantization for Deep Learning: A Comprehensive Survey

    AI recommended 7 alternatives but never named he-y/Awesome-Pruning. 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 he-y/Awesome-Pruning?
    pass
    AI named he-y/Awesome-Pruning explicitly

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

  • If a team adopts he-y/Awesome-Pruning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named he-y/Awesome-Pruning 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 he-y/Awesome-Pruning solve, and who is the primary audience?
    pass
    AI named he-y/Awesome-Pruning explicitly

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

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he-y/Awesome-Pruning — 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