RRepoGEO

REPOGEO REPORT · LITE

lyhue1991/torchkeras

Default branch master · commit c7afdb98 · scanned 5/9/2026, 1:56:57 AM

GitHub: 2,009 stars · 253 forks

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 lyhue1991/torchkeras, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    pytorch, keras, deep-learning, machine-learning, training-loop, model-training, ai, python
  • lowhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/lyhue1991/torchkeras

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 lyhue1991/torchkeras
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Lightning
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 2×
  2. Hugging Face Accelerate · recommended 2×
  3. Catalyst · recommended 2×
  4. Keras 3 · recommended 1×
  5. Ignite · recommended 1×
  • CATEGORY QUERY
    How to get Keras-style model training and evaluation API in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Keras 3
    3. Hugging Face Accelerate
    4. Catalyst
    5. Ignite
    6. Fastai
    7. TorchMetrics

    AI recommended 7 alternatives but never named lyhue1991/torchkeras. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a simple, customizable PyTorch training loop abstraction to reduce boilerplate code.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Hugging Face Accelerate
    3. Catalyst
    4. ignite
    5. simple_parsing

    AI recommended 5 alternatives but never named lyhue1991/torchkeras. 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 lyhue1991/torchkeras?
    pass
    AI named lyhue1991/torchkeras explicitly

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

  • If a team adopts lyhue1991/torchkeras in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lyhue1991/torchkeras 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 lyhue1991/torchkeras solve, and who is the primary audience?
    pass
    AI named lyhue1991/torchkeras explicitly

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

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lyhue1991/torchkeras — 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