RRepoGEO

REPOGEO REPORT · LITE

rwightman/gen-efficientnet-pytorch

Default branch master · commit 771ce082 · scanned 5/26/2026, 6:33:24 AM

GitHub: 1,583 stars · 216 forks

AI VISIBILITY SCORE
28 /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
2 / 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 rwightman/gen-efficientnet-pytorch, 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's unmaintained status

    Why:

    CURRENT
    NOTE** This repo is not being maintained --
Please use `timm` instead.
    COPY-PASTE FIX
    This repository provides foundational PyTorch implementations and pretrained weights for EfficientNet, MixNet, MobileNetV3, and similar architectures. While not actively maintained, its models and weights are compatible with `timm` and serve as a valuable reference for these efficient vision models. For active development, please use `timm`.
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the 'Homepage' field in the repository settings (e.g., a project page, documentation, or a link to the `timm` library).
  • lowtopics#3
    Expand repository topics

    Why:

    CURRENT
    caffe2, efficientnet, fbnet, mnasnet, mobilenetv3, onnx, pretrained-models, pytorch
    COPY-PASTE FIX
    caffe2, efficientnet, efficientnet-lite, fbnet, mnasnet, mobilenetv2, mobilenetv3, onnx, pretrained-models, pytorch, single-path-nas

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 rwightman/gen-efficientnet-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MobileNetV3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MobileNetV3 · recommended 1×
  2. EfficientNetV2 · recommended 1×
  3. MobileNetV2 · recommended 1×
  4. ShuffleNetV2 · recommended 1×
  5. SqueezeNet · recommended 1×
  • CATEGORY QUERY
    Looking for pre-trained compact neural network models for general computer vision tasks.
    you: not recommended
    AI recommended (in order):
    1. MobileNetV3
    2. EfficientNetV2
    3. MobileNetV2
    4. ShuffleNetV2
    5. SqueezeNet
    6. GhostNet

    AI recommended 6 alternatives but never named rwightman/gen-efficientnet-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to find optimized vision architectures with exportable weights for mobile or edge deployment?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite Model Zoo
    2. TensorFlow Model Optimization Toolkit
    3. PyTorch Mobile
    4. TorchVision
    5. ONNX Runtime
    6. ONNX Model Zoo
    7. OpenVINO Toolkit
    8. Open Model Zoo
    9. NVIDIA TensorRT
    10. Edge Impulse
    11. MediaPipe

    AI recommended 11 alternatives but never named rwightman/gen-efficientnet-pytorch. 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 rwightman/gen-efficientnet-pytorch?
    pass
    AI named rwightman/gen-efficientnet-pytorch explicitly

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

  • If a team adopts rwightman/gen-efficientnet-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rwightman/gen-efficientnet-pytorch 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 rwightman/gen-efficientnet-pytorch solve, and who is the primary audience?
    pass
    AI did not name rwightman/gen-efficientnet-pytorch — 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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rwightman/gen-efficientnet-pytorch — 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