RRepoGEO

REPOGEO REPORT · LITE

akanimax/pro_gan_pytorch

Default branch master · commit 62066139 · scanned 5/29/2026, 10:57:53 AM

GitHub: 541 stars · 98 forks

AI VISIBILITY SCORE
56 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
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 akanimax/pro_gan_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
    Rephrase README introduction to highlight problem-solving benefits

    Why:

    CURRENT
    # pro_gan_pytorch
**Unofficial PyTorch** implementation of Paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation".
    COPY-PASTE FIX
    # pro_gan_pytorch
This repository provides an unofficial PyTorch implementation of Progressive Growing of GANs (ProGAN), a technique designed to significantly improve the quality, stability, and variation of images generated by GANs. It addresses common challenges in training high-resolution generative models, making it ideal for researchers and practitioners focused on advanced image synthesis.
  • mediumreadme#2
    Add a sentence clarifying the project's unique position/differentiator

    Why:

    COPY-PASTE FIX
    As one of the earliest and most widely adopted PyTorch implementations of ProGAN, this project has served as a foundational resource for many in the generative AI community.
  • lowhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/akanimax/pro_gan_pytorch

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
1 / 2
50% of queries surface akanimax/pro_gan_pytorch
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
lucidrains/stylegan2-pytorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. lucidrains/stylegan2-pytorch · recommended 1×
  2. NVlabs/progressive_growing_of_gans · recommended 1×
  3. facebookresearch/pytorch_GAN_zoo · recommended 1×
  4. rosinality/progressive-growing-of-gans-pytorch · recommended 1×
  5. eriklindernoren/PyTorch-GAN · recommended 1×
  • CATEGORY QUERY
    Looking for a PyTorch implementation of progressive GANs for high-quality image generation.
    you: #3
    AI recommended (in order):
    1. lucidrains/stylegan2-pytorch (lucidrains/stylegan2-pytorch)
    2. NVlabs/progressive_growing_of_gans (NVlabs/progressive_growing_of_gans)
    3. akanimax/pro_gan_pytorch (akanimax/pro_gan_pytorch) ← you
    4. facebookresearch/pytorch_GAN_zoo (facebookresearch/pytorch_GAN_zoo)
    5. rosinality/progressive-growing-of-gans-pytorch (rosinality/progressive-growing-of-gans-pytorch)
    6. eriklindernoren/PyTorch-GAN (eriklindernoren/PyTorch-GAN)
    Show full AI answer
  • CATEGORY QUERY
    How to improve GAN image quality and training stability using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-GAN
    2. Lightweight GAN
    3. Keras-GAN
    4. InceptionV3
    5. PyTorch Lightning
    6. torch.optim.AdamW
    7. torch.nn.utils.spectral_norm

    AI recommended 7 alternatives but never named akanimax/pro_gan_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 akanimax/pro_gan_pytorch?
    pass
    AI did not name akanimax/pro_gan_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?

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

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

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akanimax/pro_gan_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