RRepoGEO

REPOGEO REPORT · LITE

ajbrock/BigGAN-PyTorch

Default branch master · commit 98459431 · scanned 6/20/2026, 4:48:30 PM

GitHub: 2,925 stars · 490 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
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 ajbrock/BigGAN-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 opening to emphasize large-scale, multi-GPU PyTorch BigGAN training

    Why:

    CURRENT
    The author's officially unofficial PyTorch BigGAN implementation.
    COPY-PASTE FIX
    A high-fidelity, large-scale PyTorch BigGAN implementation, optimized for multi-GPU training with gradient accumulation.
  • mediumtopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    biggan, deep-learning, dogball, gans, neural-networks, pytorch
    COPY-PASTE FIX
    biggan, deep-learning, dogball, gans, neural-networks, pytorch, multi-gpu, distributed-training, gradient-accumulation, high-fidelity-image-synthesis
  • lowabout#3
    Update the repository's 'About' description

    Why:

    CURRENT
    The author's officially unofficial PyTorch BigGAN implementation.
    COPY-PASTE FIX
    A high-fidelity, large-scale PyTorch BigGAN implementation, optimized for multi-GPU training with gradient accumulation.

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 ajbrock/BigGAN-PyTorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVlabs/stylegan3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVlabs/stylegan3 · recommended 1×
  2. NVlabs/stylegan2-ada-pytorch · recommended 1×
  3. BigGAN · recommended 1×
  4. huggingface/diffusers · recommended 1×
  5. VQGAN + CLIP · recommended 1×
  • CATEGORY QUERY
    How to implement high-fidelity image synthesis using generative adversarial networks in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. StyleGAN3 (NVlabs/stylegan3)
    2. StyleGAN2-ADA (NVlabs/stylegan2-ada-pytorch)
    3. BigGAN
    4. Diffusers (huggingface/diffusers)
    5. VQGAN + CLIP
    6. ProGAN (NVlabs/progressive_growing_of_gans)

    AI recommended 6 alternatives but never named ajbrock/BigGAN-PyTorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch solution for large-scale GAN training with multiple GPUs and gradient accumulation.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Accelerate
    3. DeepSpeed
    4. torch.nn.parallel.DistributedDataParallel
    5. torch.nn.DataParallel

    AI recommended 5 alternatives but never named ajbrock/BigGAN-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 ajbrock/BigGAN-PyTorch?
    pass
    AI did not name ajbrock/BigGAN-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 ajbrock/BigGAN-PyTorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ajbrock/BigGAN-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 ajbrock/BigGAN-PyTorch solve, and who is the primary audience?
    pass
    AI named ajbrock/BigGAN-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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ajbrock/BigGAN-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