RRepoGEO

REPOGEO REPORT · LITE

bubbliiiing/segformer-pytorch

Default branch master · commit e1e10edc · scanned 6/16/2026, 7:42:23 AM

GitHub: 503 stars · 53 forks

AI VISIBILITY SCORE
22 /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
1 / 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 bubbliiiing/segformer-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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    pytorch, segformer, semantic-segmentation, computer-vision, deep-learning, image-segmentation, custom-dataset-training
  • highabout#2
    Clarify the repository description to emphasize its purpose

    Why:

    CURRENT
    这是一个segformer-pytorch的源码，可以用于训练自己的模型。
    COPY-PASTE FIX
    一个完整的SegFormer PyTorch实现，专注于语义分割模型的训练、预测和评估，支持自定义数据集。
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/bubbliiiing/segformer-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
0 / 2
0% of queries surface bubbliiiing/segformer-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
qubvel/segmentation_models.pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. qubvel/segmentation_models.pytorch · recommended 2×
  2. Project-MONAI/MONAI · recommended 2×
  3. pytorch/vision · recommended 2×
  4. pytorch/ignite · recommended 1×
  5. rwightman/pytorch-image-models · recommended 1×
  • CATEGORY QUERY
    How can I implement semantic segmentation using PyTorch for custom dataset training?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Ignite (pytorch/ignite)
    2. segmentation_models.pytorch (qubvel/segmentation_models.pytorch)
    3. timm (rwightman/pytorch-image-models)
    4. Albumentations (albumentations-team/albumentations)
    5. MONAI (Project-MONAI/MONAI)
    6. PyTorch Lightning (Lightning-AI/lightning)
    7. torchvision.transforms (pytorch/vision)

    AI recommended 7 alternatives but never named bubbliiiing/segformer-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch implementations for training advanced image segmentation models?
    you: not recommended
    AI recommended (in order):
    1. segmentation_models.pytorch (qubvel/segmentation_models.pytorch)
    2. MMSegmentation (open-mmlab/mmsegmentation)
    3. Detectron2 (facebookresearch/detectron2)
    4. TorchVision (pytorch/vision)
    5. MONAI (Project-MONAI/MONAI)

    AI recommended 5 alternatives but never named bubbliiiing/segformer-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 bubbliiiing/segformer-pytorch?
    pass
    AI did not name bubbliiiing/segformer-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 bubbliiiing/segformer-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bubbliiiing/segformer-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 bubbliiiing/segformer-pytorch solve, and who is the primary audience?
    pass
    AI did not name bubbliiiing/segformer-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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bubbliiiing/segformer-pytorch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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bubbliiiing/segformer-pytorch — RepoGEO report