RRepoGEO

REPOGEO REPORT · LITE

hysts/pytorch_image_classification

Default branch master · commit 993089a3 · scanned 5/10/2026, 9:37:29 PM

GitHub: 1,443 stars · 306 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 hysts/pytorch_image_classification, 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
    Clarify the repo's unique value proposition in the README's opening

    Why:

    CURRENT
    # PyTorch Image Classification

Following papers are implemented using PyTorch.
    COPY-PASTE FIX
    # PyTorch Image Classification

This repository provides reproducible PyTorch implementations of various image classification models and data augmentation methods. It's designed for researchers and practitioners to easily experiment with and reproduce results from specific papers on standard datasets like CIFAR-10/100, ImageNet, and MNIST variants.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    cifar10, computer-vision, fashion-mnist, imagenet, pytorch
    COPY-PASTE FIX
    cifar10, computer-vision, fashion-mnist, imagenet, pytorch, deep-learning-models, model-reproduction, data-augmentation, convolutional-neural-networks, research-implementations
  • mediumhomepage#3
    Populate the 'About' section's homepage field

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project page, documentation, or the repository URL itself if no external site exists) to the 'About' section's homepage field.

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 hysts/pytorch_image_classification
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rwightman/pytorch-image-models
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. rwightman/pytorch-image-models · recommended 1×
  2. pytorch/vision · recommended 1×
  3. Lightning-AI/lightning-flash · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. catalyst-team/catalyst · recommended 1×
  • CATEGORY QUERY
    I need a PyTorch library for implementing popular image classification models on standard datasets.
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Image-Models (timm) (rwightman/pytorch-image-models)
    2. torchvision.models (pytorch/vision)
    3. Lightning-Flash (Lightning-AI/lightning-flash)
    4. Hugging Face Transformers (huggingface/transformers)
    5. Catalyst (catalyst-team/catalyst)

    AI recommended 5 alternatives but never named hysts/pytorch_image_classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which PyTorch frameworks offer implementations of modern image classification architectures and augmentation methods?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Image Models (timm)
    2. Albumentations
    3. PyTorch Lightning
    4. torchvision
    5. Kornia

    AI recommended 5 alternatives but never named hysts/pytorch_image_classification. 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 hysts/pytorch_image_classification?
    pass
    AI named hysts/pytorch_image_classification explicitly

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

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