REPOGEO REPORT · LITE

hysts/pytorch_image_classification

Default branch master · commit 993089a3 · scanned 6/20/2026, 11:02:54 PM

GitHub: 1,444 stars · 307 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

35 /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

3 / 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

Reposition README opening to clarify project scope and differentiator

Why:

CURRENT

# PyTorch Image Classification

Following papers are implemented using PyTorch.

COPY-PASTE FIX

# PyTorch Image Classification

This repository provides a comprehensive and modular PyTorch framework for implementing, training, and benchmarking a wide range of image classification models. It includes state-of-the-art architectures from numerous research papers, designed for datasets like CIFAR-10/100, MNIST, FashionMNIST, Kuzushiji-MNIST, and ImageNet.

highhomepage#2
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/hysts/pytorch_image_classification
```

mediumtopics#3

Expand repository topics with more specific keywords

Why:

CURRENT

cifar10, computer-vision, fashion-mnist, imagenet, pytorch

COPY-PASTE FIX

cifar10, computer-vision, fashion-mnist, imagenet, pytorch, deep-learning-models, model-zoo, image-recognition, research-framework, benchmarking

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

Of all named tools, what % are you?

Top rival

pytorch/vision

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pytorch/vision · recommended 1×
rwightman/pytorch-image-models · recommended 1×
qubvel/segmentation_models.pytorch · recommended 1×
PavelPleskov/pytorchcv · recommended 1×
PyTorch Lightning · recommended 1×

CATEGORY QUERY
What are good PyTorch implementations for common image classification models like ResNet or DenseNet?
you: not recommended
AI recommended (in order):
1. torchvision.models (pytorch/vision)
2. timm (rwightman/pytorch-image-models)
3. segmentation_models.pytorch (qubvel/segmentation_models.pytorch)
4. pytorchcv (PavelPleskov/pytorchcv)
AI recommended 4 alternatives but never named hysts/pytorch_image_classification. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I efficiently train deep learning models for image classification on CIFAR-10 datasets?
you: not recommended
AI recommended (in order):
1. PyTorch Lightning
2. Keras
3. Fastai
4. TensorFlow
5. Hugging Face Accelerate
6. Catalyst
7. Optuna
AI recommended 7 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 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?

Embed your GEO score

Drop this badge into the README of hysts/pytorch_image_classification. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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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