REPOGEO REPORT · LITE

tangzhenyu/SemanticSegmentation_DL

Default branch master · commit 03cd48f0 · scanned 5/23/2026, 5:32:45 PM

GitHub: 1,105 stars · 313 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

23 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 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 tangzhenyu/SemanticSegmentation_DL, 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

deep-learning, semantic-segmentation, computer-vision, image-segmentation, pytorch, tensorflow, machine-learning, research, resources

highlicense#2

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository to clearly state the terms of use.

mediumreadme#3

Clarify the repository's purpose in the README's opening

Why:

CURRENT

# Semantic-Segmentation
A list of all papers and resoureces on Semantic Segmentation.

COPY-PASTE FIX

# Semantic Segmentation Deep Learning Implementations and Resources
This repository provides a curated collection of deep learning model implementations and comprehensive resources for semantic image segmentation tasks, including papers, datasets, and code examples.

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 tangzhenyu/SemanticSegmentation_DL

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers & Datasets

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers & Datasets · recommended 1×
PyTorch Hub / torchvision.models.segmentation · recommended 1×
TensorFlow Hub / Keras Applications · recommended 1×
MMDetection / MMSegmentation · recommended 1×
Papers With Code · recommended 1×

CATEGORY QUERY
Where can I find deep learning models and resources for semantic image segmentation tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers & Datasets
2. PyTorch Hub / torchvision.models.segmentation
3. TensorFlow Hub / Keras Applications
4. MMDetection / MMSegmentation
5. Papers With Code
6. GitHub
7. Kaggle
AI recommended 7 alternatives but never named tangzhenyu/SemanticSegmentation_DL. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best deep learning techniques for pixel-level image classification and object delineation?
you: not recommended
AI recommended (in order):
1. Mask R-CNN
2. U-Net
3. DeepLab
4. YOLO
5. YOLACT
6. FCN
7. PANet
AI recommended 7 alternatives but never named tangzhenyu/SemanticSegmentation_DL. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
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 tangzhenyu/SemanticSegmentation_DL?
pass
AI named tangzhenyu/SemanticSegmentation_DL explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts tangzhenyu/SemanticSegmentation_DL in production, what risks or prerequisites should they evaluate first?
pass
AI named tangzhenyu/SemanticSegmentation_DL 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 tangzhenyu/SemanticSegmentation_DL solve, and who is the primary audience?
pass
AI did not name tangzhenyu/SemanticSegmentation_DL — 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

Drop this badge into the README of tangzhenyu/SemanticSegmentation_DL. 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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Pro

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tangzhenyu/SemanticSegmentation_DL — 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