REPOGEO REPORT · LITE

open-mmlab/mmsegmentation

Default branch main · commit b040e147 · scanned 6/21/2026, 6:11:45 AM

GitHub: 9,857 stars · 2,846 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

74 /100

Needs work

Category recall

1 / 2

Avg rank #1.0 when recommended

Rule findings

2 pass · 0 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 open-mmlab/mmsegmentation, 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

Add a sentence to the README's introduction or a prominent section highlighting medical applications.

Why:

COPY-PASTE FIX

Integrate a sentence like 'MMSegmentation is also highly effective for specialized tasks such as real-time medical image segmentation, including detailed analysis of retinal vessels.' into the introductory section of the README or a dedicated 'Key Features' section.

mediumreadme#2

Ensure the README's opening paragraph clearly states the project's full scope.

Why:

COPY-PASTE FIX

Review the very first paragraph of the README to ensure it clearly and concisely communicates the project's full scope, including its role as a 'Semantic Segmentation Toolbox and Benchmark' for 'efficiently implementing, training, and evaluating state-of-the-art deep learning models for pixel-level image segmentation,' as stated in the description.

lowexamples#3

Add specific examples or a dedicated section for medical image segmentation use cases.

Why:

COPY-PASTE FIX

Create a new section in the README or link to a dedicated example/tutorial page that specifically showcases MMSegmentation's application in medical imaging, such as segmenting retinal vessels, to provide concrete evidence for AI assistants.

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

1 / 2

50% of queries surface open-mmlab/mmsegmentation

Avg rank

#1.0

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pytorch/ignite

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pytorch/ignite · recommended 1×
qubvel/segmentation_models.pytorch · recommended 1×
facebookresearch/detectron2 · recommended 1×
pytorch/vision · recommended 1×
catalyst-team/catalyst · recommended 1×

CATEGORY QUERY
How to perform semantic segmentation on images using a PyTorch-based framework?
you: #1
AI recommended (in order):
1. MMSegmentation (open-mmlab/mmsegmentation) ← you
2. PyTorch-Ignite (pytorch/ignite)
3. Segmentation Models PyTorch (smp) (qubvel/segmentation_models.pytorch)
4. Detectron2 (facebookresearch/detectron2)
5. torchvision.models.segmentation (pytorch/vision)
6. Catalyst (catalyst-team/catalyst)
Show full AI answer
CATEGORY QUERY
What are the best tools for real-time medical image segmentation, especially for retinal vessels?
you: not recommended
AI recommended (in order):
1. MONAI
2. PyTorch
3. Albumentations
4. OpenCV
5. TensorFlow
6. Keras
7. imgaug
8. TensorFlow Lite
9. NVIDIA Clara Train SDK
10. OpenVINO Toolkit
11. FastAI
AI recommended 11 alternatives but never named open-mmlab/mmsegmentation. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 open-mmlab/mmsegmentation?
pass
AI named open-mmlab/mmsegmentation explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts open-mmlab/mmsegmentation in production, what risks or prerequisites should they evaluate first?
pass
AI named open-mmlab/mmsegmentation 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 open-mmlab/mmsegmentation solve, and who is the primary audience?
pass
AI named open-mmlab/mmsegmentation explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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open-mmlab/mmsegmentation — 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