RRepoGEO

REPOGEO REPORT · LITE

MIC-DKFZ/nnDetection

Default branch main · commit 97a58f31 · scanned 6/11/2026, 5:02:15 AM

GitHub: 643 stars · 122 forks

AI VISIBILITY SCORE
62 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 MIC-DKFZ/nnDetection, 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 prominent tagline to the README

    Why:

    COPY-PASTE FIX
    Add this line as the very first visible text in your README, perhaps as a tagline under the main title: 'nnDetection: The self-configuring framework for automated 3D medical object detection.'
  • hightopics#2
    Expand and refine repository topics

    Why:

    CURRENT
    3d-object-detection, detection, medical, medical-image-computing, medical-imaging, pytorch-implementation, retina-unet
    COPY-PASTE FIX
    3d-object-detection, detection, medical, medical-image-computing, medical-imaging, pytorch-implementation, retina-unet, self-configuring-framework, automated-detection, volumetric-detection, medical-ai
  • mediumhomepage#3
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    Add the official project homepage or documentation URL to the 'About' section of the repository.

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 MIC-DKFZ/nnDetection
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
Project-MONAI/MONAI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Project-MONAI/MONAI · recommended 1×
  2. deepmind/alphafold · recommended 1×
  3. optuna/optuna · recommended 1×
  4. ray-project/ray · recommended 1×
  5. Google Cloud AutoML Vision · recommended 1×
  • CATEGORY QUERY
    Looking for a self-configuring framework to automate 3D medical object detection on new datasets.
    you: #1
    AI recommended (in order):
    1. nnDetection (MIC-DKFZ/nnDetection) ← you
    2. MONAI (Project-MONAI/MONAI)
    3. DeepMind's AlphaFold (deepmind/alphafold)
    4. Optuna (optuna/optuna)
    5. Ray Tune (ray-project/ray)
    6. Google Cloud AutoML Vision
    7. Azure Machine Learning
    8. H2O.ai
    9. MMDetection3D (open-mmlab/mmdetection3d)
    Show full AI answer
  • CATEGORY QUERY
    How to automatically detect objects in volumetric medical images without extensive manual configuration?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. nnU-Net
    3. DeepMind's AlphaFold
    4. 3D Slicer
    5. ITK-SNAP
    6. OpenVINO Toolkit

    AI recommended 6 alternatives but never named MIC-DKFZ/nnDetection. 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 MIC-DKFZ/nnDetection?
    pass
    AI did not name MIC-DKFZ/nnDetection — 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 MIC-DKFZ/nnDetection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MIC-DKFZ/nnDetection 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 MIC-DKFZ/nnDetection solve, and who is the primary audience?
    pass
    AI named MIC-DKFZ/nnDetection explicitly

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

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MIC-DKFZ/nnDetection — 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