RRepoGEO

REPOGEO REPORT · LITE

xuannianz/EfficientDet

Default branch master · commit 030fb7e1 · scanned 5/22/2026, 8:37:33 PM

GitHub: 1,451 stars · 392 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
66 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 xuannianz/EfficientDet, 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
  • highabout#1
    Update repository description to highlight unique features

    Why:

    CURRENT
    EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
    COPY-PASTE FIX
    EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow, with support for quadrangle and oriented bounding box detection.
  • mediumreadme#2
    Add a dedicated "Features" section to the README

    Why:

    COPY-PASTE FIX
    Add a new `## Features` section to the README, ideally before `## Train`, listing key capabilities such as:
- Quadrangle and Oriented Bounding Box Detection (referencing `README_quad.md`)
- Anchor-free version for faster and smaller models
  • lowhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/xuannianz/EfficientDet

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 xuannianz/EfficientDet
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
11%
Of all named tools, what % are you?
Top rival
tensorflow/models
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/models · recommended 2×
  2. ultralytics/ultralytics · recommended 1×
  3. ultralytics/yolov5 · recommended 1×
  4. Keras-RetinaNet · recommended 1×
  5. TensorFlow Object Detection API · recommended 1×
  • CATEGORY QUERY
    Looking for an efficient and scalable object detection model using Keras and TensorFlow.
    you: #2
    AI recommended (in order):
    1. YOLOv8 (ultralytics/ultralytics)
    2. EfficientDet (tensorflow/models) ← you
    3. YOLOv5 (ultralytics/yolov5)
    4. RetinaNet (tensorflow/models)
    5. Faster R-CNN (tensorflow/models)
    Show full AI answer
  • CATEGORY QUERY
    Need a Keras-based object detection solution supporting quadrangle or oriented bounding boxes.
    you: not recommended
    AI recommended (in order):
    1. Keras-RetinaNet
    2. TensorFlow Object Detection API
    3. Keras-YOLOv4/YOLOv5
    4. Keras-OCR

    AI recommended 4 alternatives but never named xuannianz/EfficientDet. 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 xuannianz/EfficientDet?
    pass
    AI named xuannianz/EfficientDet explicitly

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

  • If a team adopts xuannianz/EfficientDet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xuannianz/EfficientDet 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 xuannianz/EfficientDet solve, and who is the primary audience?
    pass
    AI named xuannianz/EfficientDet explicitly

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

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xuannianz/EfficientDet — 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