RRepoGEO

REPOGEO REPORT · LITE

coderonion/awesome-yolo-object-detection

Default branch main · commit 2e64f9d6 · scanned 5/21/2026, 9:07:50 PM

GitHub: 1,738 stars · 236 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
28 /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
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 coderonion/awesome-yolo-object-detection, 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
  • highlicense#1
    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, Apache-2.0, or GPL-3.0) in the repository root.
  • hightopics#2
    Add 'awesome-list' to repository topics

    Why:

    CURRENT
    cuda, datasets, deepseek, few-shot-object-detection, gui, llama, llm, mllm, object-detection, object-detection-datasets, open-world-object-detection, qwen, rknn, snn, spiking-neural-network, tensorrt, vlm, yolo, yolov5, yolov8
    COPY-PASTE FIX
    cuda, datasets, deepseek, few-shot-object-detection, gui, llama, llm, mllm, object-detection, object-detection-datasets, open-world-object-detection, qwen, rknn, snn, spiking-neural-network, tensorrt, vlm, yolo, yolov5, yolov8, awesome-list
  • mediumabout#3
    Enhance the repository description for clarity and assertiveness

    Why:

    CURRENT
    🚀🚀🚀 A collection of some awesome public YOLO object detection series projects and the related object detection datasets.
    COPY-PASTE FIX
    🚀🚀🚀 The definitive curated collection of public YOLO object detection projects, datasets, and learning resources.

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 coderonion/awesome-yolo-object-detection
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. Kaggle · recommended 1×
  3. GitHub · recommended 1×
  4. Roboflow · recommended 1×
  5. Google Dataset Search · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of real-time object detection projects and datasets?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Kaggle
    3. GitHub
    4. Roboflow
    5. Google Dataset Search
    6. Awesome Object Detection

    AI recommended 6 alternatives but never named coderonion/awesome-yolo-object-detection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a curated list of one-stage real-time object detection models and their code.
    you: not recommended
    AI recommended (in order):
    1. YOLOv8 (ultralytics/ultralytics)
    2. YOLOv7 (WongKinYiu/yolov7)
    3. YOLOv5 (ultralytics/yolov5)
    4. YOLOv4 (AlexeyAB/darknet)
    5. YOLOR (WongKinYiu/yolor)
    6. SSD (Single Shot MultiBox Detector) (NVIDIA/DeepLearningExamples)
    7. RetinaNet (pytorch/vision)
    8. EfficientDet (google/automl)
    9. NanoDet/NanoDet-Plus (RangiLyu/nanodet)

    AI recommended 9 alternatives but never named coderonion/awesome-yolo-object-detection. 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 coderonion/awesome-yolo-object-detection?
    pass
    AI named coderonion/awesome-yolo-object-detection explicitly

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

  • If a team adopts coderonion/awesome-yolo-object-detection in production, what risks or prerequisites should they evaluate first?
    pass
    AI named coderonion/awesome-yolo-object-detection 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 coderonion/awesome-yolo-object-detection solve, and who is the primary audience?
    pass
    AI did not name coderonion/awesome-yolo-object-detection — 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 coderonion/awesome-yolo-object-detection. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/coderonion/awesome-yolo-object-detection.svg)](https://repogeo.com/en/r/coderonion/awesome-yolo-object-detection)
HTML
<a href="https://repogeo.com/en/r/coderonion/awesome-yolo-object-detection"><img src="https://repogeo.com/badge/coderonion/awesome-yolo-object-detection.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

coderonion/awesome-yolo-object-detection — 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