RRepoGEO

REPOGEO REPORT · LITE

bubbliiiing/yolov4-tiny-pytorch

Default branch master · commit 1bbb2f28 · scanned 6/15/2026, 1:17:06 PM

GitHub: 825 stars · 183 forks

AI VISIBILITY SCORE
15 /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
0 / 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 bubbliiiing/yolov4-tiny-pytorch, 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
    yolov4-tiny, pytorch, object-detection, deep-learning, computer-vision, yolo, training, custom-training
  • highabout#2
    Update repository description to English

    Why:

    CURRENT
    这是一个YoloV4-tiny-pytorch的源码，可以用于训练自己的模型。
    COPY-PASTE FIX
    A PyTorch implementation of YOLOv4-tiny for custom object detection model training.
  • mediumreadme#3
    Add a concise English summary to the README introduction

    Why:

    CURRENT
    ## YOLOV4-Tiny：You Only Look Once-Tiny目标检测模型在Pytorch当中的实现
## 目录
    COPY-PASTE FIX
    ## YOLOV4-Tiny：You Only Look Once-Tiny目标检测模型在Pytorch当中的实现
This repository provides a complete PyTorch implementation of the YOLOv4-tiny object detection model, optimized for training custom datasets and real-time inference.
## 目录

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 bubbliiiing/yolov4-tiny-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ultralytics/yolov5
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ultralytics/yolov5 · recommended 2×
  2. ultralytics/yolov8 · recommended 1×
  3. RangiLyu/nanodet-plus · recommended 1×
  4. EfficientDet · recommended 1×
  5. PicoDet · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight object detection model implementation in PyTorch for custom training.
    you: not recommended
    AI recommended (in order):
    1. YOLOv5 (ultralytics/yolov5)
    2. YOLOv8 (ultralytics/yolov8)
    3. NanoDet-Plus (RangiLyu/nanodet-plus)
    4. EfficientDet
    5. PicoDet

    AI recommended 5 alternatives but never named bubbliiiing/yolov4-tiny-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I train a fast Yolo-style object detector on my own dataset using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Ultralytics YOLO (ultralytics/ultralytics)
    2. PyTorch-YOLOv3/YOLOv4 (ultralytics/yolov5)
    3. MMDetection (open-mmlab/mmdetection)
    4. YOLOX (Megvii-BaseDetection/YOLOX)
    5. LabelImg (tzutalin/labelImg)
    6. CVAT (opencv/cvat)
    7. Roboflow

    AI recommended 7 alternatives but never named bubbliiiing/yolov4-tiny-pytorch. 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 bubbliiiing/yolov4-tiny-pytorch?
    pass
    AI did not name bubbliiiing/yolov4-tiny-pytorch — 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 bubbliiiing/yolov4-tiny-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name bubbliiiing/yolov4-tiny-pytorch — 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?

  • In one sentence, what problem does the repo bubbliiiing/yolov4-tiny-pytorch solve, and who is the primary audience?
    pass
    AI did not name bubbliiiing/yolov4-tiny-pytorch — 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

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bubbliiiing/yolov4-tiny-pytorch — 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