RRepoGEO

REPOGEO REPORT · LITE

bubbliiiing/yolov7-pytorch

Default branch master · commit 170bf5bc · scanned 6/15/2026, 10:36:47 AM

GitHub: 910 stars · 156 forks

AI VISIBILITY SCORE
35 /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
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 bubbliiiing/yolov7-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 comprehensive topics for better categorization

    Why:

    COPY-PASTE FIX
    pytorch, yolov7, object-detection, deep-learning, computer-vision, machine-learning, custom-dataset-training, multi-gpu, real-time-object-detection
  • highreadme#2
    Add a concise English opening statement to the README

    Why:

    COPY-PASTE FIX
    This repository provides a user-friendly PyTorch implementation of YOLOv7, optimized for training custom object detection models with features like multi-GPU acceleration and various learning rate schedulers. (Add this sentence immediately after the main H1 title.)
  • mediumabout#3
    Provide a clear English description for the repository

    Why:

    CURRENT
    这是一个yolov7的库，可以用于训练自己的数据集。
    COPY-PASTE FIX
    A PyTorch implementation of YOLOv7 for training custom object detection models, featuring multi-GPU support, various optimizers, and learning rate schedulers.

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/yolov7-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TensorFlow · recommended 1×
  3. MMDetection · recommended 1×
  4. JAX · recommended 1×
  5. MXNet · recommended 1×
  • CATEGORY QUERY
    Looking for a robust deep learning framework to train custom object detection models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. MMDetection
    4. JAX
    5. MXNet

    AI recommended 5 alternatives but never named bubbliiiing/yolov7-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which PyTorch libraries offer efficient object detection model training with multi-GPU acceleration?
    you: not recommended
    AI recommended (in order):
    1. MMDetection (open-mmlab/mmdetection)
    2. Detectron2 (facebookresearch/detectron2)
    3. Ultralytics YOLOv5 (ultralytics/yolov5)
    4. Ultralytics YOLOv8 (ultralytics/yolov8)
    5. PyTorch-Lightning (Lightning-AI/pytorch-lightning)
    6. torchvision (pytorch/vision)

    AI recommended 6 alternatives but never named bubbliiiing/yolov7-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/yolov7-pytorch?
    pass
    AI named bubbliiiing/yolov7-pytorch explicitly

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

  • If a team adopts bubbliiiing/yolov7-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bubbliiiing/yolov7-pytorch 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 bubbliiiing/yolov7-pytorch solve, and who is the primary audience?
    pass
    AI named bubbliiiing/yolov7-pytorch explicitly

    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 bubbliiiing/yolov7-pytorch. 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/bubbliiiing/yolov7-pytorch.svg)](https://repogeo.com/en/r/bubbliiiing/yolov7-pytorch)
HTML
<a href="https://repogeo.com/en/r/bubbliiiing/yolov7-pytorch"><img src="https://repogeo.com/badge/bubbliiiing/yolov7-pytorch.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

bubbliiiing/yolov7-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