RRepoGEO

REPOGEO REPORT · LITE

rom1504/img2dataset

Default branch main · commit 95523bc7 · scanned 5/19/2026, 9:22:07 PM

GitHub: 4,416 stars · 374 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
68 /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
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 rom1504/img2dataset, 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
    Reposition README opening to emphasize ML dataset preparation

    Why:

    CURRENT
    Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
    COPY-PASTE FIX
    img2dataset is a highly scalable tool for machine learning engineers and researchers to efficiently build large image datasets from URLs. It can download, resize, and package billions of images into ML-ready formats (like WebDataset, TFRecord, Parquet) in hours, specifically designed for deep learning training.
  • highhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/rom1504/img2dataset
  • mediumtopics#3
    Expand repository topics with ML data engineering and format-specific terms

    Why:

    CURRENT
    big-data, dataset, deep-learning, download-images, image, image-dataset, multimodal
    COPY-PASTE FIX
    big-data, dataset, deep-learning, download-images, image, image-dataset, multimodal, ml-data-preparation, data-engineering-ml, webdataset, tfrecord, parquet

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 rom1504/img2dataset
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
concurrent.futures
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. concurrent.futures · recommended 2×
  2. psf/requests · recommended 1×
  3. aio-libs/aiohttp · recommended 1×
  4. wget · recommended 1×
  5. curl/curl · recommended 1×
  • CATEGORY QUERY
    How to efficiently build a large image dataset from a list of URLs?
    you: #1
    AI recommended (in order):
    1. img2dataset (romain-lopez/img2dataset) ← you
    2. requests (psf/requests)
    3. concurrent.futures
    4. aiohttp (aio-libs/aiohttp)
    5. wget
    6. curl (curl/curl)
    7. scrapy (scrapy/scrapy)
    Show full AI answer
  • CATEGORY QUERY
    Need a tool to download and preprocess millions of images for deep learning training.
    you: not recommended
    AI recommended (in order):
    1. FiftyOne
    2. Apache Spark
    3. Spark MLlib
    4. Spark SQL
    5. Dask
    6. Google Cloud Dataflow
    7. Apache Beam
    8. AWS Step Functions
    9. AWS Lambda
    10. AWS Batch
    11. requests
    12. Pillow
    13. OpenCV
    14. tqdm
    15. concurrent.futures
    16. Scrapy

    AI recommended 16 alternatives but never named rom1504/img2dataset. 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 rom1504/img2dataset?
    pass
    AI named rom1504/img2dataset explicitly

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

  • If a team adopts rom1504/img2dataset in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rom1504/img2dataset 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 rom1504/img2dataset solve, and who is the primary audience?
    pass
    AI named rom1504/img2dataset explicitly

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

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rom1504/img2dataset — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite