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visual-layer
fastdup
Python2022–2023
visual-layer / fastdup

fastdup

fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

Data & DatabasesCreative & MediaAI & Machine LearningLearning & Reference
Explore on GitHubRead Hacker News discussions
2Distinct people
4Mentions
0Explicit recommenders
1.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week4 dated mentions

Observed mentions: 2022-12-13 02:38:36 UTC to 2023-05-02 07:03:08 UTC. Weeks start Monday, UTC.

012022-12-12 00:00:00 UTC to 2022-12-19 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 122022-12-19 00:00:00 UTC to 2022-12-26 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-12-26 00:00:00 UTC to 2023-01-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-02 00:00:00 UTC to 2023-01-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-09 00:00:00 UTC to 2023-01-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-16 00:00:00 UTC to 2023-01-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-23 00:00:00 UTC to 2023-01-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-30 00:00:00 UTC to 2023-02-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-06 00:00:00 UTC to 2023-02-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-13 00:00:00 UTC to 2023-02-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-20 00:00:00 UTC to 2023-02-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsFeb 202023-02-27 00:00:00 UTC to 2023-03-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-06 00:00:00 UTC to 2023-03-13 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-03-13 00:00:00 UTC to 2023-03-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-20 00:00:00 UTC to 2023-03-27 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023-03-27 00:00:00 UTC to 2023-04-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-03 00:00:00 UTC to 2023-04-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-10 00:00:00 UTC to 2023-04-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-17 00:00:00 UTC to 2023-04-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-24 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-01 00:00:00 UTC to 2023-05-08 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMay 1

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 122022-12-12 00:00:00 UTC2022-12-19 00:00:00 UTC101
Dec 192022-12-19 00:00:00 UTC2022-12-26 00:00:00 UTC000
Dec 262022-12-26 00:00:00 UTC2023-01-02 00:00:00 UTC000
Jan 22023-01-02 00:00:00 UTC2023-01-09 00:00:00 UTC000
Jan 92023-01-09 00:00:00 UTC2023-01-16 00:00:00 UTC000
Jan 162023-01-16 00:00:00 UTC2023-01-23 00:00:00 UTC000
Jan 232023-01-23 00:00:00 UTC2023-01-30 00:00:00 UTC000
Jan 302023-01-30 00:00:00 UTC2023-02-06 00:00:00 UTC000
Feb 62023-02-06 00:00:00 UTC2023-02-13 00:00:00 UTC000
Feb 132023-02-13 00:00:00 UTC2023-02-20 00:00:00 UTC000
Feb 202023-02-20 00:00:00 UTC2023-02-27 00:00:00 UTC000
Feb 272023-02-27 00:00:00 UTC2023-03-06 00:00:00 UTC000
Mar 62023-03-06 00:00:00 UTC2023-03-13 00:00:00 UTC101
Mar 132023-03-13 00:00:00 UTC2023-03-20 00:00:00 UTC000
Mar 202023-03-20 00:00:00 UTC2023-03-27 00:00:00 UTC110
Mar 272023-03-27 00:00:00 UTC2023-04-03 00:00:00 UTC000
Apr 32023-04-03 00:00:00 UTC2023-04-10 00:00:00 UTC000
Apr 102023-04-10 00:00:00 UTC2023-04-17 00:00:00 UTC000
Apr 172023-04-17 00:00:00 UTC2023-04-24 00:00:00 UTC000
Apr 242023-04-24 00:00:00 UTC2023-05-01 00:00:00 UTC000
May 12023-05-01 00:00:00 UTC2023-05-08 00:00:00 UTC110

What Hacker News says

4 sources
dnth·May 2, 2023Submission

Visualize your dataset using DINOv2 embedding

Visualizing your dataset (especially large ones) in a low-dimensional embedding space can tell you a lot about the patterns and clusters in your dataset. We recently release a notebook showing how you can visualize your dataset using DINOv2 models by running it on your CPU. Yes! No GPUs needed. We used it to find clusters of similar images, duplicates, and outliers in a subset of the LAION dataset Try it on your own dataset: Colab notebook - https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/d… GitHub repo - https://github.com/visual-layer/fastdup
1 points · Open discussion

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