HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
facebookresearch
segment-anything
Jupyter Notebook2023–2024
facebookresearch / segment-anything

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
21Distinct people
24Mentions
1Explicit recommenders
55.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Yeah, they are great and some of the reason (up the causal chain) for some of the work I've done! Seems really fun! <3 :)))) Facebook's Segment Anything Model I think has a lot…

tysam_and used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 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 month24 dated mentions

Observed mentions: 2023-04-05 13:26:52 UTC to 2024-06-04 15:22:31 UTC.

0510152023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 17 mentions; 2 submissions, 15 commentsApr 20232023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsNov 20232023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 2024

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
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC17215
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC202
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC000
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC000
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC101
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC101
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC101
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC101
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC000
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC110

What Hacker News says

1 sources
Stephen_Oladele·Sep 29, 2023
Webinar from last week on how to fine-tune VFMs, specifically Meta's Segment Anything Model (SAM). What you'll need to follow along the fine-tuning walkthrough: Images, ground-truth masks, and optionally, prompts from the Stamp Verification (StaVer) Dataset on Kaggle (https://www.kaggle.com/datasets/rtatman/stamp-verification-s...) Download the model weights for SAM the official GitHub repo (https://github.com/facebookresearch/segment-anything) Good understanding of the model architecture Segment Anything paper (https://ai.meta.com/research/publications/segment-anything/) GPU infra the NVIDIA A100 should do for this fine-tuning. Data curation and model evaluation tool Encord Active (https://github.com/encord-team/encord-active) Colab walkthrough for fine-tuning: https://colab.research.google.com/github/encord-team/encord-... I'd love
Read the rest
to get your thoughts and feedback. Thank you.
In How to Fine-Tune Foundation Models to Auto-Label Training Data

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.