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mlfoundations
open_clip
Python2021–2024
mlfoundations / open_clip

open_clip

An open source implementation of CLIP.

11Distinct people
13Mentions
0Explicit recommenders
14.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An open source implementation of CLIP.

GitHub

Where it is used

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

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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:00:59 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 year13 dated mentions

Observed mentions: 2021-07-31 19:58:51 UTC to 2024-09-26 06:43:05 UTC.

02462021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 6 mentions; 2 submissions, 4 comments2024

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
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC211
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC404
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC624

What Hacker News says

2 sources
What sort of compute do you get access to? There's a lot of cool stuff you could do depending on whether or not you have decent GPU's and for how much time you're allowed to experiment on them. Experimentation is fairly fundamental in practice. There are a lot of pretraining tasks in vision/multimodal that are cool. Largely techniques introduced or refined by OpenAI re-implemented as pytorch open source codebases with varying degrees of success: - Finetune your own CLIP https://github.com/mlfoundations/open_clip - Train a (much smaller) DALLE https://github.com/lucidrains/DALLE-pytorch - Train your own guided diffusion https://colab.research.google.com/drive/1javQRTkALBWLFWnx1K4... (pretty tough, may only be feasible on domain-specific data) - Train a variational autoencoder (VAE) - "VQGAN" from Heidelberg https://github.com/CompVis/tamin
Read the rest
g-transformers - "Discrete VAE", used as the backbone for OpenAI's DALL-E, reimplimented here (and other places) https://github.com/lucidrains/DALLE-pytorch - "VQVAE2" https://github.com/tgisaturday/dalle-lightning
In Ask HN: Computer Vision Project Ideas?

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