Open_clip Alternatives

open_clip reviews and mentions

• Xkcd 1425 (Tasks) turns ten years old today
  1 project | news.ycombinator.com | 25 Sep 2024

  Here’s some code: https://github.com/mlfoundations/open_clip?tab=readme-ov-fil...

• Open_clip: An open source implementation of CLIP
  1 project | news.ycombinator.com | 17 Sep 2024
• Beginner's Guide to Fine-Tuning Clip Models
  1 project | news.ycombinator.com | 9 Jul 2024
• Binarize Clip for Multimodal Applications
  1 project | news.ycombinator.com | 23 May 2024

  The part of CLIP[1] that you need to know to understand this is that it embeds text and images into the same space. ie: the word "dog" is close to images of dogs. Normally this space is a high dimensional real space. Think 512-dimensional or 512 floating point numbers. When you want to measure "closeness" between vectors in this space cosine similarity[2] is a natural choice.

  Why would you want to quantize values? Well, instead of using a 32-bit float for each dimension, what if you could get away with 1-bit? You would save you 31x the space. Often you'll want to embed millions or billions of pieces of text or images, so the savings represent a huge speed & cost savings and if accuracy isn't impacted too much then it could be worth it.

  If you naively clip the floats of an existing model, it severely impacts accuracy. However, if you train a model from scratch that produces binary outputs, then it appears to perform better.

  There is one twist. Deep learning models rely on gradient descent to train and binary output doesn't produce useful gradients. We use cosine similarity on floating point vectors and hamming distance on bit vectors. Is there a function that behaves like hamming distance but is nicely differentiable? We can then use this function during training and then vanilla hamming distance during inference. It seems like they've done that.

  I'd suggest playing around with OpenCLIP[3]. My background is in data science but all my CLIP knowledge comes from doing a side project over the course of a couple weekends.

  1. https://huggingface.co/docs/transformers/model_doc/clip

  2. https://en.wikipedia.org/wiki/Cosine_similarity

  3. https://github.com/mlfoundations/open_clip

• FLaNK-AIM: 20 May 2024 Weekly
  28 projects | dev.to | 20 May 2024
• FLaNK AI Weekly for 29 April 2024
  44 projects | dev.to | 29 Apr 2024
• A History of CLIP Model Training Data Advances
  8 projects | dev.to | 13 Mar 2024

  While OpenAI’s CLIP model has garnered a lot of attention, it is far from the only game in town—and far from the best! On the OpenCLIP leaderboard, for instance, the largest and most capable CLIP model from OpenAI ranks just 41st(!) in its average zero-shot accuracy across 38 datasets.

• How to Build a Semantic Search Engine for Emojis
  6 projects | dev.to | 10 Jan 2024

  Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

• Database of 16,000 Artists Used to Train Midjourney AI Goes Viral
  1 project | news.ycombinator.com | 7 Jan 2024

  It is a misconception that Adobe's models have not been trained on copyrighted work. Nobody should be repeating their marketing claims.

  Adobe has not shown how they train the text encoders in Firefly, or what images were used for the text-based conditioning (i.e. "text to image") part of their image generation model. They are almost certainly using CLIP or T5, which are trained on LAION2b, an image dataset with the very problems they are trying to address, C4 (a text dataset similarly encumbered) and similar.

  I welcome anyone who works at Adobe to simply answer this question of how they trained the text encoders for text conditioning and put it to rest. There is absolutely nothing sensitive about the issue, unless it exposes them in a lie.

  So no chance. I think it's a big fat lie. They'd have to have made some other scientific breakthrough, which they didn't.

  Using information from https://openai.com/research/clip and https://github.com/mlfoundations/open_clip, it's possible to investigate the likelihood that using just their stock image dataset, can they make a working text encoder?

  It's certainly not impossible, but it's impracticable. On 248m images (roughly the size of Adobe Stock), CLIP gets 37% on ImageNet, and on the 2000m from LAION, it performs 71-80%. And even with 2000m images, CLIP is substantially worse performing than the approach that Imagen uses for "text comprehension," which relies on essentially many billions more images and text tokens.

• MetaCLIP – Meta AI Research
  6 projects | news.ycombinator.com | 26 Oct 2023

  https://github.com/mlfoundations/open_clip/blob/main/docs/op...

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