Multi-Modal-Comparators
clip-retrieval
| Multi-Modal-Comparators | clip-retrieval | |
|---|---|---|
| 2 | 11 | |
| 39 | 2,139 | |
| - | - | |
| 0.0 | 7.7 | |
| over 1 year ago | 19 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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Multi-Modal-Comparators
clip-retrieval
- FLaNK AI for 11 March 2024
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[D] data for handwriting recognition
The tool clip-retreival lets you filter those 400 million images to whatever subsets you're interested in --- for example, 10,000 images of (mostly) handwriting.
- Stable Attribution
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Same.energy: Image Search by Similarity
Hehe, well you know, PR welcome, the front end is 500 lines https://github.com/rom1504/clip-retrieval/blob/main/front/sr...
Other people have done a few alternate front ends already
This one is meant to be functional, but could sure be made prettier
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Is there a way to use clip or blip to search a massive collection of images for specific things within the picture?
This might work: https://github.com/rom1504/clip-retrieval .
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Ai art
HaveIBeenTrained uses clip retrieval to search the Laion-5B and Laion-400M image datasets. These are currently the largest public text-to-image datsets, and they are used to train models like Stable Diffusion, Imagen, among many others.
- Image Similarity Score using transfer learning
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Exploring 12M of the 2.3B Images Used to Train Stable Diffusion
Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...
Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees
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Semantic and Similarity Image Search Engine
Based on OpenAI's CLIP and the clip-retrieval library (https://github.com/rom1504/clip-retrieval), I've built an end-to-end demo for a semantic and similarity image search engine. It's incredibly powerful for finding similar images amongst large image datasets, or just submitting text/natural language queries and finding the most relevant images in your dataset. Really useful tool for introspection into large datasets before annotation or ML work begins. This could potentially be used to filter or downsize your datasets by several orders of magnitude and make annotation and ML work easier and less costly.
Checkout the demo here:
http://ec2-52-39-251-116.us-west-2.compute.amazonaws.com/
And you can checkout our website or email me for updates and email list, etc.:
https://machineperception.co
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What every software engineer should know about search
Assuming you have an NVIDIA GPU, you can build a semantic search engine by indexing CLIP embeds (image or text).
https://github.com/rom1504/clip-retrieval
What are some alternatives?
clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
MoTIS - [NAACL 2022]Mobile Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP)
laion-aesthetic-datasette - Use Datasette to explore LAION improved_aesthetics_6plus training data used by Stable DIffusion
open_clip - An open source implementation of CLIP.
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
Queryable - Run OpenAI's CLIP model on iOS to search photos.
ArtLine - A Deep Learning based project for creating line art portraits.
stable-diffusion
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
img2dataset - Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.