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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Easily compute clip embeddings and build a clip retrieval system with them
| Date | Stars |
|---|---|
| 2026-07-24 | 2788 |
| 2026-07-25 | 2788 |
| 2026-07-28 | 2788 |
| 2026-07-30 | 2788 |
| 2026-08-06 | 2788 |
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# clip-retrieval [](https://pypi.python.org/pypi/clip-retrieval) [](http://badge.fury.io/js/clip-retrieval-front) [](https://colab.research.google.com/github/rom1504/clip-retrieval/blob/master/notebook/clip-retrieval-getting-started.ipynb) [](https://gitpod.io/#https://github.com/rom1504/clip-retrieval) [](https://discord.gg/eq3cAMZtCC) Easily compute clip embeddings and build a clip retrieval system with them. 100M text+image embeddings can be processed in 20h using a 3080. * clip client allows remote querying of backend via python. [clip-client notebook](https://colab.research.google.com/github/rom1504/clip-retrieval/blob/master/notebook/clip-client-query-api.ipynb) * clip inference allows you to quickly (1500 sample/s on a 3080) compute image and text embeddings * clip index builds efficient indices out of the embeddings * clip filter allows you to filter out the data using the clip index * clip back hosts the indices with a simple flask service * clip front is a simple ui querying the back. Check it out at [clip-retrieval ui](https://rom1504.github.io/clip-retrieval/) * clip end2end runs img2dataset, inference, index then back and front to make all of this easier to begin with End to end this make it possible to build a simple semantic search system. Interested to learn about semantic search in general ? You can read my [medium post](https://rom1504.medium.com/semantic-search-with-embeddings-index-anything-8fb18556443c) on the topic. Also see [laion5B](https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/) and [semantic search at billions scale](https://rom1504.medium.com/semantic-search-at-billions-scale-95f21695689a) to read more on how to make this scale to billion of samples. [<img src="https://github.com/rom1504/clip-retrieval/raw/main/doc_assets/clip-front-pic.png" alt="clip front" width="500">](https://rom1504.github.io/clip-retrieval/) If you believe in making reusable tools to make data easy to use for ML and you would like to contribute, please join the [DataToML](https://discord.gg/ep8yUUtCnp) chat. ## Related projects * [all_clip](https://github.com/rom1504/all_clip) to load any clip model * [img2dataset](https://github.com/rom1504/img2dataset) to download images from urls * [open_clip](https://github.com/mlfoundations/open_clip) to train clip models * [CLIP_benchmark](https://github.com/LAION-AI/CLIP_benchmark) to evaluate clip models ## Who is using clip retrieval ? * [cah-prepro](https://github.com/rom1504/cah-prepro) preprocess the 400M image+text crawling at home dataset. clip-retrieval is used to compute 400M clip embeddings and the indices * [autofaiss](https://github.com/criteo/autofaiss) uses clip-retrieval to display an example of use (see the multimodal notebook example there) * [afiaka87 openai demo](https://gist.github.com/afiaka87/f662486fc45199fa4394f3456c8246d7#file-dalle_blog_semantic_search-ipynb) shows how to look among the 1M example released by openai for their DALL-E demo * [antarctic-captions by dzryk](https://github.com/dzryk/antarctic-captions) uses autofaiss and clip inference as a way to generate anchors for the image to text task with great success ## Install pip install clip-retrieval If your interest it to run the laion5B index, see [this doc](docs/laion5B_back.md) ## Clip client `ClipClient` allows remote querying of a clip-retrieval backend via python. See [`ClipClient` - Getting Started Notebook](/notebook/clip-client-query-api.ipynb) for a jupyter notebook example. ### API Initialization During initialization you can specify a few parameters: * `backend_u
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:40314083f44cfa62, topic:multimodal, topic:clip, readme:multimodal
matched fp:40314083f44cfa62, topic:semantic-search
matched fp:40314083f44cfa62, topic:deep-learning