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Clip-retrieval Alternatives
Similar projects and alternatives to clip-retrieval
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SaaSHub
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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
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CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
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FLiPStackWeekly
FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
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img2dataset
Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
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OpenPDF
OpenPDF is an open-source Java library for creating, editing, rendering, and encrypting PDF documents, as well as generating PDFs from HTML. It is licensed under the LGPL and MPL.
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laion-aesthetic-datasette
Use Datasette to explore LAION improved_aesthetics_6plus training data used by Stable DIffusion
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better clip-retrieval alternative or higher similarity.
clip-retrieval discussion
clip-retrieval reviews and mentions
Posts with mentions or reviews of clip-retrieval.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-11.
- 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
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A note from our sponsor - SaaSHub
www.saashub.com | 15 Aug 2026
Stats
Basic clip-retrieval repo stats
11
2,791
2.9
5 months ago
rom1504/clip-retrieval is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of clip-retrieval is Jupyter Notebook.
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