clip-retrieval
CLIP
clip-retrieval | CLIP | |
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11 | 104 | |
2,139 | 22,316 | |
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7.7 | 1.2 | |
22 days ago | 4 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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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
CLIP
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Anomaly Detection with FiftyOne and Anomalib
pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git
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How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
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.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
What are some alternatives?
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
open_clip - An open source implementation of CLIP.
MoTIS - [NAACL 2022]Mobile Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP)
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
laion-aesthetic-datasette - Use Datasette to explore LAION improved_aesthetics_6plus training data used by Stable DIffusion
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
disco-diffusion
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
Queryable - Run OpenAI's CLIP model on iOS to search photos.
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation