Laion / CLIP-ViT-B-32-laion2B-s34B-b79K
Task: Embedding
Modality: Multimodal
Similarity Metric: Cosine
License: Mit
Dimensions: 512
Max Input Tokens: 77
Price: Free
Overview of CLIP-ViT-B-32-laion2B-s34B-b79K
The CLIP-ViT-B-32-laion2B-s34B-b79K is a multimodal embedding model developed by LAION. This model is trained to understand the relationships between text and images using the English subset of the Laion-5B dataset. It can be used for various computer vision and retrieval tasks, including zero-shot image classification, where it can classify images based on text descriptions without additional training, image-text retrieval for finding similar images or texts, and image segmentation to identify and segment objects within images based on semantic meanings.
How to create embeddings using CLIP-ViT-B-32-laion2B-s34B-b79K
You can use CLIP-ViT-B-32-laion2B-s34B-b79K for embedding through OpenCLIP.
Once the vector embeddings are created, they can be stored in a vector database like Zilliz Cloud (a fully managed vector database powered by Milvus and used for image search or multimodal search.
Here are four key steps:
- Sign up for a Zilliz Cloud account for free.
- Set up a serverless cluster and obtain the Public Endpoint and API Key.
- Create a vector collection and insert your vector embeddings.
- Run a semantic search on the stored embeddings.
Create embeddings via OpenCLIP and insert them in Zilliz Cloud for multimodal search
A step-by-step guide: coming soon.
Further reading
- Overview of CLIP-ViT-B-32-laion2B-s34B-b79K
- How to create embeddings using CLIP-ViT-B-32-laion2B-s34B-b79K
- Further reading
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