From 236615cdadbac8a3b2c4cb62ba37cdc9d7addc75 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:29:43 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index 4c10f3d..85e74d9 100644 --- a/README.md +++ b/README.md @@ -97,3 +97,15 @@ An image-text embedding operator takes a [towhee image](link/to/towhee/image/api + + +# More Resources + +- [CLIP Object Detection: Merging AI Vision with Language Understanding - Zilliz blog](https://zilliz.com/learn/CLIP-object-detection-merge-AI-vision-with-language-understanding): CLIP Object Detection combines CLIP's text-image understanding with object detection tasks, allowing CLIP to locate and identify objects in images using texts. +- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database. +- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding. +- [Hybrid Search: Combining Text and Image for Enhanced Search Capabilities - Zilliz blog](https://zilliz.com/learn/hybrid-search-combining-text-and-image): Milvus enables hybrid sparse and dense vector search and multi-vector search capabilities, simplifying the vectorization and search process. +- [The guide to all-MiniLM-L12-v2 | Hugging Face](https://zilliz.com/ai-models/all-MiniLM-L12-v2): all-MiniLM-L12-v2: a text embedding model ideal for semantic search and RAG and fine-tuned based on Microsoft/MiniLM-L12-H384-uncased +- [Build a Multimodal Search System with Milvus - Zilliz blog](https://zilliz.com/blog/how-vector-dbs-are-revolutionizing-unstructured-data-search-ai-applications): Implementing a Multimodal Similarity Search System Using Milvus, Radient, ImageBind, and Meta-Chameleon-7b +- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other. +- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models. \ No newline at end of file