From 52fd4b8488c2ebcf511171a8ad53bd050e2978c2 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:26:26 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d2a62fc..b172633 100644 --- a/README.md +++ b/README.md @@ -87,4 +87,13 @@ The decoded image data in towhee Image (a subset of numpy.ndarray). For mpvit_xsmall model, feature_dim = 256. For mpvit_small model, feature_dim = 288. For mpvit_base model, feature_dim = 480. - \ No newline at end of file + + +# More Resources + +- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models. +- [What are Vision Transformers (ViT)? - Zilliz blog](https://zilliz.com/learn/understanding-vision-transformers-vit): Vision Transformers (ViTs) are neural network models that use transformers to perform computer vision tasks like object detection and image classification. +- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more. +- [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. +- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database +- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings. \ No newline at end of file