@ -198,3 +198,15 @@ Or your can refer to the original [hugging face transformers training examples](
# 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.
- [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
- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
- [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
- [Enhancing Efficiency in Vector Searches with Binary Quantization and Milvus - Zilliz blog](https://zilliz.com/learn/enhancing-efficiency-in-vector-searches-with-binary-quantization-and-milvus): Binary quantization represents a transformative approach to managing and searching vector data within Milvus, offering significant enhancements in both performance and efficiency. By simplifying vector representations into binary codes, this method leverages the speed of bitwise operations, substantially accelerating search operations and reducing computational overhead.
- [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.
