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Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
cuVS - a library for vector search and clustering on the GPU
A fast header-only graph-based index for approximate nearest neighbor search (ANNS). https://flatnav.net
Shotit is a screenshot-to-video search engine tailored for TV & Film, blazing-fast and compute-efficient.
Official implementation for paper "Navigating Labels and Vectors: A Unified Approach to Filtered Approximate Nearest Neighbor Search"
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
AI@UCF's custom-made course, organized by semesters
Simple gRPC server for vector searching implemented by Python and Faiss
Naive implementations of some ANNS (Approximate Nearest Neighbor Search) algorithms without any optimization and generalization.
The ultimate brain of Shotit, in charge of task coordination.
Four core workers of shotit: watcher, hasher, loader and searcher.
Provide meta information and utility for shotit, for example, image proxy, cast and poster etc.