Golden Retriever-Image Retrieval Engine Files

Library and frontend engine for performing Image Retrieval tasks.

Status: Alpha

Brought to you by: xultimatex

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	InfoDownloads / Week
GRire	2014-09-01	2
PluginPacks	2013-08-24	0
Totals: 2 Items		2

Other Useful Business Software

MongoDB Atlas runs apps anywhere

Deploy in 115+ regions with the modern database for every enterprise.

MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.

Start Free

Build Data Resilience - Take the Assessment Today

Can you recover when it matters most? Take this quick assessment to identify gaps and build greater recovery confidence.

Is your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.

Take the Assessment

$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.

Start Free

Recommended Projects

BRISC Image Retrieval System
BRISC (BRISC Really IS Cool) is 1) a library for Haralick, Gabor and Markov image feature extraction from pulmonary nodule DICOM images and 2) a simple content-based image retrieval (CBIR) system for pulmonary nodule databases (ie. LIDC).
CNN for Image Retrieval
cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding
FastRAG
Efficient Retrieval Augmentation and Generation Framework
FVS: A Content-Based Retrieval Library
FVS is a cross-platform 2-D image and 3-D object analysis library. It is intented for use within content-based retrieval systems to facilitate retrieval of "similar" images or objects to a given query image or object.