Popular GitHub Repositories for Grounding Techniques

OpenMMLab Detection Toolbox and Benchmark

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

Techniques for deep learning with satellite & aerial imagery

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Pytorch implementation of convolutional neural network visualization techniques

Effortless data labeling with AI support from Segment Anything and other awesome models.

This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.

A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 ? and reasoning techniques.

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks