The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
- open-vocabulary-detection
- open-vocabulary-segmentation
- data-generation
- automatic-labeling-system
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
- deep-learning
- mit
- self-driving-cars
- deep-reinforcement-learning
A 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 RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Inpaint anything using Segment Anything and inpainting models.
unet for image segmentation
- unet
- keras
- segmentation
Data augmentation for NLP
- nlp
- augmentation
- machine-learning
- artificial-intelligence
Segment Anything in Medical Images
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
- agentic-ai
- langmem
- mcp
- openai-api
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
- agentic-ai
- agentic-rag
- agents
- rag
Segment Anything in High Quality [NeurIPS 2023]
- sam
- segmentation
- segment-anything
- zero-shot-segmentation
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
- gpu-acceleration
- large-language-models
- llm
- llm-inference
Everything you need to know to build your own RAG application
- agentic-rag
- chatbot
- llm
- machine-learning
Convert any text to a graph of knowledge. This can be used for Graph Augmented Generation or Knowledge Graph based QnA
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
- estimation
- perception
- robotics
- sensorfusion
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
- cnn
- rnn
- detection
- classification
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.
- interactive-segmentation
- segment-anything
- segment-anything-model
- video-object-segmentation
Beaker Extensions for Jupyter Notebook
- jupyter
- java
- groovy
- clojure
A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
This is the repo for our new project Highly Accurate Dichotomous Image Segmentation
- background-removal
- deep-learning
- dichotomous-image-segmentation
- computer-vision