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 YOLO11, RT-DETR, SAM …

The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"

This is an official repo for fine-tuning SAM to customized medical images.

The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…

MedSAM3: Delving into Segment Anything with Medical Concepts

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

Grounded SAM 2: Ground and Track Anything in Videos with Grounding DINO, Florence-2 and SAM 2

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

Grounded Language-Image Pre-training

This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".

code for "Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging"

Large-scale semi-supervised framework with 1B+ labeled masks from 48K+ datasets with test-time adaptation to new domains (ICCV25).

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

[npj Digital Medicine] A generalizable 3D framework and model for self-supervised learning in medical imaging

Pathology Foundation Model - Nature Medicine

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

[MICCAI 2023] This repository includes the official implementation our paper "SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation"

Implementations of recent research prototypes/demonstrations using MONAI.

[ECCV2022&TPAMI] Official pytorch implementation of UniMiSS & UniMiSS+

Promptable Generalist Model Drives Active Barely Supervised Training in Specialist Model for 3D Medical Image Segmentation

MedLSAM: Localize and Segment Anything Model for 3D Medical Images

Self distilled masked transformer based segmentor used in MICCAI 2022 paper by Jiang J, ... Veeraraghavan H et.al

iBOT 🤖: Image BERT Pre-Training with Online Tokenizer (ICLR 2022)

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

A novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specially for the task of 3D semantic segmentation.

The official code for "Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data".