dinov2

All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.

Refiners (discontinued early 2025) was a microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation.

[ICLR'24 & IJCV‘25] Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Testing adaptation of the DINOv2/3 encoders for vision tasks with Low-Rank Adaptation (LoRA)

[ECCV 2024] Improving 2D Feature Representations by 3D-Aware Fine-Tuning

Unofficial implementation of the paper "The Chosen One: Consistent Characters in Text-to-Image Diffusion Models"

[WACV2025] AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

[ICCV 2025] Official repository of the paper "Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation"

[CVPR'24] NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild

Welcome to the project repository for POPE (Promptable Pose Estimation), a state-of-the-art technique for 6-DoF pose estimation of any object in any scene using a single reference.

Turn your cameras into Home Assistant sensors with a local AI: garage door open/closed, people and cars, water, gas and power meters read by any camera or ESP32-CAM. 100% local, any CPU.

[CVPR'26 Highlight] Official Code for “V²-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence”

Integrating SAM2 with DINOv2/v3 for segmentation

[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

Multi-modal AI-generated content detection: image, video, and audio. Benchmarks, training code (DINOv2, DINOv3, ReStraV, BreathNet), and evaluation pipeline for real vs. synthetic classification with calibration-aware metrics.

校园地标图像检索与导览系统 | Vue · Spring Boot · FastAPI · DINOv2

A cli program of image retrieval using dinov2

[NeurIPS'24] A Simple Image Segmentation Framework via In-Context Examples

[WACV 2026] Official implementation of the paper: “CountingDINO: A Training-free Pipeline for Exemplar-based Class-Agnostic Counting”

go-vision 基于 Golang + ONNX 构建的视觉库，支持 SAM2、YOLOv11-Det、YOLOv11-Seg、YOLOv11-Cls、YOLOv11-Pose、YOLOv11-OBB、YOLO26-Det、YOLO26-Seg、YOLO26-Cls、YOLO26-Pose、YOLO26-OBB、DINOv2-Seg 等模型