[DEIMv2] Real Time Object Detection Meets DINOv3
- Updated
Mar 24, 2026 - Jupyter Notebook
[DEIMv2] Real Time Object Detection Meets DINOv3
All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.
Testing adaptation of the DINOv2/3 encoders for vision tasks with Low-Rank Adaptation (LoRA)
SimpleAICV:pytorch training examples.
A repository to apply DINOv3 models for different downstream tasks: image classification, semantic segmentation, object detection.
ROS 2 integration of Meta’s DINOv3 backbone with lightweight heads for vision tasks.
Integrating SAM2 with DINOv2/v3 for segmentation
Command-line tool for extracting DINOv3, CLIP, SigLIP2, RADIO, features for images and videos
Pytorch implementation of "EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation"
Switch the backbone of mask2former to DINOv3 for instance segmentation
[CVPR'26] Official Code for “V²-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence”
[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
[DEIMv2] Real Time Object Detection Meets DINOv3 C++ and ONNX version
DeepStream integration of Meta’s DINOv3 backbone with lightweight heads for vision tasks.
unofficial JAX implementation of DINOv3, translated in full from the original Meta PyTroch reference implementation (Meta please don't sue me)
Lightweight head for depth estimation using DINOv3 as backbone
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