LightlyTrain is the first PyTorch framework to pretrain computer vision models on unlabeled data for industrial applications

A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation

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

[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"

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

[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.

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

A cli program of image retrieval using dinov2

[ICCV2025] Official repository of the paper "Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation"

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

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

Official implementation of the paper 'Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery'

Integrating SAM2 with DINOv2/v3 for segmentation

This project is an image retrieval system based on DINOv2 and CLIP models. It uses Chroma vector database to support both text-to-image and image-to-image retrieval.

The inference of DINOv2 ONNX models using the ONNXRuntime library.

This repo contains the official implementation of ICCV 2025 paper "MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning

Code for the paper "Robust representations for image classification via counterfactual contrastive learning" (Medical Image Analysis) and "Counterfactual contrastive learning: robust representations via causal image synthesis" (MICCAI Data Engineering Workshop)

DINOv2 module for use with Autodistill.