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

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

[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"

[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

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

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

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'

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.

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)

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

DINOv2 module for use with Autodistill.

[ICML 2025] Official implementation of SPEC method for interpretable embedding comparison. paper: Towards an Explainable Comparison and Alignment of Feature Embeddings

Learn to use deep learning models such as transformer, mamba, stable diffusion, clip, blip, dinov2, expert systems, etc