This project is a self-supervised vision foundation model based on a vision transformer architecture. It is designed to learn dense visual representations from unlabeled images, serving as a general-purpose backbone for a wide variety of downstream vision tasks.
The system is distinguished by its use of self-distillation and masked image modeling to extract semantic and geometric features. It also incorporates an image-text alignment model that maps visual embeddings to textual descriptions, enabling zero-shot image recognition, zero-shot segmentation, and cross-modal retrieval.
The project covers a broad range of computer vision capabilities, including dense feature extraction, monocular depth estimation, and semantic image segmentation. It supports object detection and classification via linear-head task adaptation, as well as image similarity retrieval and object tracking across video frames.
The repository includes tools for distributed vision pretraining on GPU clusters and methods for high-resolution or metadata-guided model adaptation.
