dinov2

We have hosted the application dinov2 in order to run this application in our online workstations with Wine or directly.

Quick description about dinov2:

DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student�teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks�from linear probing on classification to retrieval, detection, and segmentation�often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints.

Features:

• Self-supervised training recipe for ViT backbones using student�teacher distillation
• Strong, task-agnostic features that transfer to classification, retrieval, and segmentation
• Ready-to-use pretrained weights at multiple model scales
• Baseline evaluation scripts for linear probes and k-NN classifiers
• Feature extraction utilities for downstream pipelines and nearest-neighbor search
• Reproducible configs and training utilities for large-scale pretraining

Programming Language: Python.
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