DINOv2 is a self-supervised learning model developed by Meta AI that enables accurate object detection, segmentation, and understanding in images and videos without requiring extensive labeled data. It achieves this through its advanced network architecture and design, which leverages knowledge distillation to compress large models into smaller ones while maintaining accuracy. The model's pretraining dataset consists of 142 million images, curated from a mix of public datasets and crawled web data. DINOv2 has shown promising results in various computer vision applications, including depth estimation, semantic segmentation, instance retrieval, video understanding, and fine-grained classification. Its versatility and ability to generalize across domains make it an attractive tool for industries such as augmented reality, robotics, autonomous vehicles, medical imaging, human-computer interaction, gaming, and entertainment. The model is available on GitHub under the Creative Commons Attribution-NonCommercial 4.0 International Public License, allowing non-commercial use, but its performance may not significantly surpass other labeling methods.