吞下17亿图片，Meta最强巨兽DINOv3开源，重新定义CV天花板

Core Insights - Meta has developed DINOv3, a self-supervised learning model trained on 1.7 billion images with 7 billion parameters, which has been successfully utilized by NASA for Mars exploration [1][3][26] - DINOv3 sets a new benchmark in computer vision performance, surpassing specialized solutions in various dense prediction tasks [1][10][19] - The model is fully open-sourced, including the pre-trained backbone, adapters, and training and evaluation code, making it suitable for commercial use [6][26] Performance Metrics - DINOv3 achieved significant improvements in various benchmarks compared to its predecessors, such as: - Segmentation on ADE-20k: 55.9 (up from 49.5 with DINOv2) [2] - Depth estimation on NYU I: 0.309 (improved from 0.372 with DINOv2) [2] - Video tracking on DAVIS: 83.3 (up from 76.6 with DINOv2) [2] - Instance retrieval on Met: 55.4 (increased from 44.6 with DINOv2) [2] - Image classification on ImageNet ReaL: 90.4 (up from 86.1 with DINOv2) [2] Applications and Impact - DINOv3's self-supervised learning approach allows it to function effectively in scenarios where labeled data is scarce, such as satellite imagery and medical imaging [10][12][15] - The model has been applied in real-world scenarios, such as monitoring deforestation and supporting ecological restoration efforts by the World Resources Institute [16] - DINOv3 has demonstrated a reduction in measurement error for tree canopy height estimation in Kenya, from 4.1 meters to 1.2 meters [17] Model Flexibility and Deployment - DINOv3's architecture allows for high efficiency and versatility, enabling it to perform multiple visual tasks without the need for fine-tuning [22][24] - Meta has created a family of models ranging from lightweight to high-performance versions to cater to various computational needs, ensuring practical deployment across different applications [26]