分割任何东西 (Segment Anything)

Alexander Kirillov,Eric Mintun,Nikhila Ravi,Hanzi Mao,Chloe Rolland,Laura Gustafson,Tete Xiao,Spencer Whitehead,Alexander C. Berg,Wan-Yen Lo,Piotr Dollár,Ross Girshick

from arxiv, Project web-page: https://segment-anything.com

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.

翻译：我们介绍了Segment Anything（SA）项目：一项新的任务，模型和图像分割数据集。使用我们的高效模型在数据收集循环中，我们构建了迄今为止最大的分割数据集（远远超过），包括超过10亿个掩膜和1100万张受许可的、注重隐私的图像。该模型经过设计和训练，可以进行提示，因此可以将其零样本转移到新的图像分布和任务中。我们评估了它的能力，在众多任务上发现其零样本性能令人印象深刻-通常是优于或甚至优于先前的完全监督结果。我们正在https://segment-anything.com上发布分割任何东西模型（SAM）和相应的数据集（SA-1B），以促进基础计算机视觉模型的研究。