Segment Anything in Python (Code Example included)

Full article link: 2304.02643v1.pdf (arxiv.org)

Citation:@article{kirillov2023segany, title={Segment Anything}, author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, journal={arXiv:2304.02643}, year={2023}}

Google Colab Link: https://colab.research.google.com/drive/16xDU3iHoyW4OsFtqjTmcm_S8N2GqRgKX?usp=sharing

The Segment Anything (SA) project introduces a groundbreaking task, model, and dataset for image segmentation. Leveraging an efficient model in a data collection loop, we have created the largest segmentation dataset to date — comprising over 1 billion masks on 11 million licensed and privacy-respecting images. The Segment Anything Model (SAM) is designed to be promptable, showcasing impressive zero-shot performance on various tasks, often rivaling or surpassing prior fully supervised results.

Large language models pre-trained on web-scale datasets have revolutionized natural language processing (NLP) with strong zero-shot and few-shot generalization. This concept extends into computer vision with foundation models, albeit to a lesser extent. This work focuses on building a foundation model for…