Model Card for Segment Anything Model (SAM) ViT Base (ViT B) version Detailed architecture of Segment Anything Model (SAM). Table of Contents 0. TL;DR 1. Model Details 2. Usage 3. Citation TL;DR Link to original repository The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero shot performance on a variety of segmentation tasks. The abstract of the paper states: 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 An…
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