facebook-sam-vit-huge

facebook-sam-vit-huge

Meta
Version: 6
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 SAM model is made up of 3 modules:
  • The VisionEncoder: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used.
  • The PromptEncoder: generates embeddings for points and bounding boxes
  • The MaskDecoder: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed
  • The Neck: predicts the output masks based on the contextualized masks produced by the MaskDecoder.

Training Data

See here for an overview of the datastet.
apache-2.0

Quick facts

Model providerMeta
TypeImage segmentation
LifecycleGenerally available (GA)
PricingView pricing

Quick Start

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