What types of prompts does SAM support?

SAM supports foreground/background points, bounding boxes, and masks. Text prompts have been explored but not yet released.

What is the structure of the SAM model?

The SAM model includes a ViT-H image encoder, a prompt encoder, and a transformer-based mask decoder.

Can SAM integrate with other systems?

Yes, SAM can take input prompts from other systems such as gaze tracking from AR/VR headsets or bounding box prompts from object detectors.

Is SAM capable of zero-shot generalization?

Yes, SAM can generalize to unfamiliar objects and images without requiring additional training.

What kind of training data was used for SAM?

SAM was trained on the SA-1B dataset, which includes over 1.1 billion segmentation masks from approximately 11 million images.

How long does it take for SAM to perform inference?

The image encoder takes about 0.15 seconds on an NVIDIA A100 GPU, while the prompt encoder and mask decoder take around 50ms on a CPU.

Does SAM work on videos?

Currently, SAM only works on images and not on videos.

How is SAM's model designed for efficiency?

SAM is decoupled into a one-time image encoder and a lightweight mask decoder that can run in web browsers within milliseconds per prompt.

What platforms support SAM?

The image encoder is implemented in PyTorch for GPU use, while the prompt encoder and mask decoder can be executed with PyTorch or ONNX runtime on both CPU and GPU.

What is the size of the SAM model?

The image encoder has 632 million parameters, and the prompt encoder and mask decoder have 4 million parameters.

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Segment Anything By Meta

Name: Segment Anything By Meta
Brand: Segment Anything By Meta
Rating: 1 (1 reviews)
Author: Segment Anything By Meta

Last updated: August 8, 2024

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The Segment Anything Model (SAM) by Meta AI is a versatile AI tool designed to segment any object in an image with a single click. Leveraging a 'promptable' system, it supports various input methods like interactive points and bounding boxes without needing additional training. With zero-shot generalization capabilities, SAM can handle unfamiliar objects and images efficiently. It also features a lightweight mask decoder compatible with web browsers, making it highly flexible for integration with other systems and use cases such as video tracking, image editing, and 3D modeling. Trained on the extensive SA-1B dataset consisting of over 1.1 billion masks from 11 million images, SAM exemplifies an advanced AI model for segmentation tasks.

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Segment Anything By Meta's Top Features

Zero-shot generalization to unfamiliar objects and images

Supports various input prompts: interactive points, bounding boxes, masks

Efficient one-time image encoding

Lightweight mask decoder compatible with web browsers

Extensive training on SA-1B dataset (1.1 billion masks from 11 million images)

Integration capability with AR/VR and object detection systems

High-speed inference times

No need for additional training

Versatility for multiple use cases

Advanced transformer-based model architecture

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