Segment Anything Model (SAM)
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About
Segment Anything Model (SAM) is a new AI model from Meta AI that can "cut out" any object, in any image, with a single click. SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training. It uses various input prompts, enabling flexible integration with other systems and extensible outputs. SAM has learned a general notion of what objects are, enabling zero-shot generalization to unfamiliar objects and images without requiring additional training. The model was trained on the SA-1B dataset consisting of 11M images and 1B+ masks.
Platform
Task
Features
• zero-shot generalization
• flexible integration with other systems
• extensible outputs
• multiple valid masks for ambiguous prompts
• automatic segmentation
• interactive point and box prompts
• promptable segmentation system
FAQs
What type of prompts are supported?
Foreground/background points, Bounding box, Mask
What is the structure of the model?
A ViT-H image encoder that runs once per image and outputs an image embedding. A prompt encoder that embeds input prompts such as clicks or boxes. A lightweight transformer based mask decoder that predicts object masks from the image embedding and prompt embeddings
What platforms does the model use?
The image encoder is implemented in PyTorch and requires a GPU for efficient inference. The prompt encoder and mask decoder can run directly with PyTroch or converted to ONNX and run efficiently on CPU or GPU across a variety of platforms that support ONNX runtime.
How big is the model?
The image encoder has 632M parameters. The prompt encoder and mask decoder have 4M parameters.
How long does inference take?
The image encoder takes ~0.15 seconds on an NVIDIA A100 GPU. The prompt encoder and mask decoder take ~50ms on CPU in the browser using multithreaded SIMD execution.
What data was the model trained on?
The model was trained on our SA-1B dataset. See our dataset viewer.
How long does it take to train the model?
The model was trained for 3-5 days on 256 A100 GPUs.
Does the model produce mask labels?
No, the model predicts object masks only and does not generate labels.
Does the model work on videos?
Currently the model only supports images or individual frames from videos.
Where can I find the code?
Code is available on [GitHub](https://github.com/facebookresearch/segment-anything)
Pricing Plans
Free
Free PlanJob Opportunities
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