Question 1

What is Segment Anything?

Accepted Answer

**Segment Anything Model (SAM)** by Meta AI is a groundbreaking AI tool that lets you "cut out" any object in any image with just a single click. It’s designed to work with a variety of input prompts, making it incredibly versatile for image segmentation tasks without needing additional training.

Question 2

What are the features of Segment Anything?

Accepted Answer

* **Promptable Segmentation**: Use points, boxes, or even text to specify what to segment in an image.
* **Zero-Shot Generalization**: Works on unfamiliar objects and images without extra training.
* **Efficient Design**: Runs in a web browser in just a few milliseconds per prompt.
* **Extensible Outputs**: Masks can be used for video tracking, 3D modeling, and creative tasks like collaging.

Question 3

What are the use cases of Segment Anything?

Accepted Answer

* **Image Editing**: Easily remove or isolate objects in photos.
* **AR/VR Integration**: Future applications could include object selection based on user gaze in AR/VR headsets.
* **Creative Projects**: Use segmented objects for collages or other artistic endeavors.

Question 4

How to use Segment Anything?

Accepted Answer

1. **Select an Image**: Upload or choose an image you want to work with.
2. **Add Prompts**: Use points, boxes, or text to specify the object you want to segment.
3. **Get Results**: SAM will generate the mask for the selected object instantly.

Question 5

Segment Anything Contact Email:

Accepted Answer

segment-anything@meta.com

Question 6

What type of prompts are supported?

Accepted Answer

Foreground/background points, bounding boxes, and masks. Text prompts are explored but not yet released.

Question 7

What is the structure of the model?

Accepted Answer

It includes a ViT-H image encoder, a prompt encoder, and a lightweight transformer-based mask decoder.

Question 8

What platforms does the model use?

Accepted Answer

The image encoder requires a GPU, while the prompt encoder and mask decoder can run on CPU or GPU across platforms supporting ONNX runtime.

Question 9

How big is the model?

Accepted Answer

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

Question 10

How long does inference take?

Accepted Answer

The image encoder takes ~0.15 seconds on an NVIDIA A100 GPU, and the prompt encoder and mask decoder take ~50ms on CPU in the browser.

Question 11

What data was the model trained on?

Accepted Answer

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

Question 12

Does the model work on videos?

Accepted Answer

Currently, it only supports images or individual frames from videos.

Segment Anything