Segment Anything Model (SAM) FAQs

Question 1

Can I combine SAM with text prompts?

Accepted Answer

The core SAM model uses points and boxes. To segment via text, you typically pair this skill with a detection model like GroundingDINO to generate the initial bounding box from text.

Question 2

What makes the Segment Anything Model (SAM) unique?

Accepted Answer

SAM is a foundation model trained on over 1.1 billion masks, allowing it to segment objects in images it has never seen before (zero-shot) based on simple user prompts like clicks or boxes.

Question 3

Can I use this for real-time video segmentation?

Accepted Answer

While SAM is optimized for images, it can be used for video frame-by-frame. For native video support with temporal consistency, SAM 2 is the recommended successor.

Question 4

Which SAM model size should I choose?

Accepted Answer

ViT-B (375MB) is best for speed and edge deployment; ViT-L (1.2GB) offers a balance of speed and accuracy; ViT-H (2.4GB) provides the highest precision but requires more VRAM.

Question 5

Does SAM require a GPU?

Accepted Answer

While SAM can run on a CPU, it is highly recommended to use a CUDA-enabled GPU for acceptable inference speeds, especially when using the larger ViT-H model.

Segment Anything Model (SAM)

Key Features

Use Cases

Segment Anything Model (SAM)

Key Features

Use Cases