SAM ViT-Base
| Property | Value |
|---|---|
| Parameter Count | 93.7M |
| License | Apache 2.0 |
| Author | |
| Paper | arXiv:2304.02643 |
What is sam-vit-base?
SAM ViT-Base is Facebook's implementation of the Segment Anything Model (SAM) using a Vision Transformer base architecture. This model represents a breakthrough in image segmentation, trained on an impressive dataset of 11 million images and 1.1 billion masks. It excels at generating high-quality object masks from various input prompts such as points or boxes.
Implementation Details
The model architecture consists of four main components: a VIT-based Vision Encoder for computing image embeddings, a Prompt Encoder for generating point and box embeddings, a Mask Decoder utilizing a two-way transformer architecture, and a Neck module for final mask prediction. The model uses F32 tensor types and implements relative positional embedding for enhanced spatial understanding.
- Zero-shot capability for various segmentation tasks
- Supports both prompted and automatic mask generation
- Efficient processing with customizable points per batch
- Robust performance across diverse image distributions
Core Capabilities
- High-quality object mask generation from point or box prompts
- Automatic mask generation using a grid of 1024 points
- Zero-shot transfer to new image distributions
- Efficient processing of large-scale images
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to perform zero-shot segmentation tasks with competitive or superior performance compared to supervised alternatives. It's trained on the largest segmentation dataset to date and can generate masks for any object in an image without specific training.
Q: What are the recommended use cases?
The model is ideal for applications requiring automatic object segmentation, interactive image editing, and computer vision tasks requiring precise object masking. It's particularly useful in scenarios where manual annotation would be time-consuming or impractical.
