What is SAM3 and how does this skill help?

SAM3 is a foundation model from Meta for image and video segmentation. This skill provides Claude with the specific API patterns and best practices needed to implement it quickly and correctly.

Does this skill support video object tracking?

Yes, it includes specific guidance for the SAM3 video predictor, including multi-GPU configurations and tracker-backbone integration.

Can I use SAM1-style point prompts with this skill?

Absolutely. It details the 'predict_inst' API and the 'enable_inst_interactivity' configuration required for interactive click-based segmentation.

How does it handle batched inference?

The skill provides patterns for using the DataPoint API, which allows for processing multiple images and complex queries in a single forward pass for maximum efficiency.

SAM3 Image Segmentation API

Name: SAM3 Image Segmentation API
Author: tordks

bytordks

Ciencia de Datos y ML

Provides standardized API patterns and implementation guidance for Meta's Segment Anything Model 3 (SAM3) across image and video tasks.

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The SAM3 Image Segmentation skill enables Claude to provide expert-level guidance for implementing Segment Anything Model 3, a unified foundation model for promptable segmentation. It covers essential patterns for text prompts (open-vocabulary), geometric bounding boxes, and SAM1-style interactive point prompts. Developers can leverage this skill to implement complex computer vision workflows, including multi-GPU video tracking, efficient batched inference using the DataPoint API, and performance optimizations for modern GPU architectures.

Características Principales

Advanced video tracking implementation for consistent object identification across frames
Efficient batched inference patterns using DataPoint and FindQueryLoaded structures
0 GitHub stars
Interactive mask refinement techniques using logit-based feedback loops
Comprehensive patterns for text, box, and point-based promptable segmentation
GPU optimization guidance including bfloat16 and TensorFloat32 configurations

Casos de Uso

Building interactive annotation tools for medical, satellite, or consumer imagery
Implementing zero-shot object detection and segmentation using natural language
Developing high-performance video processing pipelines for automated tracking and masking

Acerca de

Características Principales

Advanced video tracking implementation for consistent object identification across frames
Efficient batched inference patterns using DataPoint and FindQueryLoaded structures
0 GitHub stars
Interactive mask refinement techniques using logit-based feedback loops
Comprehensive patterns for text, box, and point-based promptable segmentation
GPU optimization guidance including bfloat16 and TensorFloat32 configurations

Casos de Uso

Building interactive annotation tools for medical, satellite, or consumer imagery
Implementing zero-shot object detection and segmentation using natural language
Developing high-performance video processing pipelines for automated tracking and masking