Agent skill · research science · davila7
segment-anything-model
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
Why this skill is useful
Provides specific commands and usage patterns for implementing zero-shot image segmentation with the Segment Anything Model that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, opencv-python, pycocotools, segment-anything, torch, transformers installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Segment Anything Model (SAM) Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation. When to use SAM Use SAM when: Need to segment any object in images without task-specific training Building interactive annotation tools with point/box prompts Generating training data for other vision models Need zero-shot transfer to new image domains Building object detection/segmentation pipelines Processing medical, satellite, or domain-specific images Key features: Zero-shot segmentation: Works on any image domain without fine-tuning Flexible prompts: Points, bounding boxes, or previous masks Automatic segmentation: Generate all object masks automatically High quality: Trained on 1.1 billion masks from 11 million images Multiple model sizes: ViT-B (fastest), ViT-L, ViT-H (most accurate) ONNX export: Deploy in browsers and edge devices Use alternatives instead: YOLO/Detectron2: For real-time object detection with classes Mask2Former: For semantic/panoptic segmentation with categories GroundingDINO + SAM: For text-prompted segmentation SAM 2: For video segmentation tasks Quick start Installation Download checkpoints Basic usage with SamPredictor HuggingFace Transformers Core concepts Model architecture Model variants Model Checkpoint Size Speed Accuracy ------- ------------ ------ ------- ---------- ViT-H vith 2.4 GB Slowest Best ViT-L vitl 1.2 GB Medium Good ViT-B vitb 375 MB Fastest Good Prompt types Prompt Description Use Case -------- ------------- ---------- Point (foreground) Click on object Single object selection Point (background) Click outside object Exclude regions Bounding box Rectangle around object Larger objects Previous mask Low-res mask input Iterative refinement Interactive segmentation Point prompts Box prompts Combined prompts Iterative refinement Automatic mask generation Basic automatic segmentation Customized generation Filtering masks Batched inference Multiple images Multiple prompts per image ONNX deployment Export …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/multimodal-segment-anything