How do I install the Sam Segmentation skill?

Run npx skillmds@latest add dvcrn/sam-segmentation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Sam Segmentation safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: CAUTION, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Sam Segmentation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Sam Segmentation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Sam Segmentation?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Sam Segmentation by dvcrn — Agent Skill

Name: Sam Segmentation
Rating: 4.666666666666667 (3 reviews)
Author: dvcrn

Sam Segmentation

Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds, cut out objects, extract foreground subjects, or perform image segmentation.

dvcrn Updated Sep 8, 2026 32 repo stars

File contents

SAM Background Removal

Extract foreground subjects from images using Meta's Segment Anything Model, outputting transparent PNGs.

Quick Start

python3 scripts/segment.py <input_image> <output.png>

Defaults to the image center as the foreground hint — works well for portraits and product shots where the subject is centered.

Parameters

Param	Description	Default
`input`	Input image path	required
`output`	Output PNG path (single mode) or directory (`--all` mode)	required
`--model`	Model size: `vit_b` (fast) · `vit_l` (medium) · `vit_h` (best quality)	`vit_h`
`--checkpoint`	Local checkpoint path; auto-downloaded if omitted	auto
`--points`	Foreground hint points as `x,y`, multiple allowed	center
`--all`	Grid-sweep mode: extract all distinct elements	off
`--grid`	Grid density for `--all`; 16 means 16×16=256 probe points	`16`
`--iou-thresh`	Minimum predicted IoU to accept a mask (`--all`)	`0.88`
`--min-area`	Minimum mask area as fraction of image (`--all`)	`0.001`

Examples

# Basic background removal (auto-downloads vit_h ~2.5GB)
python3 scripts/segment.py photo.jpg output.png

# Specify hint point when subject is off-center
python3 scripts/segment.py photo.jpg output.png --points 320,240

# Multiple hints with lightweight model
python3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300

# Extract all elements (one PNG per element)
python3 scripts/segment.py photo.jpg ./elements/ --all

# Denser grid to capture small objects
python3 scripts/segment.py photo.jpg ./elements/ --all --grid 32

# Use a local checkpoint
python3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth

Dependencies

segment_anything is auto-installed on first run, or install manually:

pip install git+https://github.com/facebookresearch/segment-anything.git
pip install pillow numpy torch torchvision

Workflow

User provides image path
Ask if hint points are needed (when subject is off-center)
Run script; checkpoint auto-downloads on first use to ~/.cache/sam/
Output transparent-background PNG

Model Selection

Model	Size	Speed	Quality
`vit_b`	~375 MB	fastest	good
`vit_l`	~1.25 GB	medium	better
`vit_h`	~2.5 GB	slower	best

CUDA is used automatically when a GPU is available.

---name: sam-segmentationdescription: Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds, cut out objects, extract foreground subjects, or perform image segmentation.---# SAM Background RemovalExtract foreground subjects from images using Meta's Segment Anything Model, outputting transparent PNGs.## Quick Start```bashpython3 scripts/segment.py <input_image> <output.png>```Defaults to the image center as the foreground hint — works well for portraits and product shots where the subject is centered.## Parameters| Param | Description | Default ||---|---|---|| `input` | Input image path | required || `output` | Output PNG path (single mode) or directory (`--all` mode) | required || `--model` | Model size: `vit_b` (fast) · `vit_l` (medium) · `vit_h` (best quality) | `vit_h` || `--checkpoint` | Local checkpoint path; auto-downloaded if omitted | auto || `--points` | Foreground hint points as `x,y`, multiple allowed | center || `--all` | Grid-sweep mode: extract all distinct elements | off || `--grid` | Grid density for `--all`; 16 means 16×16=256 probe points | `16` || `--iou-thresh` | Minimum predicted IoU to accept a mask (`--all`) | `0.88` || `--min-area` | Minimum mask area as fraction of image (`--all`) | `0.001` |## Examples```bash# Basic background removal (auto-downloads vit_h ~2.5GB)python3 scripts/segment.py photo.jpg output.png# Specify hint point when subject is off-centerpython3 scripts/segment.py photo.jpg output.png --points 320,240# Multiple hints with lightweight modelpython3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300# Extract all elements (one PNG per element)python3 scripts/segment.py photo.jpg ./elements/ --all# Denser grid to capture small objectspython3 scripts/segment.py photo.jpg ./elements/ --all --grid 32# Use a local checkpointpython3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth```## Dependencies`segment_anything` is auto-installed on first run, or install manually:```bashpip install git+https://github.com/facebookresearch/segment-anything.gitpip install pillow numpy torch torchvision```## Workflow1. User provides image path2. Ask if hint points are needed (when subject is off-center)3. Run script; checkpoint auto-downloads on first use to `~/.cache/sam/`4. Output transparent-background PNG## Model Selection| Model | Size | Speed | Quality ||---|---|---|---|| `vit_b` | ~375 MB | fastest | good || `vit_l` | ~1.25 GB | medium | better || `vit_h` | ~2.5 GB | slower | best |CUDA is used automatically when a GPU is available.

Frequently asked questions