sam3

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2 scripts

Create and work with Meta SAM 3 (facebookresearch/sam3) for open-vocabulary image and video segmentation with text, point, box, and mask prompts. Use when setting up SAM3 environments, requesting Hugging Face checkpoint access, generating inference scripts, integrating SAM3 into Python apps, fine-tuning with sam3/train configs, running SA-Co or custom evaluations, or debugging CUDA/checkpoint/prompt pipeline issues.

1installs

Sourcejakerains/agentskills

Added on2026-02-09

NPX Install

npx skill4agent add jakerains/agentskills sam3

SKILL.md Content

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SAM 3 (facebookresearch/sam3)

Overview

Build, integrate, fine-tune, and evaluate Meta SAM 3 with reproducible commands and minimal setup friction.

Quick Routing

User intent	Action
Install SAM 3 and run first inference	Follow setup in `references/setup-and-inference.md`
Add SAM 3 to an existing Python app	Generate starter code with `scripts/create_inference_starter.py` and adapt API calls
Verify environment before setup/inference	Run `scripts/sam3_preflight_check.py`
Fine-tune on custom data	Use `references/training-and-eval.md` training flow and config guidance
Run SA-Co benchmarks or eval custom predictions	Use eval commands in `references/training-and-eval.md` and upstream `scripts/eval/*` docs
Debug runtime failures	Run the troubleshooting checklist in `references/setup-and-inference.md`

Core Workflow

Confirm objective and modality.
Set up environment and checkpoint access.
Run a smoke test.
Execute the task path: inference, training, or evaluation.
Return reproducible commands and file paths.

1) Confirm objective and modality

Identify whether the user needs image inference, video inference, fine-tuning, or benchmark evaluation.
Confirm whether CUDA is available and which GPU memory budget applies.
Confirm whether Hugging Face access to
```
facebook/sam3
```
is already approved.

2) Set up environment and checkpoint access

Use a clean environment:

bash

conda create -n sam3 python=3.12 -y
conda activate sam3
pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
git clone https://github.com/facebookresearch/sam3.git
cd sam3
pip install -e .

Authenticate with Hugging Face before first model load:

bash

hf auth login

Optionally run a preflight check before model download:

bash

python scripts/sam3_preflight_check.py --strict

For full setup and verification commands, read

references/setup-and-inference.md

.

3) Run a smoke test

From this skill folder, generate a starter script:

bash

python scripts/create_inference_starter.py --mode image --output ./sam3_smoke.py

Edit placeholders and run from a SAM3 checkout.

4) Execute the task path

Image and video inference: Use

references/setup-and-inference.md

to apply

Sam3Processor

and

build_sam3_video_predictor

patterns.

Fine-tuning: Use
```
references/training-and-eval.md
```
and start from a provided config in
```
sam3/train/configs
```
.

Benchmark evaluation: Use

references/training-and-eval.md

plus upstream dataset notes under

scripts/eval/gold

,

scripts/eval/silver

, and

scripts/eval/veval

.

5) Return reproducible output

Report exact commands run and any config overrides.
Include checkpoint source and authentication assumptions.
Include prompt text, frame index, and confidence threshold when reporting inference outputs.

Guardrails

Do not assume checkpoint access is granted; verify login and permission first.
Prefer official
```
sam3.model_builder
```
and predictor APIs over custom re-implementations.
Keep generated scripts editable and avoid machine-specific absolute paths.
If running on CPU, explicitly note expected performance limits before large jobs.

Resources

Setup and inference guide:
```
references/setup-and-inference.md
```
Training and evaluation guide:
```
references/training-and-eval.md
```
Starter generator:
```
scripts/create_inference_starter.py
```
Preflight checker:
```
scripts/sam3_preflight_check.py
```

sam3

NPX Install

Tags

SKILL.md Content

SAM 3 (facebookresearch/sam3)

Overview

Quick Routing

Core Workflow

1) Confirm objective and modality

2) Set up environment and checkpoint access

3) Run a smoke test

4) Execute the task path

5) Return reproducible output

Guardrails

Resources