SAM 3 GitHub

The official SAM 3 GitHub gives you everything you need to get started with Meta AI’s most advanced segmentation model complete with open vocabulary support, text/image prompts, multi instance tracking, and full video workflows. Whether you're a developer, researcher, or creator, this repo isn’t just code it’s your launchpad into the future of vision AI.

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SAM 3 GitHub

SAM 3 GitHub: Your Ultimate Guide to Meta’s Open‑Source Promptable Segmentation

Meta AI’s Segment Anything Model 3 (SAM 3) represents one of the most exciting leaps in computer vision a unified model for open‑vocabulary segmentation, detection, and tracking using both text and visual prompts. Unlike earlier versions in the Segment Anything family, SAM 3 lets you find, segment, and track all instances of a concept across images and videos with flexible input prompts.

The official GitHub repository for SAM 3 is where developers, researchers, and AI practitioners can access official code, models, examples, evaluation scripts, and documentation. This article dives deep into everything you need to know about the SAM 3 GitHub from what’s inside the repo, how to use it, typical workflows, best practices, and how it fits into broader ecosystems.

1. What Is the SAM 3 GitHub Repository?

The SAM 3 GitHub repository (hosted under facebookresearch/sam3) is the central resource for the codebase behind SAM 3’s capabilities, including:

  • Model definitions and implementations

  • Inference code for image and video segmentation

  • Examples and notebooks demonstrating how to use the model

  • Evaluation scripts for benchmarking

  • Utilities for data, loading checkpoints, and more

It serves as both a reference implementation and a starting point for integration into custom applications, research experiments, or production systems.

2. Key Features of the SAM 3 GitHub Repo

The repository supports several groundbreaking capabilities:

2.1 Promptable Concept Segmentation (PCS)

SAM 3’s defining innovation Promptable Concept Segmentation lets the model segment all objects matching a user specified concept based on:

  • Text prompts (e.g., “red car”, “soccer ball”)

  • Image exemplars (visual snippets of an object)

  • Hybrid prompts (text + image)

This is different from earlier segmentation systems (like SAM 2) that required geometric prompts, such as clicks or boxes, for specific object instances.

2.2 Unified Detection, Segmentation, and Tracking

The SAM 3 codebase includes modules to handle:

  • Open vocabulary detection

  • Pixel‑accurate segmentation

  • Temporal tracking across video frames

The official examples show both image inference and video segmentation + tracking workflows out of the box.

2.3 Support for Multiple Prompt Types

In addition to text and exemplars, SAM 3 supports:

  • Visual prompts (points, boxes, masks  as before)

  • Combined prompts (allowing more precise control)

This makes SAM 3 a general‑purpose vision tool for segmentation tasks.

2.4 Evaluation and Benchmarks

The GitHub repository includes:

  • Scripts to evaluate SAM 3 on the SA‑Co benchmark (Segment Anything with Concepts)

  • Tools for performance evaluation against other segmentation models

This supports research and helps quantify advances against standardized tasks.

3. Behind the Scenes: How SAM 3 Works

Before diving into GitHub specifics, it helps to understand the model briefly.

At a high level, SAM 3 combines:

  • Multimodal prompt encoders (text + image)

  • Vision backbone shared by detection and tracking

  • Cross‑modal alignment layers to link prompts to visual features

  • Segmentation and identity tracking outputs for instances across images and videos

The GitHub repository mirrors this modular architecture, making it easier to customize or extend.

4. Navigating the SAM 3 GitHub Repository

Here’s a breakdown of the main folders and files you’ll find in the official repo:

4.1 sam3/ - Core Model Code

This directory typically contains:

  • Model definitions

  • Prompt encoders

  • Backbone and segmentation heads

  • Integration points for text, visual, and hybrid prompts

This is where the main architecture lives.

4.2 examples/ - Ready‑Made Examples

The examples folder is one of the most useful parts for beginners because it includes:

  • sam3_video_predictor_example.ipynb
    A notebook showing how to perform video segmentation and tracking with minimal code.

  • sam3_agent.ipynb
    Demonstrates how to use SAM 3 as an agent in combination with large language models (LLMs)  e.g., to handle complex text queries like “segment the leftmost child.” 

These examples are critical for hands‑on learning.

4.3 scripts/ - Utilities and Benchmarks

This contains:

  • Tools for evaluating on the SA‑Co benchmark

  • Utilities for parsing annotations and running metrics

  • Evaluation data setups for SA‑Co/Gold and other subsets of the benchmark

This is essential if you plan to measure performance or run comparisons.

4.4 Documentation and README Files

The repository comes with a detailed README.md and often sub‑folders with instructions on how to:

  • Install dependencies

  • Clone and set up the project

  • Download model checkpoints

  • Run inference on images and video

  • Fine‑tune on custom datasets

Following these docs is crucial for successful setup.

5. Setting Up SAM 3 from GitHub

To use the repository locally, the typical workflow looks like this:

5.1 Clone the Repository

 
git clone https://github.com/facebookresearch/sam3.git cd sam3

This grabs the entire codebase to your local machine.

5.2 Install Dependencies

The SAM 3 repo usually relies on:

  • Python (3.12+ recommended)

  • PyTorch (latest supported version, e.g., 2.7+)

  • Vision libraries (OpenCV, Pillow, etc.)

  • Optional: CUDA for GPU acceleration

Installation steps are documented in the README.

5.3 Download Model Checkpoints

SAM 3’s checkpoints (weights for the model) are typically provided via:

  • Meta’s official GitHub instructions

  • Hugging Face model hub (facebook/sam3)

Because of ongoing access regulations, users may sometimes need to request access or manage tokens to download large checkpoints.

5.4 Run Example Scripts

Once everything is set up, you can try example scripts, such as:

 
python examples/image_inference.py \ --image_path path/to/image.jpg \ --prompt_text "red car"

Or video workflows from the provided notebooks.

6. Using SAM 3 for Real‑World Segmentation Tasks

The GitHub repo isn’t just academic - it supports a wide range of practical scenarios.

6.1 Image Segmentation with Text Prompts

With SAM 3, you can segment objects purely by specifying a text prompt like:

  • “yellow school buses”

  • “boxes on a warehouse floor”

  • “striped cats”

The model returns segmentation masks for all matching instances in a single pass a powerful open‑vocabulary capability.

6.2 Video Segmentation and Tracking

Using the video predictor example notebooks, you can:

  • Initialize the model on a key frame

  • Provide a text or visual prompt

  • Automatically track objects across frames with consistent IDs

This is especially useful for:

  • VFX and creative editing

  • Video analytics and surveillance

  • Time‑based tracking of moving entities

6.3 Combining Prompts and Interactive Refinement

SAM 3 seamlessly supports:

  • Text prompts (e.g., “cars in the left lane”)

  • Visual prompts (points, boxes, existing masks)

  • Hybrid prompts (text + exemplar)

This allows users to refine segmentations interactively or programmatically.

7. Advanced GitHub Features and Experimentation

As an open‑source project, SAM 3’s GitHub encourages experimentation:

7.1 Fine‑Tuning and Dataset Integration

The repository provides foundations to:

  • Fine‑tune SAM 3 on custom datasets

  • Integrate your own data pipelines

  • Combine SA‑Co benchmark scripts with new data

This makes SAM 3 suitable for:

  • Research experiments

  • Domain‑specific segmentation (e.g., medical, aerial imagery)

7.2 Custom Architectures and Extensions

Because the codebase is modular, developers can:

  • Swap out prompt encoders

  • Modify the segmentation head

  • Integrate with LLMs for advanced prompt reasoning

The sam3_agent.ipynb shows one such integration example with a large language model.

7.3 Evaluation and Performance Analysis

The scripts/eval/ and benchmark folders let you:

  • Measure performance vs. human annotations

  • Compare models under different prompt regimes

  • Run structured experiments for research publications

8. Community Tools and Third‑Party Integrations

Beyond the core SAM 3 GitHub, the community has rapidly built integrations and enhancements:

8.1 Ultralytics Integration

SAM 3 is integrated into the Ultralytics package, allowing easy pip installation and use with popular pipelines like YOLO.

8.2 Autodistill Support

Repositories like autodistill-sam3 provide auto‑labeling workflows that use SAM 3 for dataset creation and downstream object detection fine‑tuning.

8.3 ComfyUI Nodes and Plugins

Open‑source UI tools like ComfyUI now support nodes for SAM 3, enabling segmentation automation in visual workflows without code.

9. Limitations and Challenges

Even with a powerful GitHub offering, there are important caveats:

9.1 Checkpoint Access and Size

SAM 3’s model weights are large requiring requests for access or authenticated downloads in some cases.

9.2 Hardware Demands

Because SAM 3 is a foundation model, GPU acceleration is strongly recommended, especially for video tasks.

9.3 Prompt Ambiguity

Open‑vocabulary segmentation is powerful but sometimes yields mixed results for ambiguous prompts. Good prompt engineering remains crucial.

9.4 Domain Shift Sensitivity

SAM 3 trained on broad datasets may struggle without fine‑tuning in highly specialized domains.

10. Future Directions on GitHub

The SAM 3 GitHub repository is expected to evolve as the community contributes:

  • New prompt interfaces

  • Performance optimizations

  • Mobile/edge deployments

  • Cross‑modal embeddings with LLMs

  • 3D segmentation and multimodal tasks

Given the rapid pace of open‑source AI development, it’s likely that extensions and companion repos will proliferate.

11. Conclusion: Why the SAM 3 GitHub Matters

The SAM 3 GitHub repository is more than just code it’s the gateway to next‑generation, promptable, open‑vocabulary segmentation. It empowers both researchers and practitioners to:

  • Explore cutting‑edge segmentation research

  • Build real‑world applications

  • Prototype new vision workflows

  • Benchmark and extend foundation models

Whether you’re a developer, data scientist, or AI researcher, mastering the SAM 3 GitHub unlocks one of today’s most versatile and powerful tools in computer vision.