SAM 3: Segment Anything with Concepts

SAM 3 (Segment Anything Model 3) represents Meta's next-generation foundation model for Promptable Concept Segmentation (PCS). Building upon SAM 2, SAM 3 introduces a fundamentally new capability: detecting, segmenting, and tracking all instances of a visual concept specified by text prompts, image exemplars, or both. Unlike previous SAM versions that segment single objects per prompt, SAM 3 can find and segment every occurrence of a concept appearing anywhere in images or videos, aligning with open-vocabulary goals in modern instance segmentation.

Overview

SAM 3 achieves a 2× performance gain over existing systems in Promptable Concept Segmentation while maintaining and improving SAM 2's capabilities for interactive visual segmentation. The model excels at open-vocabulary segmentation, allowing users to specify concepts using simple noun phrases (e.g., "yellow school bus", "striped cat") or by providing example images of the target object. These capabilities complement production-ready pipelines that rely on streamlined predict and track workflows.

What is Promptable Concept Segmentation (PCS)?

The PCS task takes a concept prompt as input and returns segmentation masks with unique identities for all matching object instances. Concept prompts can be:

• Text: Simple noun phrases like "red apple" or "person wearing a hat", similar to zero-shot learning
• Image exemplars: Bounding boxes around example objects (positive or negative) for fast generalization
• Combined: Both text and image exemplars together for precise control

This differs from traditional visual prompts (points, boxes, masks) which segment only a single specific object instance, as popularized by the original SAM family.

Key Performance Metrics

For context on model metrics and trade-offs in production, see model evaluation insights and YOLO performance metrics.

Architecture

SAM 3 consists of a detector and tracker that share a Perception Encoder (PE) vision backbone. This decoupled design avoids task conflicts while enabling both image-level detection and video-level tracking, with an interface compatible with Ultralytics Python usage and CLI usage.

Core Components

• Detector: DETR-based architecture for image-level concept detection

  • Text encoder for noun phrase prompts
  • Exemplar encoder for image-based prompts
  • Fusion encoder to condition image features on prompts
  • Novel presence head that decouples recognition ("what") from localization ("where")
  • Mask head for generating instance segmentation masks

• Tracker: Memory-based video segmentation inherited from SAM 2

  • Prompt encoder, mask decoder, memory encoder
  • Memory bank for storing object appearance across frames
  • Temporal disambiguation aided by techniques like a Kalman filter in multi-object settings

• Presence Token: A learned global token that predicts whether the target concept is present in the image/frame, improving detection by separating recognition from localization.

Key Innovations

1. Decoupled Recognition and Localization: The presence head predicts concept presence globally, while proposal queries focus only on localization, avoiding conflicting objectives.
2. Unified Concept and Visual Prompts: Supports both PCS (concept prompts) and PVS (visual prompts like SAM 2's clicks/boxes) in a single model.
3. Interactive Exemplar Refinement: Users can add positive or negative image exemplars to iteratively refine results, with the model generalizing to similar objects rather than just correcting individual instances.
4. Temporal Disambiguation: Uses masklet detection scores and periodic re-prompting to handle occlusions, crowded scenes, and tracking failures in video, aligning with instance segmentation and tracking best practices.

SA-Co Dataset

SAM 3 is trained on Segment Anything with Concepts (SA-Co), Meta's largest and most diverse segmentation dataset to date, expanding beyond common benchmarks like COCO and LVIS.

Training Data

Benchmark Data

The SA-Co evaluation benchmark contains 214K unique phrases across 126K images and videos, providing over 50× more concepts than existing benchmarks. It includes:

• SA-Co/Gold: 7 domains, triple-annotated for measuring human performance bounds
• SA-Co/Silver: 10 domains, single human annotation
• SA-Co/Bronze and SA-Co/Bio: 9 existing datasets adapted for concept segmentation
• SA-Co/VEval: Video benchmark with 3 domains (SA-V, YT-Temporal-1B, SmartGlasses)

Data Engine Innovations

SAM 3's scalable human- and model-in-the-loop data engine achieves 2× annotation throughput through:

1. AI Annotators: Llama based models propose diverse noun phrases including hard negatives
2. AI Verifiers: Fine-tuned multimodal LLMs verify mask quality and exhaustivity at near-human performance
3. Active Mining: Focuses human effort on challenging failure cases where AI struggles
4. Ontology-Driven: Leverages a large ontology grounded in Wikidata for concept coverage

Installation

SAM 3 will be supported natively in the Ultralytics package upon release:

pip install ultralytics

Models will download automatically when first used. You can then use standard predict mode and later export models to formats like ONNX and TensorRT for deployment.

How to Use SAM 3: Versatility in Concept Segmentation

Supported Tasks and Models

SAM 3 supports both Promptable Concept Segmentation (PCS) and Promptable Visual Segmentation (PVS) tasks:

Concept Segmentation Examples

Segment with Text Prompts

from ultralytics import SAM

# Load SAM 3 model
model = SAM("sam3.pt")

# Segment all instances of a concept
results = model("path/to/image.jpg", prompt="yellow school bus")

# Works with descriptive phrases
results = model("path/to/image.jpg", prompt="person wearing a red hat")

# Or simple object names
results = model("path/to/image.jpg", prompt="striped cat")

# Segment all matching concepts in an image
yolo segment model=sam3.pt source=path/to/image.jpg prompt="yellow school bus"

Segment with Image Exemplars

from ultralytics import SAM

model = SAM("sam3.pt")

# Provide a positive example box - finds all similar objects
results = model("path/to/image.jpg", bboxes=[100, 150, 300, 400], labels=[1])

# Add negative examples to exclude certain instances
results = model(
    "path/to/image.jpg",
    bboxes=[[100, 150, 300, 400], [500, 200, 600, 350]],  # Two boxes
    labels=[1, 0],  # First is positive, second is negative
)

# Combine text and image exemplars for precision
results = model("path/to/image.jpg", prompt="dog", bboxes=[100, 150, 300, 400], labels=[1])

Interactive Refinement

from ultralytics import SAM

model = SAM("sam3.pt")

# Initial segmentation with text
results = model("path/to/image.jpg", prompt="car")

# If some cars are missed, add a positive exemplar
results = model(
    "path/to/image.jpg",
    prompt="car",
    bboxes=[missed_car_box],
    labels=[1],  # Positive example
)

# If false positives appear, add negative exemplars
results = model(
    "path/to/image.jpg",
    prompt="car",
    bboxes=[false_positive_box],
    labels=[0],  # Negative example
)

Video Concept Segmentation

from ultralytics.models.sam import SAM3VideoPredictor

# Create video predictor
predictor = SAM3VideoPredictor(model="sam3.pt", imgsz=1024, conf=0.25)

# Track all instances of a concept
results = predictor(source="video.mp4", prompt="person wearing blue shirt")

# Combine text with exemplar for precision
results = predictor(
    source="video.mp4",
    prompt="kangaroo",
    bboxes=[initial_box],  # Exemplar from first frame
    labels=[1],
)

For broader streaming and production setups, see object tracking and view results in terminal.

Visual Prompts (SAM 2 Compatibility)

SAM 3 maintains full backward compatibility with SAM 2's visual prompting:

from ultralytics import SAM

model = SAM("sam3.pt")

# Single point prompt (SAM 2 style)
results = model(points=[900, 370], labels=[1])

# Multiple points
results = model(points=[[400, 370], [900, 370]], labels=[1, 1])

# Box prompt
results = model(bboxes=[100, 150, 300, 400])

Performance Benchmarks

Image Segmentation

SAM 3 achieves state-of-the-art results across multiple benchmarks, including real-world datasets like LVIS and COCO for segmentation:

Explore dataset options for quick experimentation in Ultralytics datasets.

Video Segmentation Performance

SAM 3 shows significant improvements over SAM 2 and prior state-of-the-art across video benchmarks such as DAVIS 2017 and YouTube-VOS:

Few-Shot Adaptation

SAM 3 excels at adapting to new domains with minimal examples, relevant for data-centric AI workflows:

Interactive Refinement Effectiveness

SAM 3's concept-based prompting with exemplars converges much faster than visual prompting:

Object Counting Accuracy

SAM 3 provides accurate counting by segmenting all instances, a common requirement in object counting:

SAM 3 vs SAM 2 vs YOLO Comparison

Here we compare SAM 3's capabilities with SAM 2 and YOLO11 models:

Key Takeaways:

• SAM 3: Best for open-vocabulary concept segmentation, finding all instances of a concept with text or exemplar prompts
• SAM 2: Best for interactive single-object segmentation in images and videos with geometric prompts
• YOLO11: Best for real-time, high-speed segmentation in resource-constrained deployments using efficient export pipelines like ONNX and TensorRT

Evaluation Metrics

SAM 3 introduces new metrics designed for the PCS task, complementing familiar measures like F1 score, precision, and recall.

Classification-Gated F1 (CGF1)

The primary metric combining localization and classification:

CGF1 = 100 × pmF1 × IL_MCC

Where:

• pmF1 (Positive Macro F1): Measures localization quality on positive examples
• IL_MCC (Image-Level Matthews Correlation Coefficient): Measures binary classification accuracy ("is the concept present?")

Why These Metrics?

Traditional AP metrics don't account for calibration, making models difficult to use in practice. By evaluating only predictions above 0.5 confidence, SAM 3's metrics enforce good calibration and mimic real-world usage patterns in interactive predict and track loops.

Key Ablations and Insights

Impact of Presence Head

The presence head decouples recognition from localization, providing significant improvements:

The presence head provides a +5.7 CGF1 boost (+9.9%), primarily improving recognition ability (IL_MCC +6.5%).

Effect of Hard Negatives

Hard negatives are crucial for open-vocabulary recognition, improving IL_MCC by 54.5% (0.44 → 0.68).

Training Data Scaling

High-quality human annotations provide large gains over synthetic or external data alone. For background on data quality practices, see data collection and annotation.

Applications

SAM 3's concept segmentation capability enables new use cases:

• Content Moderation: Find all instances of specific content types across media libraries
• E-commerce: Segment all products of a certain type in catalog images, supporting auto-annotation
• Medical Imaging: Identify all occurrences of specific tissue types or abnormalities
• Autonomous Systems: Track all instances of traffic signs, pedestrians, or vehicles by category
• Video Analytics: Count and track all people wearing specific clothing or performing actions
• Dataset Annotation: Rapidly annotate all instances of rare object categories
• Scientific Research: Quantify and analyze all specimens matching specific criteria

SAM 3 Agent: Extended Language Reasoning

SAM 3 can be combined with Multimodal Large Language Models (MLLMs) to handle complex queries requiring reasoning, similar in spirit to open-vocabulary systems like OWLv2 and T-Rex.

Performance on Reasoning Tasks

Example Complex Queries

SAM 3 Agent can handle queries requiring reasoning:

• "People sitting down but not holding a gift box in their hands"
• "The dog closest to the camera that is not wearing a collar"
• "Red objects larger than the person's hand"

The MLLM proposes simple noun phrase queries to SAM 3, analyzes returned masks, and iterates until satisfied.

Limitations

While SAM 3 represents a major advancement, it has certain limitations:

• Phrase Complexity: Best suited for simple noun phrases; long referring expressions or complex reasoning may require MLLM integration
• Ambiguity Handling: Some concepts remain inherently ambiguous (e.g., "small window", "cozy room")
• Computational Requirements: Larger and slower than specialized detection models like YOLO
• Vocabulary Scope: Focused on atomic visual concepts; compositional reasoning is limited without MLLM assistance
• Rare Concepts: Performance may degrade on extremely rare or fine-grained concepts not well-represented in training data

Citation

@inproceedings{sam3_2025,
  title     = {SAM 3: Segment Anything with Concepts},
  author    = {Anonymous authors},
  booktitle = {Submitted to ICLR 2026},
  year      = {2025},
  url       = {https://openreview.net/forum?id=r35clVtGzw},
  note      = {Paper ID: 4183, under double-blind review}
}

FAQ

When Will SAM 3 Be Released?

SAM 3 is currently under review at ICLR 2026 (conference in 2026, review in 2025). Official models, weights, and benchmarks will be released publicly following the review process, likely in 2026. Ultralytics will provide immediate support for SAM 3 integration upon Meta's release and document usage in predict mode and track mode.

Will SAM 3 Be Integrated Into Ultralytics?

Yes. SAM 3 will be supported in the Ultralytics Python package upon release, including concept segmentation, SAM 2–style visual prompts, and multi-object video tracking. You will be able to export to formats like ONNX and TensorRT for deployment, with streamlined Python and CLI workflows.

What Is Promptable Concept Segmentation (PCS)?

PCS is a new task introduced in SAM 3 that segments all instances of a visual concept in an image or video. Unlike traditional segmentation that targets a specific object instance, PCS finds every occurrence of a category. For example:

• Text prompt: "yellow school bus" → segments all yellow school buses in the scene
• Image exemplar: Box around one dog → segments all dogs in the image
• Combined: "striped cat" + exemplar box → segments all striped cats matching the example

See related background on object detection and instance segmentation.

How Does SAM 3 Differ From SAM 2?

SAM 3 maintains backward compatibility with SAM 2 visual prompting while adding concept-based capabilities.

What datasets are used to train SAM 3?

SAM 3 is trained on the Segment Anything with Concepts (SA-Co) dataset:

Training Data:

• 5.2M images with 4M unique noun phrases (SA-Co/HQ) - high-quality human annotations
• 52.5K videos with 24.8K unique noun phrases (SA-Co/VIDEO)
• 1.4B synthetic masks across 38M noun phrases (SA-Co/SYN)
• 15 external datasets enriched with hard negatives (SA-Co/EXT)

Benchmark Data:

• 214K unique concepts across 126K images/videos
• 50× more concepts than existing benchmarks (e.g., LVIS has ~4K concepts)
• Triple annotation on SA-Co/Gold for measuring human performance bounds

This massive scale and diversity enables SAM 3's superior zero-shot generalization across open-vocabulary concepts.

How does SAM 3 compare to YOLO11 for segmentation?

SAM 3 and YOLO11 serve different use cases:

SAM 3 Advantages:

• Open-vocabulary: Segments any concept via text prompts without training
• Zero-shot: Works on new categories immediately
• Interactive: Exemplar-based refinement generalizes to similar objects
• Concept-based: Automatically finds all instances of a category
• Accuracy: 47.0 AP on LVIS zero-shot instance segmentation

YOLO11 Advantages:

• Speed: 10-15× faster inference (2-3ms vs 30ms per image)
• Efficiency: 70× smaller models (5.9MB vs ~400MB expected)
• Resource-friendly: Runs on edge devices and mobile
• Real-time: Optimized for production deployments

Recommendation:

• Use SAM 3 for flexible, open-vocabulary segmentation where you need to find all instances of concepts described by text or examples
• Use YOLO11 for high-speed, production deployments where categories are known in advance
• Use SAM 2 for interactive single-object segmentation with geometric prompts

Can SAM 3 handle complex language queries?

SAM 3 is designed for simple noun phrases (e.g., "red apple", "person wearing hat"). For complex queries requiring reasoning, combine SAM 3 with an MLLM as SAM 3 Agent:

Simple queries (native SAM 3):

• "yellow school bus"
• "striped cat"
• "person wearing red hat"

Complex queries (SAM 3 Agent with MLLM):

• "People sitting down but not holding a gift box"
• "The dog closest to the camera without a collar"
• "Red objects larger than the person's hand"

SAM 3 Agent achieves 76.0 gIoU on ReasonSeg validation (vs 65.0 previous best, +16.9% improvement) by combining SAM 3's segmentation with MLLM reasoning capabilities.

How accurate is SAM 3 compared to human performance?

On the SA-Co/Gold benchmark with triple human annotation:

• Human lower bound: 74.2 CGF1 (most conservative annotator)
• SAM 3 performance: 65.0 CGF1
• Achievement: 88% of estimated human lower bound
• Human upper bound: 81.4 CGF1 (most liberal annotator)

SAM 3 achieves strong performance approaching human-level accuracy on open-vocabulary concept segmentation, with the gap primarily on ambiguous or subjective concepts (e.g., "small window", "cozy room").

📅 Created 0 days ago ✏️ Updated 0 days ago

Comments