Computer vision has rapidly evolved over the last decade, but one persistent limitation has been the ability to accurately detect and segment objects from free-form natural language prompts. Traditional segmentation models require predefined object categories, restricted vocabularies, or manual annotations. Meta’s Segment Anything Model (SAM) series changed this landscape by introducing promptable segmentation. Now, with the release of SAM 3, the field has entered a new era.
SAM 3 represents a unified foundation model capable of detecting, segmenting, and tracking objects across images and videos using open vocabulary prompts, including text phrases, bounding boxes, points, and mask exemplars. With its new architecture, large-scale dataset, and refined detector–tracker system, SAM 3 approaches human-level performance in many tasks. This blog explores SAM 3’s architecture, features, performance metrics, installation steps, and real-world applications, providing a comprehensive understanding for developers, researchers, and AI enthusiasts.
What Is SAM 3?
SAM 3, developed by Meta Superintelligence Labs, is a major upgrade to previous versions of the Segment Anything Model. It introduces a scalable, open-vocabulary segmentation framework capable of handling hundreds of thousands of unique concepts. While SAM 1 and SAM 2 were powerful for interactive segmentation, SAM 3 expands these capabilities into text-driven segmentation, enabling users to precisely identify objects using simple language prompts such as:
- “A person wearing white”
- “The red bicycle”
- “A small black dog”
Unlike earlier models, SAM 3 can segment all instances of a given concept, not just the most prominent one. This allows for more comprehensive and accurate object detection in complex scenes.
Key Innovations in SAM 3
1. Open-Vocabulary Concept Segmentation
SAM 3 can process over 270,000 unique concepts in the new SA-CO dataset, which is 50 times more than previous benchmarks. Whether the prompt is a noun phrase or a descriptive sentence, the model adapts seamlessly. This is made possible by:
- A new presence token that helps differentiate between similar concepts.
- Large-scale training on more than 4 million automatically annotated concepts, forming one of the largest segmentation datasets ever built.
2. Unified Image and Video Architecture
SAM 3 integrates both image segmentation and video object tracking into a single framework. Its architecture includes:
- A DETR-based detector conditioned on text, geometry, and exemplar images.
- A SAM 2-style transformer encoder-decoder tracker optimized for real-time video tasks.
By sharing a vision encoder across the detector and tracker, SAM 3 achieves efficient multi-task performance without sacrificing accuracy.
3. Improved Distinction Between Similar Prompts
Thanks to the presence token and refined training pipeline, SAM 3 performs significantly better in discriminating closely related prompts such as:
- “A player in white” vs. “A player in red”
- “A cat sitting” vs. “A cat standing”
This solves a major problem found in prior segmentation models where overlapping features caused inaccurate outputs.
Performance Highlights
Image Segmentation
SAM 3 delivers top-tier performance across several benchmarks:
- 37.2 cgF1 on LVIS
- 48.5 cgF1 on SA-Co/Gold
- 54.1 AP on LVIS instance segmentation
Compared to previous models like Gemini 2.5, DINO-X, and OWLv2, SAM 3 consistently outperforms them in both segmentation and detection tasks.
Video Tracking and Segmentation
On video datasets such as SA-V and YT-Temporal-1B, SAM 3 achieves:
- 30.3 cgF1 on SA-V test
- 50.8 cgF1 on YT-Temporal-1B
- 36.4 cgF1 on SmartGlasses test
While human-level performance remains higher, SAM 3 narrows the gap significantly and offers robust tracking in diverse scenarios.
Installation Guide
To use SAM 3, you need:
- Python 3.12 or above
- PyTorch 2.7+
- CUDA 12.6+
Basic installation steps:
conda create -n sam3 python=3.12 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 .
You also need access to the official checkpoints via the Hugging Face repository, which requires authentication.
Using SAM 3 for Image Segmentation
Once installed, using SAM 3 is straightforward:
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
from PIL import Image
model = build_sam3_image_model()
processor = Sam3Processor(model)
image = Image.open("your_image.jpg")
state = processor.set_image(image)
output = processor.set_text_prompt(state=state, prompt="a small dog")
masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
This simple workflow allows developers to convert natural language prompts into actionable segmentation outputs.
Applications of SAM 3
1. Autonomous Vehicles
Accurate object detection from long text prompts improves safety and environmental understanding.
2. Medical Imaging
SAM 3’s precision segmentation can assist in detecting tumors, organ boundaries, and anomalies.
3. Robotics
Robots can interact with the environment using language-driven visual recognition.
4. Video Analytics
From sports analysis to security monitoring, SAM 3 enhances event tracking and instance recognition.
5. AI-Assisted Creative Tools
Artists and designers can isolate or modify objects in photos and videos using simple text commands.
Conclusion
SAM 3 marks a significant advancement in vision-language models, combining powerful open-vocabulary segmentation with state-of-the-art detection and tracking capabilities. Its ability to interpret natural language prompts and exhaustively segment all matching instances makes it a transformative tool for researchers, developers, and businesses. With the support of one of the largest concept-level segmentation datasets ever created, SAM 3 pushes the boundaries of what is possible in AI-driven image and video understanding. As the model continues to evolve, it is poised to become a foundation for next-generation multimodal AI systems.