--- comments: true description: Explore the revolutionary Segment Anything Model (SAM) for promptable image segmentation with zero-shot performance. Discover key features, datasets, and usage tips. keywords: Segment Anything, SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, instance segmentation, computer vision, AI, machine learning --- # Segment Anything Model (SAM) Welcome to the frontier of image segmentation with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field. ## Introduction to SAM: The Segment Anything Model The Segment Anything Model, or SAM, is a cutting-edge image segmentation model that allows for promptable segmentation, providing unparalleled versatility in image analysis tasks. SAM forms the heart of the Segment Anything initiative, a groundbreaking project that introduces a novel model, task, and dataset for image segmentation. SAM's advanced design allows it to adapt to new image distributions and tasks without prior knowledge, a feature known as zero-shot transfer. Trained on the expansive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which contains more than 1 billion masks spread over 11 million carefully curated images, SAM has displayed impressive zero-shot performance, surpassing previous fully supervised results in many cases. ![Dataset sample image](https://user-images.githubusercontent.com/26833433/238056229-0e8ffbeb-f81a-477e-a490-aff3d82fd8ce.jpg) **SA-1B Example images.** Dataset images overlaid masks from the newly introduced SA-1B dataset. SA-1B contains 11M diverse, high-resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks. These masks were annotated fully automatically by SAM, and as verified by human ratings and numerous experiments, are of high quality and diversity. Images are grouped by number of masks per image for visualization (there are ∼100 masks per image on average). ## Key Features of the Segment Anything Model (SAM) - **Promptable Segmentation Task:** SAM was designed with a promptable segmentation task in mind, allowing it to generate valid segmentation masks from any given prompt, such as spatial or text clues identifying an object. - **Advanced Architecture:** The Segment Anything Model employs a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This unique architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation tasks. - **The SA-1B Dataset:** Introduced by the Segment Anything project, the SA-1B dataset features over 1 billion masks on 11 million images. As the largest segmentation dataset to date, it provides SAM with a diverse and large-scale training data source. - **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for prompt engineering. For an in-depth look at the Segment Anything Model and the SA-1B dataset, please visit the [Segment Anything website](https://segment-anything.com) and check out the research paper [Segment Anything](https://arxiv.org/abs/2304.02643). ## Available Models, Supported Tasks, and Operating Modes This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes. | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | ----------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | | SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | | SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | ## How to Use SAM: Versatility and Power in Image Segmentation The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs. ### SAM prediction example !!! Example "Segment with prompts" Segment image with given prompts. === "Python" ```python from ultralytics import SAM # Load a model model = SAM("sam_b.pt") # Display model information (optional) model.info() # Run inference with bboxes prompt model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709]) # Run inference with points prompt model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1]) ``` !!! Example "Segment everything" Segment the whole image. === "Python" ```python from ultralytics import SAM # Load a model model = SAM("sam_b.pt") # Display model information (optional) model.info() # Run inference model("path/to/image.jpg") ``` === "CLI" ```bash # Run inference with a SAM model yolo predict model=sam_b.pt source=path/to/image.jpg ``` - The logic here is to segment the whole image if you don't pass any prompts(bboxes/points/masks). !!! Example "SAMPredictor example" This way you can set image once and run prompts inference multiple times without running image encoder multiple times. === "Prompt inference" ```python from ultralytics.models.sam import Predictor as SAMPredictor # Create SAMPredictor overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024, model="mobile_sam.pt") predictor = SAMPredictor(overrides=overrides) # Set image predictor.set_image("ultralytics/assets/zidane.jpg") # set with image file predictor.set_image(cv2.imread("ultralytics/assets/zidane.jpg")) # set with np.ndarray results = predictor(bboxes=[439, 437, 524, 709]) results = predictor(points=[900, 370], labels=[1]) # Reset image predictor.reset_image() ``` Segment everything with additional args. === "Segment everything" ```python from ultralytics.models.sam import Predictor as SAMPredictor # Create SAMPredictor overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024, model="mobile_sam.pt") predictor = SAMPredictor(overrides=overrides) # Segment with additional args results = predictor(source="ultralytics/assets/zidane.jpg", crop_n_layers=1, points_stride=64) ``` - More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md). ## SAM comparison vs YOLOv8 Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment.md): | Model | Size | Parameters | Speed (CPU) | | ---------------------------------------------- | -------------------------- | ---------------------- | -------------------------- | | Meta's SAM-b | 358 MB | 94.7 M | 51096 ms/im | | [MobileSAM](mobile-sam.md) | 40.7 MB | 10.1 M | 46122 ms/im | | [FastSAM-s](fast-sam.md) with YOLOv8 backbone | 23.7 MB | 11.8 M | 115 ms/im | | Ultralytics [YOLOv8n-seg](../tasks/segment.md) | **6.7 MB** (53.4x smaller) | **3.4 M** (27.9x less) | **59 ms/im** (866x faster) | This comparison shows the order-of-magnitude differences in the model sizes and speeds between models. Whereas SAM presents unique capabilities for automatic segmenting, it is not a direct competitor to YOLOv8 segment models, which are smaller, faster and more efficient. Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test: !!! Example === "Python" ```python from ultralytics import SAM, YOLO, FastSAM # Profile SAM-b model = SAM("sam_b.pt") model.info() model("ultralytics/assets") # Profile MobileSAM model = SAM("mobile_sam.pt") model.info() model("ultralytics/assets") # Profile FastSAM-s model = FastSAM("FastSAM-s.pt") model.info() model("ultralytics/assets") # Profile YOLOv8n-seg model = YOLO("yolov8n-seg.pt") model.info() model("ultralytics/assets") ``` ## Auto-Annotation: A Quick Path to Segmentation Datasets Auto-annotation is a key feature of SAM, allowing users to generate a [segmentation dataset](../datasets/segment/index.md) using a pre-trained detection model. This feature enables rapid and accurate annotation of a large number of images, bypassing the need for time-consuming manual labeling. ### Generate Your Segmentation Dataset Using a Detection Model To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below: !!! Example === "Python" ```python from ultralytics.data.annotator import auto_annotate auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt") ``` | Argument | Type | Description | Default | | ---------- | ------------------- | ------------------------------------------------------------------------------------------------------- | ------------ | | data | str | Path to a folder containing images to be annotated. | | | det_model | str, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | 'yolov8x.pt' | | sam_model | str, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | 'sam_b.pt' | | device | str, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | | | output_dir | str, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | None | The `auto_annotate` function takes the path to your images, with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results. Auto-annotation with pre-trained models can dramatically cut down the time and effort required for creating high-quality segmentation datasets. This feature is especially beneficial for researchers and developers dealing with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation. ## Citations and Acknowledgements If you find SAM useful in your research or development work, please consider citing our paper: !!! Quote "" === "BibTeX" ```bibtex @misc{kirillov2023segment, title={Segment Anything}, author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick}, year={2023}, eprint={2304.02643}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community. ## FAQ ### What is the Segment Anything Model (SAM)? The Segment Anything Model (SAM) is a cutting-edge image segmentation model designed for promptable segmentation, allowing it to generate segmentation masks based on spatial or text-based prompts. SAM is capable of zero-shot transfer, meaning it can adapt to new image distributions and tasks without prior knowledge. It's trained on the extensive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which comprises over 1 billion masks on 11 million images. For more details, check the [Introduction to SAM](#introduction-to-sam-the-segment-anything-model). ### How does SAM achieve zero-shot performance in image segmentation? SAM achieves zero-shot performance by leveraging its advanced architecture, which includes a robust image encoder, a prompt encoder, and a lightweight mask decoder. This configuration enables SAM to respond effectively to any given prompt and adapt to new tasks without additional training. Its training on the highly diverse SA-1B dataset further enhances its adaptability. Learn more about its architecture in the [Key Features of the Segment Anything Model](#key-features-of-the-segment-anything-model-sam). ### Can I use SAM for tasks other than segmentation? Yes, SAM can be employed for various downstream tasks beyond its primary segmentation role. These tasks include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. Through prompt engineering, SAM can adapt swiftly to new tasks and data distributions, offering flexible applications. For practical use cases and examples, refer to the [How to Use SAM](#how-to-use-sam-versatility-and-power-in-image-segmentation) section. ### How does SAM compare to Ultralytics YOLOv8 models? While SAM excels in automatic, real-time segmentation with promptable capabilities, Ultralytics YOLOv8 models are smaller, faster, and more efficient for object detection and instance segmentation tasks. For instance, the YOLOv8n-seg model is significantly smaller and faster than the SAM-b model, making it ideal for applications requiring high-speed processing with lower computational resources. See a detailed comparison in the [SAM comparison vs YOLOv8](#sam-comparison-vs-yolov8) section. ### How can I auto-annotate a segmentation dataset using SAM? To auto-annotate a segmentation dataset, you can use the `auto_annotate` function provided by the Ultralytics framework. This function allows you to automatically generate high-quality segmentation masks using a pre-trained detection model paired with the SAM segmentation model: ```python from ultralytics.data.annotator import auto_annotate auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt") ``` This approach accelerates the annotation process by bypassing manual labeling, making it especially useful for large datasets. For step-by-step instructions, visit [Generate Your Segmentation Dataset Using a Detection Model](#generate-your-segmentation-dataset-using-a-detection-model).