description: Discover FastSAM, a real-time CNN-based solution for segmenting any object in an image. Efficient, competitive, and ideal for various vision tasks.
The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. This task is designed to segment any object within an image based on various possible user interaction prompts. FastSAM significantly reduces computational demands while maintaining competitive performance, making it a practical choice for a variety of vision tasks.
FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
1.**Real-time Solution:** By leveraging the computational efficiency of CNNs, FastSAM provides a real-time solution for the segment anything task, making it valuable for industrial applications that require quick results.
2.**Efficiency and Performance:** FastSAM offers a significant reduction in computational and resource demands without compromising on performance quality. It achieves comparable performance to SAM but with drastically reduced computational resources, enabling real-time application.
3.**Prompt-guided Segmentation:** FastSAM can segment any object within an image guided by various possible user interaction prompts, providing flexibility and adaptability in different scenarios.
4.**Based on YOLOv8-seg:** FastSAM is based on [YOLOv8-seg](../tasks/segment.md), an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
5.**Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than [SAM](sam.md) on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
6.**Practical Applications:** The proposed approach provides a new, practical solution for a large number of vision tasks at a really high speed, tens or hundreds of times faster than current methods.
7.**Model Compression Feasibility:** FastSAM demonstrates the feasibility of a path that can significantly reduce the computational effort by introducing an artificial prior to the structure, thus opening new possibilities for large model architecture for general vision tasks.
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.
The FastSAM models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development.
This way you can run inference on image and get all the segment `results` once and run prompts inference multiple times without running inference multiple times.
=== "Prompt inference"
```python
from ultralytics.models.fastsam import FastSAMPredictor
All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
Please note that FastSAM only supports detection and segmentation of a single class of object. This means it will recognize and segment all objects as the same class. Therefore, when preparing the dataset, you need to convert all object category IDs to 0.
FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
Additionally, you can try FastSAM through a [Colab demo](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) or on the [HuggingFace web demo](https://huggingface.co/spaces/An-619/FastSAM) for a visual experience.
## Citations and Acknowledgements
We would like to acknowledge the FastSAM authors for their significant contributions in the field of real-time instance segmentation:
The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
FastSAM, short for Fast Segment Anything Model, is a real-time convolutional neural network (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
FastSAM achieves real-time segmentation by decoupling the segmentation task into all-instance segmentation with YOLOv8-seg and prompt-guided selection stages. By utilizing the computational efficiency of CNNs, FastSAM offers significant reductions in computational and resource demands while maintaining competitive performance. This dual-stage approach enables FastSAM to deliver fast and efficient segmentation suitable for applications requiring quick results.
This flexibility allows FastSAM to adapt to a wide range of user interaction scenarios, enhancing its utility across different applications. For more information on using these prompts, refer to the [Key Features](#key-features) section.
