--- comments: true description: Discover FastSAM, a real-time CNN-based solution for segmenting any object in an image. Efficient, competitive, and ideal for various vision tasks. keywords: FastSAM, Fast Segment Anything Model, Ultralytics, real-time segmentation, CNN, YOLOv8-seg, object segmentation, image processing, computer vision --- # Fast Segment Anything Model (FastSAM) 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.



Watch: Object Tracking using FastSAM with Ultralytics

## Model Architecture ![Fast Segment Anything Model (FastSAM) architecture overview](https://user-images.githubusercontent.com/26833433/248551984-d98f0f6d-7535-45d0-b380-2e1440b52ad7.jpg) ## Overview 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. ## Key Features 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. ## 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 | |------------|---------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------| | FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | | FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | ## Usage Examples The FastSAM models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development. ### Predict Usage To perform object detection on an image, use the `predict` method as shown below: !!! Example === "Python" ```python from ultralytics import FastSAM from ultralytics.models.fastsam import FastSAMPrompt # Define an inference source source = "path/to/bus.jpg" # Create a FastSAM model model = FastSAM("FastSAM-s.pt") # or FastSAM-x.pt # Run inference on an image everything_results = model(source, device="cpu", retina_masks=True, imgsz=1024, conf=0.4, iou=0.9) # Prepare a Prompt Process object prompt_process = FastSAMPrompt(source, everything_results, device="cpu") # Everything prompt ann = prompt_process.everything_prompt() # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2] ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300]) # Text prompt ann = prompt_process.text_prompt(text="a photo of a dog") # Point prompt # points default [[0,0]] [[x1,y1],[x2,y2]] # point_label default [0] [1,0] 0:background, 1:foreground ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1]) prompt_process.plot(annotations=ann, output="./") ``` === "CLI" ```bash # Load a FastSAM model and segment everything with it yolo segment predict model=FastSAM-s.pt source=path/to/bus.jpg imgsz=640 ``` This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image. ### Val Usage Validation of the model on a dataset can be done as follows: !!! Example === "Python" ```python from ultralytics import FastSAM # Create a FastSAM model model = FastSAM("FastSAM-s.pt") # or FastSAM-x.pt # Validate the model results = model.val(data="coco8-seg.yaml") ``` === "CLI" ```bash # Load a FastSAM model and validate it on the COCO8 example dataset at image size 640 yolo segment val model=FastSAM-s.pt data=coco8.yaml imgsz=640 ``` 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. ### Track Usage To perform object tracking on an image, use the `track` method as shown below: !!! Example === "Python" ```python from ultralytics import FastSAM # Create a FastSAM model model = FastSAM("FastSAM-s.pt") # or FastSAM-x.pt # Track with a FastSAM model on a video results = model.track(source="path/to/video.mp4", imgsz=640) ``` === "CLI" ```bash yolo segment track model=FastSAM-s.pt source="path/to/video/file.mp4" imgsz=640 ``` ## FastSAM official Usage 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: ### Installation 1. Clone the FastSAM repository: ```shell git clone https://github.com/CASIA-IVA-Lab/FastSAM.git ``` 2. Create and activate a Conda environment with Python 3.9: ```shell conda create -n FastSAM python=3.9 conda activate FastSAM ``` 3. Navigate to the cloned repository and install the required packages: ```shell cd FastSAM pip install -r requirements.txt ``` 4. Install the CLIP model: ```shell pip install git+https://github.com/ultralytics/CLIP.git ``` ### Example Usage 1. Download a [model checkpoint](https://drive.google.com/file/d/1m1sjY4ihXBU1fZXdQ-Xdj-mDltW-2Rqv/view?usp=sharing). 2. Use FastSAM for inference. Example commands: - Segment everything in an image: ```shell python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg ``` - Segment specific objects using text prompt: ```shell python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog" ``` - Segment objects within a bounding box (provide box coordinates in xywh format): ```shell python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]" ``` - Segment objects near specific points: ```shell python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]" ``` 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: !!! Quote "" === "BibTeX" ```bibtex @misc{zhao2023fast, title={Fast Segment Anything}, author={Xu Zhao and Wenchao Ding and Yongqi An and Yinglong Du and Tao Yu and Min Li and Ming Tang and Jinqiao Wang}, year={2023}, eprint={2306.12156}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` 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.