Fast SAM: The Easy Way to Segment Anything

Fast SAM is a strong tool that employs attention processing and deep learning to segment images rapidly and reliably. This is accomplished by focusing on the most significant aspects of an image while disregarding irrelevant information. This makes it an effective and user-friendly picture segmentation solution.

Fast SAM has received a lot of attention due to its ease of use and high performance. It has the potential to completely transform the field of picture segmentation. In this article, we will look into Fast SAM’s capabilities and benefits. Then show how it can easily segment a variety of images.

Speed of FastSAM

FastSAM, which was trained using only 2% of the SA-1B dataset published by the respected SAM authors, combines efficiency and precision like never before.

FastSAM has transformed the field of picture segmentation by outperforming the renowned SAM approach while operating at an incredible 50 times faster run-time speed. FastSAM swiftly solves the most complex segmentation problems in a quarter of the time, eliminating the need to wait indefinitely for results.

How to Install FastSAM?

Step 1: Clone the repository first.

Begin by copying the FastSAM repository to your own system with the command:

git clone

Step 2: Configure the Conda Environment

FastSAM requires Python 3.7 or higher, as well as PyTorch (1.7 or higher) and TorchVision (0.8 or higher). Conda environments can be created and activated using the following commands:

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conda create -n FastSAM python=3.9 conda activate FastSAM

Step 3: Install Dependencies

Navigate to the FastSAM directory and use pip to install the necessary packages:

cd FastSAM pip install -r requirements.txt

Step 4: Install CLIP

FastSAM makes use of the CLIP library as well. Install it with the following command:

pip install git+

Getting Started with FastSAM

Follow these steps to get started with FastSAM:

Step 1: Get the Model Checkpoint.Download a model checkpoint for FastSAM.

Step 2: Run the Inference ScriptsYou can use FastSAM in various modes by running the inference scripts provided. Here are some examples:

Everything mode

python -model_path ./weights/ -img_path ./images/dogs.jpg

Text prompt

python -model_path ./weights/ -img_path ./images/dogs.jpg -text_prompt “the yellow dog”

Box prompt (xywh)

python -model_path ./weights/ -img_path ./images/dogs.jpg -box_prompt “[570,200,230,400]”

Points prompt

python -model_path ./weights/ -img_path ./images/dogs.jpg -point_prompt “[[520,360],[620,300]]” -point_label “[1,0]”

Step 3: Create Masks and View the ResultsUse the code below to produce masks, select masks depending on prompts, and visualize the results:

from fastsam import FastSAM, FastSAMPrompt model = FastSAM(‘./weights/’) IMAGE_PATH = ‘./images/dogs.jpg’ DEVICE = ‘cpu’ everything_results = model(IMAGE_PATH, device=DEVICE, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9,) prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE) # 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=[[620, 360]], pointlabel=[1]) prompt_process.plot(annotations=ann,output=’./output/’,)

Results for Fastest Segmentation Model

Fast SAM is the fastest segmentation model available on a single NVIDIA GeForce RTX 3090.

Inference Times with Fast SAM

FastSAM can segment one point prompt image in 40 milliseconds, a 10 point prompt image in 40 milliseconds, and a 100 point prompt image in 40 milliseconds.

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Optimize Memory Usage with FastSAM

FastSAM excels not only in speed but also in memory efficiency, allowing you to fully utilise your GPU resources. Here’s a comparison of GPU memory utilisation (in megabytes) between FastSAM and two additional approaches, SAM-H and SAM-B, when applied to the COCO 2017 dataset:

Zero-shot Transfer Experiments

Edge Detection Performance: Fast SAM, a cutting-edge segmentation model, performs zero-shot transfer tests on the BSDB500 dataset for edge detection tasks. Let’s compare FastSAM’s performance to that of two other approaches, HED and SAM, using several assessment metrics:

Visualizes FastSAM’s Power

Fast SAM, the cutting-edge segmentation model, achieves outstanding results across a wide range of pictures Using huggingface. Take a look at the following visualisations to see how effective Fast SAM is in segmenting various objects:

To try FastSAM to segment anything on hugging face.

Upload and Segment: Experience the power of Fast Segment Anything by uploading an image and letting it do its magic. In this sample, you can try the “Everything mode” for comprehensive segmentation.

Quick Segmentation: The segmentation process usually takes roughly 6 seconds to complete. Please be patient if the queue has a large concurrency_count, as this may cause minor delays.

Fast Sam
Fast SAM: The Easy Way to Segment Anything 1

Faster Results: If you want faster results, choose a lower input size and uncheck the high_visual_quality option. This optimisation has the potential to dramatically accelerate the process.

Convenient Colab Option: For added convenience, you can acquire segmentation results for any image by using the given Colab link. Open it in Colab for quick access to the results.

Benefits of FastSAM

Fast inference speedHigh accuracyFlexible input

Also read: You might also find useful our guide on SAM and HQ-SAM: A New Generation of Image Segmentation Models

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Fast SAM is a game-changing solution that simplifies the process of segmenting anything. Fast SAM simplifies the complicated work of segmentation with its advanced capabilities and user-friendly interface, allowing users to produce accurate and high-quality results with ease. Fast SAM is a dependable and efficient solution for any segmentation job, with lightning-fast inference speeds, efficient memory utilisation, and excellent performance across multiple datasets. Discover the power of Fast SAM and achieve a new level of convenience and efficiency when segmenting anything you want.

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