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FastSAM is a fast and efficient model for segmenting objects in images. It is designed to segment any type of object quickly and accurately, making it suitable for a variety of applications such as autonomous driving, object recognition, and image editing. The model achieves its speed by using a lightweight architecture and parallel processing techniques. It is trained on a large dataset of annotated images to learn the features that distinguish different objects. FastSAM can efficiently process high-resolution images and can generate segmentations in real-time. It has been shown to achieve state-of-the-art performance on several benchmark datasets.

Use cases

FastSAM has a wide range of potential use cases. In the field of autonomous driving, FastSAM could be used to accurately identify and segment objects such as other vehicles, pedestrians, and traffic signs, allowing self-driving cars to make informed decisions based on a detailed understanding of their surroundings. FastSAM's fast and accurate object segmentation capabilities could also be utilized in object recognition applications, enabling systems to quickly identify and classify objects in real-time. In the field of image editing, FastSAM could be used to efficiently segment objects of interest, allowing users to easily apply targeted edits or remove unwanted elements from photos. Additionally, FastSAM's ability to process high-resolution images in real-time makes it suitable for a range of practical applications, from medical imaging to video analysis. Overall, FastSAM's speed, accuracy, and versatility make it a valuable tool for a variety of industries and applications.



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Avg run time
Nvidia T4 GPU

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Summary of this model and related resources.

Model NameFastsam
Fast Segment Anything
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


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How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.00385
Prediction HardwareNvidia T4 GPU
Average Completion Time7 seconds