An-619

FastSAM

An-619

46

FastSAM is a CNN Segment Anything Model trained by only 2% of the SA-1B dataset published by the Segment Anything Model (SAM) authors. Despite this much smaller training dataset, FastSAM achieves comparable performance to the full SAM model, while running 50 times faster. FastSAM was developed by An-619 at the CASIA-IVA-Lab. The Segment Anything Model (SAM) is a state-of-the-art model that can generate high quality object masks from various input prompts like points or bounding boxes. It has been trained on a massive dataset of 11 million images and 1.1 billion masks. Another variant, the SAM-ViT-Base model, uses a Vision Transformer (ViT) backbone, while the SAM-ViT-Huge version uses an even larger ViT-H backbone. Model inputs and outputs Inputs Image**: The input image for which segmentation masks will be generated. Text prompt**: An optional text description of the object to be segmented. Box prompt**: An optional bounding box around the object to be segmented. Point prompt**: An optional set of points indicating the object to be segmented. Outputs Segmentation masks**: One or more segmentation masks corresponding to the objects in the input image, based on the provided prompts. Confidence scores**: Confidence scores for each of the output segmentation masks. Capabilities FastSAM can generate high-quality object segmentation masks at a much faster speed than the original SAM model. This makes it particularly useful for real-time applications or when computational resources are limited. The model has shown strong zero-shot performance on a variety of segmentation tasks, similar to the full SAM model. What can I use it for? FastSAM can be used in a wide range of computer vision applications that require object segmentation, such as: Image editing**: Quickly select and mask objects in an image for editing, compositing, or other manipulations. Autonomous systems**: Extract detailed object information from camera inputs for tasks like self-driving cars, robots, or drones. Content creation**: Easily isolate and extract objects from images for use in digital art, 3D modeling, or other creative projects. Things to try Try experimenting with different input prompts - text, bounding boxes, or point clicks - to see how the model's segmentation results vary. You can also compare the speed and performance of FastSAM to the original SAM model on your specific use case. Additionally, explore the different inference options provided by the FastSAM codebase.

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