Image Segmentation: Cutting Through the Noise with FastSAM
Image segmentation plays a crucial role in computer vision, allowing us to identify and separate objects in an image. Traditional methods have relied on handcrafted features, but recent advancements in deep learning models have revolutionized the field. However, these models were limited by the objects they were trained on and couldn’t segment new or unknown objects.
Enter the Segment Anything Model (SAM), a groundbreaking vision model capable of segmenting any object within an image based on user interaction prompts. SAM is built on a Transformer architecture and trained on the extensive SA-1B dataset, pushing the boundaries of image segmentation.
SAM’s only drawback is its complexity and high computational demands, making it challenging to apply in practical scenarios. Fortunately, there’s a solution: FastSAM.
FastSAM addresses the need for faster execution of SAM in industrial applications. It breaks down the segment anything task into two stages: all-instance segmentation and prompt-guided selection. In the first stage, a Convolutional Neural Network (CNN)-based detector produces segmentation masks for all instances in the image. Then, in the second stage, FastSAM identifies the region of interest indicated by the user prompt.
By leveraging the efficiency of CNNs, FastSAM achieves real-time segmentation without compromising performance quality. It’s based on YOLOv8-seg, an object detector equipped with an instance segmentation branch inspired by the YOLACT method. Surprisingly, FastSAM achieves comparable performance to SAM while significantly reducing computational demands. In fact, FastSAM outperforms SAM in terms of Average Recall at 1000 proposals while running 50 times faster on a single NVIDIA RTX 3090.
FastSAM opens up new possibilities for practical image segmentation applications, providing a more accessible and efficient alternative to the powerful yet computationally demanding SAM model.
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Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. He wrote his M.Sc. thesis about image denoising using deep convolutional networks. He received his Ph.D. degree in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning.” His research interests include deep learning, computer vision, video encoding, and multimedia networking.