Implemented the Segment Anything Model (SAM2) using PyTorch and CUDA, enabling high-performance and scalable image segmentation tasks by leveraging GPU acceleration for faster processing and real-time applications.
Developed an automatic mask generator for precise object segmentation, allowing for seamless identification and extraction of objects within images, significantly improving the accuracy of segmentation in complex visual data.
Created an interactive point-based segmentation system, where users can define points of interest within an image to fine-tune the segmentation process, enabling more precise control and customization in diverse use cases.
Explored practical applications in fields such as object detection, image editing, and computer vision research, demonstrating the versatility and effectiveness of the model for various industries, including healthcare, autonomous vehicles, and multimedia processing.
