Datature Blog

SAM 3 is Meta’s next-generation segmentation model that shifts from geometric, prompt-based segmentation to concept-level understanding through Promptable Concept Segmentation, enabling open-vocabulary instance detection via text or visual exemplars.

This article introduces 3D segmentation, partitioning volumetric data into labeled regions for applications in medical imaging, robotics, and more. Focusing on 3D semantic segmentation, it uses the Swin UNETR architecture for brain tumor segmentation as an example. The article covers core concepts, training on the BraTS dataset including MRI normalization, input/output processing, computational challenges, and adapting Swin UNETR for 3D image classification.

This article examines the latest advancements in PaliGemma 2, a next-generation vision-language model designed for scalability, high-resolution processing, and domain-specific adaptability. We dive into its architecture, benchmarks, and innovations, offering a comprehensive overview for machine learning practitioners and researchers seeking to understand its capabilities and potential applications.