Weiyao Wang spent eight years at Meta — his first job out of college — helping build multimodal perception systems and contributing to open-world segmentation projects, including SAM3D. His final day ...

Learn how Intersection over Union (IoU) works and how to implement it step-by-step using PyTorch. This guide covers everything from the basic concept to practical coding examples for object detection ...

Abstract: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity ...

Abstract: U-Nets have been established as a standard neural network architecture for image-to-image problems such as segmentation and inverse problems in imaging. For high-dimensional applications, as ...

Recap: It has been a busy month for the Raspberry Pi Foundation. Shortly after announcing branded SD cards and a silicon bumper case for the Pi 5, the foundation introduced a series of branded SSDs ...

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish ...

Volume Segmantics provides a simple command-line interface and API that allows researchers to quickly train a variety of 2D PyTorch segmentation models (e.g. U-Net, U-Net++, FPN, DeepLabV3+) on their ...

Cells were stained by tartrate-resistant acid phosphatase (TRAP) staining (Sigma-Aldrich, St. Louis, MO, United States), and images were captured using the BZ-X810 inverted microscope (Keyence, Osaka, ...