Machine Learning A Guide to Using U-Nets for Image Segmentation Image segmentation is a powerful technique to locate objects or boundaries in images on a per-pixel level. In this blog we take a quick look at how U-Nets work, explore how they build on standard CNNs, and show how easy it is to build and iterate on U-Nets in PerceptiLabs.
Manufacturing Use Case: Air Conditioner Piston Check With the growing use of computer vision in Industrial IoT (IIoT) and Industry 4.0 to analyze and detect defects, we were inspired to build an image recognition model that can classify images of AC pistons.
Use Case Use Case: Classifying Chest X-Rays to Detect Pneumonia To accurately diagnose ailments, doctors need every possible tool at their disposal. This Use Case shows how to classify chest x-rays to detect Pneumonia.
Machine Learning Five GANs for Better Image Processing Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.
Machine Learning Recreating Nvidia’s PilotNet in PerceptiLabs Computer Vision is a key technology for building algorithms to enable self-driving cars. One of the pioneering projects in this field was an experimental system called PilotNet by Nvidia. It uses a deep neural network (DNN) that takes image frames from cameras mounted on the front of a car and
