This article will explain how we developed Eidetik, what architectural decisions we made, and what technical issues we faced and how it represents a new way to organize and manage personal and organizational knowledge in the digital age.
The article’s objective is to examine the relationship between image modifications and classification accuracy. The chosen classifier for this investigation is MobileNetV2, a lightweight and efficient convolutional neural network (CNN) architecture specifically crafted for mobile and edge devices.
This article focuses on building a driver state classification system with computer vision. The system will utilize an Inception v3 model to detect and classify ten distinct distraction categories.
This article address the traffic challenges and provide a proposed solution involves implementing a sophisticated traffic management system that leverages computer vision’s object detection capabilities.
