Search Results for author:
Found 6 papers, 2 papers with code
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems
Due to the advent of modern embedded systems and mobile devices with constrained resources, there is a great demand for incredibly efficient deep neural networks for machine learning purposes.
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox
Intelligent edge devices with built-in processors vary widely in terms of capability and physical form to perform advanced Computer Vision (CV) tasks such as image classification and object detection, for example.
Image Classification with CondenseNeXt for ARM-Based Computing Platforms
In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles.
R-MnasNet: Reduced MnasNet for Computer Vision
It is trained on CIFAR-10 [4] and has a validation accuracy of 91. 13%.
Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060
Reduced Mobilenet V2 (RMNv2) is one of those models which is specifically designed for deploying easily in embedded and mobile devices.
