Kalgaonkar, PriyankEl-Sharkawy, Mohamed2023-02-222023-02-222021-04Kalgaonkar, P., & El-Sharkawy, M. (2021). Image Classification with CondenseNeXt for ARM-Based Computing Platforms. 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 1–6. https://doi.org/10.1109/IEMTRONICS52119.2021.9422541https://hdl.handle.net/1805/31401In 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. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of Con-denseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.enPublisher PolicyCondenseNeXtconvolutional neural networkcomputer visionConference proceedings