Slim MobileNet: An Enhanced Deep Convolutional Neural Network
Safa Bouguezzi, Hassene Faiedh, Chokri Souani
Abstract
The Convolutional Neural Network (CNN) is dominant in computer vision applications such as object detection, traffic sign recognition, image classification, face recognition applications, etc. The MobileNet model is a CNN architecture that was constructed to be implemented on an embedded board. However, there are constraints of such architecture for the hardware deployment, which is the limited memory of micro-controller units. This paper proposes an enhanced version of MobileNet that verify the condition to be implemented on an embedded board while improved the accuracy. The proposed model obtained the name of Slim MobileNet because of its small size of 7.3 MB. Slim MobileNet has fewer number of layers, improved accuracy while depreciating the overall size of the model and lower average time compared to the MobileNet-V1 model. We achieve a significant accuracy by replacing the ReLU activation function with the Tanh Exponential (TanhExp) activation function and by making some modifications in the unit of depthwise separable convolution. The small size of Slim MobileNet is occurred by dropping some layers from the original architecture of baseline MobileNet. The experiment is realized using the CIFAR-10 database.