This implementation provides an example procedure of training and validating any prevalent deep neural network architecture, with modular data processing, training, logging and visualization integrated. Requirements Dependencies PyTorch 1.0+ NVIDIA-DALI(in development, not recommended) ...
If the output exceeds a given threshold, its fires (or activates) are passed data to the next Input layer Multi hidden layer Output layer Figure 3: An example of ANN architecture [11, 12]. layer in the network. As a result, its output becomes the input of the next node. )is process...
The main idea of designing a lightweight neural network architecture is to design a more efficient network structure by optimizing the computational method of convolution to effectively reduce the computational effort during convolutional computation. Iandola F N et al. [14] proposed the SqueezeNet net...
Figure 2.MHC-PSPNet network architecture. 2.2.1. MobileNetV2 Backbone Network Large-scale networks and lightweight networks represent two major development trends in deep learning. However, large-scale networks require high-performance GPUs, making them unsuitable for deployment on mobile devices. Theref...
Firstly, we selected MobileNetV2, a lightweight convolutional neural network architecture based on depthwise convolutions. MobileNetV2 is notable for its superior computational efficiency and small model size. Its backward residual structure, linear bottleneck design, and ReLU6 activation function effectively...