Programmer Think

 --neozng1@hnu.edu.cn 1. Backbone As a network focusing on edge platform deployment, especially for CPU devices, NanoDet naturally chose to use a lightweight backbone network with deep separable convolution. Here we mainly introduce the default Backbone: GhostNet , this is a lightweight backbone network proposed by Huawei. For details about ...

Pytorch: Overview of target detection networks, indicator calculation and use of pre-training networks Copyright: Jingmin Wei, Pattern Recognition and Intelligent System, School of Artificial and Intelligence, Huazhong University of Science and Technology Pytorch Tutorial Column Link This tutorial is not commercial and is only for lear ...

The data used in this example is three categories of English data. torchtext is used to process the data, build an iterator and build textcnn. The data is trained with textcnn to get the training results. In this example, the validation set is not used to evaluate the model. 1, Development environment and data set 1. Development envir ...

DenseNet, a densely connected convolution network with Pytorch Note32 A summary of all notes: Pytorch Note Happy Planet DenseNet Previous Resnet enhanced the information flow between the front and back layers through Shortcuts, alleviated the disappearance of gradients to some extent, and built the neural network very deeply. Furthermore ...

Convolutional neural network series preface In this article, we use a custom implemented LeNet-5 neural network architecture to perform image classification on MNIST data sets. 1, Integration LeNet was founded in 1998 by Yann Lecun, Leon bottom, yoshua bengio, and Patrick Haffner My thesis Gradient-Based Learning Applied To ...