Notifications You must be signed in to change notification settings Most of the code and associated materials are referenced from https://github.com/JiachengCao/cnn ...

The dataset is composed of 28x28 images in grayscale. Its popularity comes from being a "sibling" of the MNIST dataset of handwritten digits used in the paper that originally proposed the LeNet CNN.

momentum=0.5) # optimizer = SGD(Lenet.parameters(), learning_rate=1e-2, momentum=0.9) # momentum改为0.9训练更快 ...

Contain some classical deep learning model, like Lenet, AleNet, VGGNet, GoogleNet and so on. Contain how to save and load model which is trained before. Contain how to save model as json file.

Deep learning technologies are contributing to substantial innovations in the field of object detection. While Convolutional Neural Networks (CNN) have laid a solid foundation, new models such as You ...

The Lenet CNN without GAN may loss a little accuracy, but the training of wGAN can lead to a similar performance compared to the original Lenet. If we train wGAN more frequently than the CNN, we will ...

This project is an attempt to implemnt a harware CNN structure. The code is written by Verilog/SystemVerilog ... and feed the computing units. A LeNet is constructed using the 4 elementary modules, ...

LeNet was first introduced in 1998 by Yann LeCun with the intention of being used mainly for optical character recognition, which is why we are going to run the MNIST dataset against it. The code for ...

This model is motivated from the Striving for Simplicity - All Convolution Net paper. The paper achieves 95.6% accuracy using the All-CNN architecture. My model (YGNet) has few changes in the ...

<Build command="/usr/bin/make -j4 -f &quot;/home/shawn/Shawn_CNN_C++/cmake-build-debug/Makefile&quot; VERBOSE=1 all"/> <CompileFile command="/usr/bin/make -j4 -f ...