vgg_cifar10

Visual Geometry Group (VGG)

Source

Model Info

The basic building block of classic CNNs is a sequence of the following: (i) a convolutional layer with padding to maintain the resolution, (ii) a nonlinearity such as a ReLU, (iii) a pooling layer such as a maximum pooling layer. One VGG block consists of a sequence of convolutional layers, followed by a maximum pooling layer for spatial downsampling. In the original VGG paper Simonyan & Zisserman, 2015, the authors employed convolutions with 3×3 kernels with padding of 1 (keeping height and width) and 2×2 maximum pooling with stride of 2 (halving the resolution after each block).

Training

cd vision/vgg_cifar10
julia --project vgg_cifar10.jl

References

• Simonyan, K. and Zisserman, A., “Very Deep Convolutional Networks for Large-Scale Image Recognition”, arXiv e-prints, 2015.

• Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola, "Dive into Deep Learning", 2020