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VGGNet

This is the implementation of "VGGNet" for Multiclass Classification.
Original paper: K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015. link

Usage

1. Build

Please build the source file according to the procedure.

$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ cd ..

2. Dataset Setting

Recommendation

  • THE MNIST DATABASE of handwritten digits
    This is the dataset of 28x28 grayscale for handwritten digits in 10 classes that has a training set of 60000 images and a test set of 10000 images.
    Link: official

  • The CIFAR-10 dataset
    This is the dataset of 32x32 color based on labeled tiny images in 10 classes that has a training set of 50000 images and a test set of 10000 images.
    Link: official

  • The CIFAR-100 dataset
    This is the dataset of 32x32 color based on labeled tiny images in 100 classes that has a training set of 50000 images and a test set of 10000 images.
    Link: official

Setting

Please create a link for the dataset.
The following hierarchical relationships are recommended.

datasets
|--Dataset1
|    |--train
|    |    |--class1
|    |    |    |--image1.png
|    |    |    |--image2.bmp
|    |    |    |--image3.jpg
|    |    |
|    |    |--class2
|    |    |--class3
|    |
|    |--valid
|    |--test
|
|--Dataset2
|--Dataset3

The following is an example for "MNIST".
This is downloaded and placed, maintaining the above hierarchical relationships.

$ cd datasets
$ sudo apt install python3 python3-pip
$ pip3 install scikit-image
$ sh ../../../scripts/set_MNIST.sh
$ cd ..

Please set the text file for class names.

$ vi list/MNIST.txt

In case of "MNIST", please set as follows.

0
1
2
3
4
5
6
7
8
9

3. Training

Setting

Please set the shell for executable file.

$ vi scripts/train.sh

The following is an example of the training phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='MNIST'

./VGGNet \
    --train true \
    --n_layers 16 \
    --BN true \
    --epochs 300 \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 10 \
    --size 224 \
    --batch_size 16 \
    --gpu_id 0 \
    --nc 1

Run

Please execute the following to start the program.

$ sh scripts/train.sh

4. Test

Setting

Please set the shell for executable file.

$ vi scripts/test.sh

The following is an example of the test phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='MNIST'

./VGGNet \
    --test true \
    --n_layers 16 \
    --BN true \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 10 \
    --size 224 \
    --gpu_id 0 \
    --nc 1

Run

Please execute the following to start the program.

$ sh scripts/test.sh

Acknowledgments

This code is inspired by VGG16-PyTorch.