ResNet

ResNet

This is the implementation of "ResNet" for Multiclass Classification.
Original paper: K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceesings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 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'

./ResNet \
    --train true \
    --n_layers 50 \
    --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'

./ResNet \
    --test true \
    --n_layers 50 \
    --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 pytorch-cifar.