pytorch-cifar10

Personal practice on CIFAR10 with PyTorch
Inspired by pytorch-cifar by kuangliu.

Introduction

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

Requirement

python3.6
numpy
pytorch 0.4.0
torchvision 0.2.0

Usage

python3 main.py

optional arguments:

--lr                default=1e-3    learning rate
--epoch             default=200     number of epochs tp train for
--trainBatchSize    default=100     training batch size
--testBatchSize     default=100     test batch size

Configs

200 epochs for each run-through,
500 batches for each training epoch,
100 batches for each validating epoch,
100 images for each training and validating batch

Learning Rate

1e-3 for [1,74] epochs
5e-4 for [75,149] epochs
2.5e-4 for [150,200) epochs

Result

Models	Accuracy	Comments
LeNet	67.52%	- - - -
Alexnet	74.74%	Result is far away from my expectation (5%+). Reasons might be inappropriate modification to fit dataset(32x32 images).
VGG11	87.48%	- - - -
VGG13	90.17%	- - - -
VGG16	TBD	- - - -
VGG19	TBD	- - - -
GoogleNet	92.57%	- - - -
ResNet18	TBD	- - - -
ResNet34	TBD	- - - -
ResNet50	TBD	- - - -
ResNet101	TBD	- - - -
ResNet152	TBD	- - - -
DenseNet121	TBD	- - - -
DenseNet161	TBD	- - - -
DenseNet169	TBD	- - - -
DenseNet201	TBD	- - - -