/CIFAR10-CIFAR100

image classification for CIFAR-10, CIFAR-100 using pytorch

Primary LanguagePython

Course project of CSE463 Machine Learning, UNIST

Image Classification for CIFAR-10, CIFAR-100

Platform

This code was implemented and tested with below environment

python == 3.6.12
torch == 1.9.0
torchvision == 0.10.0

Training

# ************************** CIFAR-10 *****************************
$ python train.py --model lenet --dataset cifar10 --n_train 20000 
$ python train.py --model MLP --dataset cifar10 --n_train 20000 
$ python train.py --model resnet --dataset cifar10 --n_train 20000 
$ python train.py --model vgg --dataset cifar10 --n_train 20000 
$ python train.py --model googlenet --dataset cifar10 --n_train 20000 
# ************************** CIFAR-100 *****************************
$ python train.py --model lenet --dataset cifar100 --n_train 20000 
$ python train.py --model MLP --dataset cifar100 --n_train 20000 
$ python train.py --model resnet --dataset cifar100 --n_train 20000 
$ python train.py --model vgg --dataset cifar100 --n_train 20000 
$ python train.py --model googlenet --dataset cifar100 --n_train 20000

Performance

  • lr : 0.005, epochs: 100, batch_size : 100, optimizer : SGD, n_train : 20000
Dataset Model Accuracy
CIFAR-10 RF 42.19 %
CIFAR-10 MLP 44.3 %
CIFAR-10 LeNet 64 %
CIFAR-100 RF 16.7 %
CIFAR-100 MLP 2.45 %
CIFAR-100 LeNet 24 %
  • learning rate
    • epochs: 100, batch_size : 100, optimizer : SGD, n_train : 20000
Dataset Learning Rate Accuracy
CIFAR-10 0.1 25 %
CIFAR-10 0.01 61 %
CIFAR-10 0.005 64 %
CIFAR-10 0.001 55 %

lr

  • CNN

    • lr : 0.005, epochs: 100, batch_size : 100, optimizer : SGD, n_train : 20000
    Dataset Model Accuracy
    CIFAR-10 LeNet-5 64 %
    CIFAR-10 GoogLeNet 74 %
    CIFAR-10 VGG19 78 %
    CIFAR-10 ResNet18 74 %

model

  • Dataset

    • lr : 0.005, epochs: 100, batch_size : 100, optimizer : SGD, n_train : 20000
    Dataset Model Accuracy
    CIFAR-10 LeNet-5 64 %
    CIFAR-100 LeNet-5 24 %
    Fasion MNIST LeNet-5 85 %

dataset

  • number of training data
    • lr : 0.005, epochs: 100, batch_size : 100, optimizer : SGD
Dataset Model # of data Accuracy
CIFAR-10 LeNet-5 10000 56 %
CIFAR-10 LeNet-5 20000 64 %
CIFAR-10 LeNet-5 30000 66 %
CIFAR-10 LeNet-5 40000 66 %
CIFAR-10 LeNet-5 50000 69 %

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