ImageRecognitionDataset

Caltech101/256, CIFAR-10/100, MNIST/FashionMNIST, omniglot

Requirement

• Python >= 3.8
• Poetry >= 1.2

Install

pip

pip install numpy pillow tqdm

poetry

poetry install

Usage

# Dataset Download 
python src/download.py --dataset {CIFAR10 | CIFAR100 | MNIST | fashionMNIST | caltech101 | caltech256 | omniglot}
# Calculate Dataset Mean Std
python src/calculate.py --dataset {CIFAR10 | CIFAR100 | MNIST | fashionMNIST | caltech101 | caltech256 | omniglot}

Caluculated Result

GrayScale dataset

dataset	mean	std
MNIST(train)	0.1307	0.3013
fashionMNIST(train)	0.2860	0.3202
Omniglot(images_background)	0.9221	0.2622

RGB dataset

dataset	mean(R, G, B)	std(R, G, B)
CIFAR10(train)	(0.4914, 0.4822, 0.4465)	(0.2022, 0.1993, 0.2009)
CIFAR100(train)	(0.5071, 0.4865, 0.4409)	(0.2008, 0.1983, 0.2022)
Caltech101(all images)	(0.5487, 0.5313, 0.5050)	(0.2497, 0.2467, 0.2483)
Caltech256(all images)	(0.5520, 0.5336, 0.5050)	(0.2420, 0.2412, 0.2438)

Link

Mean and std calculations are based on　https://discuss.pytorch.org/t/about-normalization-using-pre-trained-vgg16-networks/23560/5