Multi-digit MNIST for Few-shot Learning

Multi-digit MNIST generator creates datasets consisting of handwritten digit images from MNIST for few-shot image classification and meta-learning. It simply samples images from MNIST dataset and put digits together to create images with multiple digits. It also creates training/validation/testing splits (64/20/16 classes for DoubleMNIST and 640/200/160 for TripleMNIST).

You can generate customized by following the cammands provided in Usage to change the number of images in each class, the image size, etc. You can also download generated datasets from Datasets.

This repository benchmarks the performance of MAML (Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks) using datasets created via the generation script in a variety of settings.

Some examples of images from the datasets are as follows.

• Double MNIST Datasets (100 classes: 00 to 99)

Class	10	48	59	62	73
Image

• Triple MNIST Datasets (1000 classes: 000 to 999)

Class	039	146	258	512	874
Image

Prerequisites

• NumPy
• imageio

Usage

Generate a DoubleMNIST dataset with 1k images for each class

python generator.py --num_image_per_class 1000 --multimnist_path ./dataset/double_mnist --num_digit 2 --image_size 64 64

Generate a TripleMNIST dataset with 1k images for each class

python generator.py --num_image_per_class 1000 --multimnist_path ./dataset/triple_mnist --num_digit 3 --image_size 84 84

Arguments

• --mnist_path: the path to the MNIST dataset (download it if not found)
• --multimnist_path: the path to the output Multi-digit MNIST dataset
• --num_digit: how many digits in an image
• --train_val_test_ratio: determine how many classes for train, val, and test
• --image_size: the size of images. Note that the width needs to be larger than num_digit * mnist_width
• --num_image_per_class: how many images for each class
• --random_seed: numpy random seed

Datasets

You can download the generated datasets

Benchmark

This repository benchmarks training MAML (Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks) using datasets created via this generation script in a variety of settings.

Hyperparameters

• slow learning rate: 1e-3
• fast learning rate: 0.4
• number of gradient steps: 1
• meta batch size: 12
• number of conv layers: 4
• iterations: 100k

Training

*The trainings have not fully converged and the new results will be reported once they are finished.

Cite this repository

@misc{mulitdigitmnist,
  author = {Sun, Shao-Hua},
  title = {Multi-digit MNIST for Few-shot Learning},
  year = {2019},
  journal = {GitHub repository},
  url = {https://github.com/shaohua0116/MultiDigitMNIST},
}

Author

Shao-Hua Sun