ryushinn/MMNIST

Moving MNIST (train split)

This dataset is the classic Moving MNIST benchmark set released by Nitish Srivastava et al. for sequence prediction and video representation learning.

• Source file: mnist_test_seq.npy
• Original source: https://www.cs.toronto.edu/~nitish/unsupervised_video/
• Content: 10,000 sequences, each 20 frames long
• Frame size: 64x64, grayscale
• Data type: uint8

Dataset structure

This Hugging Face dataset stores one sequence per row:

• Split: train
• Number of rows: 10000
• Feature schema:
  • video: Array3D(shape=(20, 64, 64), dtype='uint8')

Each video item is a full sequence of 20 frames.

How the original data was created

The original Moving MNIST sequences are synthetic videos formed by placing MNIST digit sprites into a 64x64 canvas and moving them with constant velocity and elastic wall bounces. In this specific benchmark file, each sequence contains two moving digits over 20 time steps.

The released benchmark file (mnist_test_seq.npy) is arranged as:

• Raw shape: (20, 10000, 64, 64) = (time, sequence, height, width)

For this Hugging Face conversion, it is reorganized conceptually into per-example rows:

• Per-example shape: (20, 64, 64)

Citation

If you use this dataset, please cite the original paper:

@inproceedings{srivastava2015unsupervised,
  title={Unsupervised Learning of Video Representations using LSTMs},
  author={Srivastava, Nitish and Mansimov, Elman and Salakhutdinov, Ruslan},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2015}
}

And optionally reference the project page distributing the benchmark file:

• https://www.cs.toronto.edu/~nitish/unsupervised_video/