Datasets:
File size: 1,909 Bytes
5d77709 8aa7d22 5d77709 8aa7d22 5d77709 8aa7d22 5d77709 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
pretty_name: Moving MNIST (test split)
task_categories:
- video-classification
language:
- en
tags:
- moving-mnist
- video
- synthetic
- grayscale
size_categories:
- 10K<n<100K
---
# 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:
```bibtex
@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/
|