README.md

metadata

license: apache-2.0
task_categories:
  - feature-extraction
tags:
  - disentanglement
  - sequential_disentanglement
pretty_name: SMD Sprites
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: x
      sequence: image
    - name: movement
      dtype:
        class_label:
          names:
            '0': front walk
            '1': left walk
            '2': right walk
            '3': front spellcard
            '4': left spellcard
            '5': right spellcard
            '6': front slash
            '7': left slash
            '8': right slash
    - name: body
      dtype:
        class_label:
          names:
            '0': light
            '1': neutral
            '2': dark-gray
            '3': gray
            '4': brown
            '5': black
    - name: bottom
      dtype:
        class_label:
          names:
            '0': white
            '1': yellow
            '2': red
            '3': gray
            '4': shade-green
            '5': green
    - name: top
      dtype:
        class_label:
          names:
            '0': red
            '1': blue
            '2': white
            '3': gray
            '4': brown
            '5': white-tie
    - name: hair
      dtype:
        class_label:
          names:
            '0': green
            '1': blue
            '2': orange
            '3': white
            '4': red
            '5': purple
  splits:
    - name: train
      num_bytes: 110413032.432
      num_examples: 8164
    - name: val
      num_bytes: 24119309.5
      num_examples: 1750
    - name: test
      num_bytes: 24075703.5
      num_examples: 1750
  download_size: 140865862
  dataset_size: 158608045.43199998
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*

MSD Sprites Dataset Attribution

The Multi-factor Sequential Disentanglement benchmark includes a modified variant of the Sprites dataset, adapted to support sequential multi-factor disentanglement.

• Original repository:
  https://github.com/YingzhenLi/Sprites

• Reference paper:
  Y. Li, J. Yu, C. Zhang, C. Gan.
  Disentangled Sequential Autoencoder, ICML 2018.
  https://arxiv.org/abs/1803.02991

@inproceedings{li2018disentangle,
  title = {Disentangled Sequential Autoencoder},
  author = {Li, Yingzhen and Mandt, Stephan},
  booktitle = {International Conference on Machine Learning},
  year = {2018}
}

⚠ Note: The Sprites dataset does not have an explicit open-source license. We redistribute it here solely for non-commercial research purposes, following the original publication. Please cite the above paper when using this dataset in your work.