metadata
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int32
- name: x
sequence: image
- name: floor_hue
dtype:
class_label:
names:
'0': '0.0'
'1': '0.1'
'2': '0.2'
'3': '0.3'
'4': '0.4'
'5': '0.5'
'6': '0.6'
'7': '0.7'
'8': '0.8'
'9': '0.9'
- name: wall_hue
dtype:
class_label:
names:
'0': '0.0'
'1': '0.1'
'2': '0.2'
'3': '0.3'
'4': '0.4'
'5': '0.5'
'6': '0.6'
'7': '0.7'
'8': '0.8'
'9': '0.9'
- name: object_hue
dtype:
class_label:
names:
'0': '0.0'
'1': '0.1'
'2': '0.2'
'3': '0.3'
'4': '0.4'
'5': '0.5'
'6': '0.6'
'7': '0.7'
'8': '0.8'
'9': '0.9'
- name: shape
dtype:
class_label:
names:
'0': cube
'1': cylinder
'2': sphere
'3': capsule
- name: scale_dynamic
dtype:
class_label:
names:
'0': increasing_1x
'1': decreasing_1x
'2': increasing_2x
'3': decreasing_2x
'4': alternating_big_small
'5': alternating_small_big
- name: orientation_dynamic
dtype:
class_label:
names:
'0': counterclockwise
'1': static
'2': clockwise
splits:
- name: train
num_bytes: 1719272462
num_examples: 50400
- name: val
num_bytes: 386294024
num_examples: 10800
- name: test
num_bytes: 385308154
num_examples: 10800
download_size: 1992990038
dataset_size: 2490874640
MSD Shapes3D Dataset Attribution
The Multi-factor Sequential Disentanglement benchmark includes a modified variant of the 3D Shapes dataset, adapted to support sequential multi-factor disentanglement.
In this setup, the floor hue, wall hue, object hue, and shape are static, while the scale and orientation change over time.
- Original repository:
https://github.com/deepmind/3d-shapes
@misc{3dshapes18,
title={3D Shapes Dataset},
author={Burgess, Chris and Kim, Hyunjik},
howpublished={https://github.com/deepmind/3dshapes-dataset/},
year={2018}
}
⚠ Note: The 3D Shapes dataset is released under the Apache License 2.0. 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.