Dataset Card for dSprites

Dataset Description

The dSprites dataset is a synthetic 2D shapes dataset designed for benchmarking algorithms in disentangled representation learning and unsupervised representation learning. It is widely used as a standard benchmark in the representation learning community.

The dataset was introduced in the β-VAE paper and consists of procedurally generated binary black-and-white images of 2D sprites, under controlled variations of 6 known factors of variation:

Object color (1 value: white)
Object shape (3 values: square, ellipse, heart)
Object scale (6 values)
Object orientation (40 values)
Object position X (32 values)
Object position Y (32 values)

All possible combinations of these factors are present exactly once, generating a total of 737,280 images at a resolution of 64×64 pixels. The ground-truth latent factors are provided for each image, both as discrete classes and continuous values. The dataset is specifically designed for assessing the ability of models to learn disentangled representations, and has been used in many follow-up works after β-VAE.

The dataset is commonly used for benchmarking disentanglement learning, and can be used in conjunction with other variants:

Dataset Source

Homepage: https://github.com/google-deepmind/dsprites-dataset
License: zlib/libpng License
Paper: Irina Higgins et al. β-VAE: Learning basic visual concepts with a constrained variational framework. ICLR 2017.

Dataset Structure

Factors	Possible Classes (Indices)	Values
color	white=0	1.0 (fixed)
shape	square=0, ellipse=1, heart=2	1.0, 2.0, 3.0 (categorical)
scale	0,...,5	[0.5, 1.0] linearly spaced (6 values)
orientation	0,...,39	[0, 2π] radians (40 values)
posX	0,...,31	[0, 1] normalized position (32 values)
posY	0,...,31	[0, 1] normalized position (32 values)

Each image corresponds to a unique combination of these 6 factors. The images are stored in a row-major order (fastest-changing factor is posY, slowest-changing factor is color).

Why no train/test split?

The dSprites dataset does not provide an official train/test split. It is designed for representation learning research, where the goal is to learn disentangled and interpretable latent factors. Since the dataset is a complete Cartesian product of all factor combinations, models typically require access to the full dataset to explore factor-wise variations.

Example Usage

Below is a quick example of how to load this dataset via the Hugging Face Datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("randall-lab/dsprites", split="train", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]         # [color_idx, shape_idx, scale_idx, orientation_idx, posX_idx, posY_idx]
label_values = example["label_values"]  # corresponding continuous values

# Label Classes
color = example["color"]         # 0
shape = example["shape"]         # 0-2
scale = example["scale"]         # 0-5
orientation = example["orientation"]   # 0-39
posX = example["posX"]           # 0-31
posY = example["posY"]           # 0-31

# Label Values
color_value = example["colorValue"]         # 1.0
shape_value = example["shapeValue"]         # 1.0, 2.0, 3.0
scale_value = example["scaleValue"]         # [0.5, 1.0]
orientation_value = example["orientationValue"]   # [0, 2π]
posX_value = example["posXValue"]           # [0, 1]
posY_value = example["posYValue"]           # [0, 1]

image.show()  # Display the image
print(f"Label (factors): {label}")
print(f"Label values (factors): {label_values}")

If you are using colab, you should update datasets to avoid errors

pip install -U datasets

Citation

@inproceedings{higgins2017beta,
  title={beta-vae: Learning basic visual concepts with a constrained variational framework},
  author={Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
  booktitle={International conference on learning representations},
  year={2017}
}