README.md

---
license: zlib
---
# 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.

![Dataset Visualization](https://huggingface.co/datasets/randall-lab/dsprites/resolve/main/animation0.gif)

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

- [randall-lab/dsprites-color](https://huggingface.co/datasets/randall-lab/dsprites-color)
- [randall-lab/dsprites-noisy](https://huggingface.co/datasets/randall-lab/dsprites-noisy)
- [randall-lab/dsprites-scream](https://huggingface.co/datasets/randall-lab/dsprites-scream)

## Dataset Source
- **Homepage**: [https://github.com/google-deepmind/dsprites-dataset](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:
```python
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}
}
```