| --- |
| license: apache-2.0 |
| --- |
| # Dataset Card for 3dshapes |
|
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| ## Dataset Description |
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| The **3dshapes dataset** is a **synthetic 3D object image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. |
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| It was introduced in the **FactorVAE** paper [[Kim & Mnih, ICML 2018](https://proceedings.mlr.press/v80/kim18b.html)], as one of the standard testbeds for learning interpretable and disentangled latent factors. The dataset consists of images of **3D procedurally generated scenes**, where 6 **ground-truth independent factors of variation** are explicitly controlled: |
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| - **Floor color** (hue) |
| - **Wall color** (hue) |
| - **Object color** (hue) |
| - **Object size** (scale) |
| - **Object shape** (categorical) |
| - **Object orientation** (rotation angle) |
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| **3dshapes is generated as a full Cartesian product of all factor combinations**, making it perfectly suited for systematic evaluation of disentanglement. The dataset contains **480,000 images** at a resolution of **64×64 pixels**, covering **all possible combinations of the 6 factors exactly once**. The images are stored in **row-major order** according to the factor sweep, enabling precise control over factor-based evaluation. |
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| ## Dataset Source |
| - **Homepage**: [https://github.com/deepmind/3dshapes-dataset](https://github.com/deepmind/3dshapes-dataset) |
| - **License**: [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0) |
| - **Paper**: Hyunjik Kim & Andriy Mnih. _Disentangling by Factorising_. ICML 2018. |
|
|
| ## Dataset Structure |
| |Factors|Possible Values| |
| |---|---| |
| |floor_color (hue)| 10 values linearly spaced in [0, 1] | |
| |wall_color (hue)| 10 values linearly spaced in [0, 1] | |
| |object_color (hue)| 10 values linearly spaced in [0, 1] | |
| |scale| 8 values linearly spaced in [0.75, 1.25] | |
| |shape| 4 values: 0, 1, 2, 3 | |
| |orientation| 15 values linearly spaced in [-30, 30] | |
| |
| Each image corresponds to a unique combination of these **6 factors**. The images are stored in a **row-major order** (fastest-changing factor is `orientation`, slowest-changing factor is `floor_color`). |
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| ### Why no train/test split? |
| The 3dshapes 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/shapes3d", split="train", trust_remote_code=True) |
| |
| # Access a sample from the dataset |
| example = dataset[0] |
| image = example["image"] |
| label = example["label"] # Value labels: [floor_hue, wall_hue, object_hue, scale, shape, orientation] |
| label_index = example["label_index"] # Index labels: [floor_idx, wall_idx, object_idx, scale_idx, shape_idx, orientation_idx] |
| |
| # Label Value |
| floor_value = example["floor"] # 0-1 |
| wall_value = example["wall"] # 0-1 |
| object_value = example["object"] # 0-1 |
| scale_value = example["scale"] # 0.75-1.25 |
| shape_value = example["shape"] # 0,1,2,3 |
| orientation_value = example["orientation"] # -30 - 30 |
| |
| # Label index |
| floor_idx = example["floor_idx"] # 0-9 |
| wall_idx = example["wall_idx"] # 0-9 |
| object_idx = example["object_idx"] # 0-9 |
| scale_idx = example["scale_idx"] # 0-7 |
| shape_idx = example["shape_idx"] # 0-3 |
| orientation_idx = example["orientation_idx"] # 0-14 |
| |
| image.show() # Display the image |
| print(f"Label (factor values): {label}") |
| print(f"Label (factor indices): {label_index}") |
| ``` |
| If you are using colab, you should update datasets to avoid errors |
| ``` |
| pip install -U datasets |
| ``` |
| ## Citation |
| ``` |
| @InProceedings{pmlr-v80-kim18b, |
| title = {Disentangling by Factorising}, |
| author = {Kim, Hyunjik and Mnih, Andriy}, |
| booktitle = {Proceedings of the 35th International Conference on Machine Learning}, |
| pages = {2649--2658}, |
| year = {2018}, |
| editor = {Dy, Jennifer and Krause, Andreas}, |
| volume = {80}, |
| series = {Proceedings of Machine Learning Research}, |
| month = {10--15 Jul}, |
| publisher = {PMLR}, |
| pdf = {http://proceedings.mlr.press/v80/kim18b/kim18b.pdf}, |
| url = {https://proceedings.mlr.press/v80/kim18b.html}, |
| abstract = {We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon beta-VAE by providing a better trade-off between disentanglement and reconstruction quality and being more robust to the number of training iterations. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.} |
| } |
| ``` |
