Create README.md

README.md

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+---
+license: cc-by-4.0
+---
+# Dataset Card for MPI3D-toy
+
+## Dataset Description
+
+The **MPI3D-toy dataset** is a **synthetically rendered image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. It is part of the broader MPI3D dataset suite, which also includes **realistic simulated** and **real-world** variants.
+
+The **toy version** was generated using a **simplified computer graphics renderer** (Quicksilver hardware renderer in Autodesk 3ds Max), based on CAD models of a physical robotic setup. This allows researchers to systematically study **sim-to-real transfer** and the effect of simulation fidelity.
+
+All images depict **physical 3D objects** that would be manipulated by a robotic platform, under **controlled variations of 7 known factors**:
+
+- Object color (6 values)
+- Object shape (6 values)
+- Object size (2 values)
+- Camera height (3 values)
+- Background color (3 values)
+- Robotic arm horizontal axis (40 values)
+- Robotic arm vertical axis (40 values)
+
+The dataset contains **1,036,800 images** at a resolution of **64×64 pixels**, representing the full Cartesian product of these factors. All factors are **identical** to those used in the realistic and real-world versions of MPI3D, enabling direct comparisons between different levels of simulation fidelity.
+
+## Dataset Source
+- **Homepage**: [https://github.com/rr-learning/disentanglement_dataset](https://github.com/rr-learning/disentanglement_dataset)
+- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)
+- **Paper**: Muhammad Waleed Gondal et al. _On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset_. NeurIPS 2019.
+
+## Dataset Structure
+
+|Factors|Possible Values|
+|---|---|
+|object_color|white=0, green=1, red=2, blue=3, brown=4, olive=5|
+|object_shape|cone=0, cube=1, cylinder=2, hexagonal=3, pyramid=4, sphere=5|
+|object_size|small=0, large=1|
+|camera_height|top=0, center=1, bottom=2|
+|background_color|purple=0, sea green=1, salmon=2|
+|horizontal_axis (DOF1)|0,...,39|
+|vertical_axis (DOF2)|0,...,39|
+
+Each image corresponds to a unique combination of these 7 factors. The images are stored in a **row-major order** (fastest-changing factor is `vertical_axis`, slowest-changing factor is `object_color`).
+
+### Why no train/test split?
+The MPI3D-toy 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("your-hf-namespace/mpi3d_toy", split="train", trust_remote_code=True)
+
+# Access a sample from the dataset
+example = dataset[0]
+image = example["image"]
+label = example["label"]  # [object_color: 0, object_shape: 0, object_size: 0, camera_height: 0, background_color: 0, horizontal_axis: 0, vertical_axis: 0]
+color = example["color"] # 0 
+shape = example["shape"] # 0
+size = example["size"] # 0
+height = example["height"] # 0
+background = example["background"] # 0 
+dof1 = example["dof1"] # 0 
+dof2 = example["dof2"] # 0
+
+
+image.show()  # Display the image
+print(f"Label (factors): {label}")
+```
+If you are using colab, you should update datasets to avoid errors
+```
+pip install -U datasets
+```
+
+## Citation
+```
+@article{gondal2019transfer,
+  title={On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset},
+  author={Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
+  journal={Advances in Neural Information Processing Systems},
+  volume={32},
+  year={2019}
+}
+```