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README.md

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+---
+license: cc-by-4.0
+---
+# Dataset Card for MPI3D-complex
+
+## Dataset Description
+
+The **MPI3D-complex dataset** is a **real-world image dataset** of **complex everyday objects**, designed for benchmarking algorithms in **disentangled representation learning** and **robustness to object variability**. It is an **extension** of the broader MPI3D dataset suite, which also includes [synthetic toy](https://huggingface.co/datasets/randall-lab/mpi3d-toy), [realistic simulated](https://huggingface.co/datasets/randall-lab/mpi3d-realistic), and [real-world geometric shape](https://huggingface.co/datasets/randall-lab/mpi3d-real) variants.
+
+The **complex version** was recorded using the same **robotic platform** as the other MPI3D datasets, but with a new set of **real-world objects** (coffee-cup, tennis-ball, croissant, beer-cup) and a reduced color set. Images were captured using **real cameras**, with realistic lighting and background conditions. This allows researchers to assess how well models trained on simpler domains generalize to more complex object appearances.
+
+All images depict **real-world objects** under **controlled variations of 7 known factors**:
+
+- Object color (4 values)
+- Object shape (4 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 **460,800 images** at a resolution of **64×64 pixels**. The factors of variation are consistent with the other MPI3D datasets, but with **different factor sizes** for object shape and color.
+![Dataset Visualization](https://huggingface.co/datasets/randall-lab/mpi3d-complex/resolve/main/complex1.gif)
+
+## 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|yellow=0, green=1, olive=2, red=3|
+|object_shape|coffee-cup=0, tennis-ball=1, croissant=2, beer-cup=3|
+|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-complex dataset does not provide an official train/test split. It is designed for **representation learning research** and for testing **robustness to object complexity and appearance variation**. 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/mpi3d-complex", 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}
+}
+```

mpi3d-complex.py

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+import datasets
+import numpy as np
+from PIL import Image
+
+_MPI3D_URL = "https://drive.google.com/file/d/1Tp8eTdHxgUMtsZv5uAoYAbJR1BOa_OQm/view?usp=sharing"
+
+class MPI3DComplex(datasets.GeneratorBasedBuilder):
+    """MPI3D Complex dataset: 4x4x2x3x3x40x40 factor combinations, 64x64 RGB images."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=(
+                "MPI3D Complex dataset: real-world images of complex everyday objects (coffee-cup, tennis-ball, "
+                "croissant, beer-cup) manipulated by a robotic platform under controlled variations of 7 known factors. "
+                "Images are 64x64 RGB (downsampled). "
+                "Factors: object color (4), object shape (4), object size (2), camera height (3), "
+                "background color (3), robotic arm DOF1 (40), robotic arm DOF2 (40). "
+                "Images were captured using real cameras, introducing realistic noise and lighting conditions. "
+                "The images are ordered as the Cartesian product of the factors in row-major order."
+            ),
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),    # (64, 64, 3)
+                    "index": datasets.Value("int32"),  # index of the image
+                    "label": datasets.Sequence(datasets.Value("int32")),  # 7 factor indices
+                    "color": datasets.Value("int32"),  # object color index (0-3)
+                    "shape": datasets.Value("int32"),  # object shape index (0-3)
+                    "size": datasets.Value("int32"),  # object size index (0-1)
+                    "height": datasets.Value("int32"),  # camera height index (0-2)
+                    "background": datasets.Value("int32"),  # background color index (0-2)
+                    "dof1": datasets.Value("int32"),  # robotic arm DOF1 index (0-39)
+                    "dof2": datasets.Value("int32"),  # robotic arm DOF2 index (0-39)
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/rr-learning/disentanglement_dataset",
+            license="Creative Commons Attribution 4.0 International",
+            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}
+}""",
+        )
+
+    def _split_generators(self, dl_manager):
+        npz_path = dl_manager.download(_MPI3D_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"npz_path": npz_path},
+            ),
+        ]
+
+    def _generate_examples(self, npz_path):
+        # Load npz
+        data = np.load(npz_path)
+        images = data["images"]  # shape: (460800, 64, 64, 3)
+
+        factor_sizes = np.array([4, 4, 2, 3, 3, 40, 40])  # key change for complex
+        factor_bases = np.cumprod([1] + list(factor_sizes[::-1]))[::-1][1:]
+
+        def index_to_factors(index):
+            factors = []
+            for base, size in zip(factor_bases, factor_sizes):
+                factor = (index // base) % size
+                factors.append(int(factor))
+            return factors
+
+        # Iterate over images
+        for idx in range(len(images)):
+            img = images[idx]
+            img_pil = Image.fromarray(img)
+
+            factors = index_to_factors(idx)
+
+            yield idx, {
+                "image": img_pil,
+                "index": idx,
+                "label": factors,
+                "color": factors[0],
+                "shape": factors[1],
+                "size": factors[2],
+                "height": factors[3],
+                "background": factors[4],
+                "dof1": factors[5],
+                "dof2": factors[6],
+            }