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README.md

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+---
+license: apache-2.0
+---
+# Dataset Card for 3dshapes
+
+## Dataset Description
+
+The **3dshapes dataset** is a **synthetic 3D object image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**.  
+
+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:
+
+- **Floor color** (hue)
+- **Wall color** (hue)
+- **Object color** (hue)
+- **Object size** (scale)
+- **Object shape** (categorical)
+- **Object orientation** (rotation angle)
+
+**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.
+![Dataset Visualization](https://huggingface.co/datasets/randall-lab/shapes3d/resolve/main/3dshapes.gif)
+
+## 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`).
+
+### 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("YOUR_HF_REPO/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.}
+}
+```

shapes3d.py

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+import datasets
+import numpy as np
+import os
+from PIL import Image
+
+_SHAPES3D_URL = "https://huggingface.co/datasets/randall-lab/shapes3d/resolve/main/shapes3d.npz"
+
+class Shapes3D(datasets.GeneratorBasedBuilder):
+    """Shapes3D dataset: 10x10x10x8x4x15 factor combinations, 64x64 RGB images."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=(
+                "Shapes3D dataset: procedurally generated images of 3D shapes with 6 independent factors of variation. "
+                "Commonly used for disentangled representation learning. "
+                "Factors: floor hue (10), wall hue (10), object hue (10), scale (8), shape (4), orientation (15). "
+                "Images are stored 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("float32")),  # 6 factor values (continuous)
+                    "label_index": datasets.Sequence(datasets.Value("int32")),  # 6 factor indices
+                    "floor": datasets.Value("float32"),  # value of floor (0-1)
+                    "wall": datasets.Value("float32"),   # value of wall (0-1)
+                    "object": datasets.Value("float32"),  # value of object (0-1)
+                    "scale": datasets.Value("float32"),  # value of scale (0.75-1.25)
+                    "shape": datasets.Value("float32"),    # value of shape (0-3)
+                    "orientation": datasets.Value("float32"),  # value of orientation (-30 to 30)
+                    "floor_idx": datasets.Value("int32"),
+                    "wall_idx": datasets.Value("int32"),
+                    "object_idx": datasets.Value("int32"),
+                    "scale_idx": datasets.Value("int32"),
+                    "shape_idx": datasets.Value("int32"),
+                    "orientation_idx": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/google-deepmind/3dshapes-dataset/",
+            license="apache-2.0",
+            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}
+}""",
+        )
+
+    def _split_generators(self, dl_manager):
+        npz_path = dl_manager.download(_SHAPES3D_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"]  # (480000, 64, 64, 3)
+        labels = data["labels"]  # (480000, 6)
+
+        # Define factor sizes (from README / paper)
+        factor_sizes = np.array([10, 10, 10, 8, 4, 15])
+        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)
+
+            label_value = labels[idx].tolist()
+            label_index = index_to_factors(idx)
+
+            yield idx, {
+                "image": img_pil,
+                "index": idx,
+                "label": label_value,
+                "label_index": label_index,
+                "floor": label_value[0],
+                "wall": label_value[1], 
+                "object": label_value[2],
+                "scale": label_value[3],
+                "shape": label_value[4], 
+                "orientation": label_value[5],
+                "floor_idx": label_index[0],
+                "wall_idx": label_index[1],
+                "object_idx": label_index[2],
+                "scale_idx": label_index[3],
+                "shape_idx": label_index[4],
+                "orientation_idx": label_index[5],
+            }