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README.md

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-# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
-{}
+license: zlib
 ---
-
 # Dataset Card for dSprites
 
-<!-- Provide a quick summary of the dataset. -->
+## Dataset Description
 
-## Dataset Details
+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.
 
-### Dataset Description
+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**:
 
-<!-- Provide a longer summary of what this dataset is. -->
-dSprites is a dataset of 2D shapes procedurally generated from 6 ground truth independent latent factors. These factors are color, shape, scale, rotation, x-position, and y-position of a sprite. All possible combinations of these latents are present exactly once, generating 737,280 total images. The dataset is widely used for studying disentangled representations in machine learning.
+- 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)
 
-### Dataset Sources
+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.
 
-<!-- Provide the basic links for the dataset. -->
+![Dataset Visualization](https://github.com/deepmind/dsprites-dataset/raw/master/dsprites.gif)
 
-- **Homepage:** https://github.com/google-deepmind/dsprites-dataset
-- **Paper:** Matthey, L., Higgins, I., Hassabis, D., & Lerchner, A. (2017, May). dsprites: Disentanglement testing sprites dataset.
+## 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)|
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-Each sample in the dataset contains:
-
-- **image:** A 64×64 grayscale image
-
-- **orientation:** A float representing the rotation angle of the shape
-
-- **shape:** A categorical label representing the shape type (square, ellipse, heart)
-
-- **scale:** A float representing the size of the shape
-
-- **color:** A categorical label representing the color (only 'white' in this dataset)
-
-- **position_x:** A float representing the x-position of the shape
-
-- **position_y:** A float representing the y-position of the shape
-
-Total images: 737,280
-
-Classes: 3 (square, ellipse, heart)
-
-Splits:
+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`).
 
-- **Train:** 70% of total dataset
-
-- **Test:** 30% of total dataset
-
-Image specs: PNG format, 64×64 pixels, grayscale
+### 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.
+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}")
 ```
-from datasets import load_dataset  
-
-# Load the dataset  
-dataset = load_dataset("../../aidatasets/images/dsprites.py", split="train", trust_remote_code=True)
-# dataset = load_dataset("../../aidatasets/images/dsprites.py", split="test", trust_remote_code=True)  
-
-# Access a sample from the dataset  
-example = dataset[0]  
-image = example["image"]  
-shape = example["shape"]  
-
-image.show()  # Display the image  
-print(f"Shape: {shape}")
+If you are using colab, you should update datasets to avoid errors
+```
+pip install -U datasets
 ```
-
 ## Citation
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-@misc{matthey2017dsprites,
-  title={dsprites: Disentanglement testing sprites dataset},
-  author={Matthey, Loic and Higgins, Irina and Hassabis, Demis and Lerchner, Alexander},
+```
+@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}
 }
+```