README.md

---
license: other
---
# Dataset Card for SmallNORB

## Dataset Description

The **SmallNORB dataset** is a **real-world stereo image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. It was introduced by **LeCun et al. (2004)** for evaluating **generic object recognition** with **invariance to pose and lighting**.

Unlike synthetic datasets such as **dSprites** or **MPI3D**, which are generated as a **complete Cartesian product of factors** (i.e. every possible combination is present), SmallNORB consists of **real photographs** of physical toy objects under controlled variations, but **not every combination of factors is present** — for example, object instances are sampled randomly and the views (azimuth, elevation, lighting) do not form an exact grid.

Each sample contains **two views**:
- **Left image** (96x96 grayscale)
- **Right image** (96x96 grayscale)

Each image pair is associated with **4 known factors of variation** and **instance index**:
- **category** (object type)
- **instance** (specific object instance)
- **elevation** (camera tilt angle)
- **azimuth** (camera rotation angle)
- **lighting** (lighting condition)

The dataset allows researchers to evaluate **representation learning on real-world 3D objects**, under complex lighting and pose variations. SmallNORB provides an **official train/test split**. Typically, **instance** is not considered as a factor.
![Dataset visualization](https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/animation0.gif)

## Dataset Source
- **Homepage**: [https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/)
- **License**: other. Small NORB is public domain, for research use.
- **Paper**: Yann LeCun et al. _Learning methods for generic object recognition with invariance to pose and lighting_. CVPR 2004.

## Dataset Structure
|Factors|Possible Classes (Indices)|Values|
|---|---|---|
|category|0,...,4| airplane=0, car=1, truck=2, human=3, animal=4 |
|instance|0,...,9| specific instance of object |
|elevation|0,...,8| 9 elevation angles |
|azimuth|0,...,17| azimuth originally 0,2,...,34 → scaled to 0-17 |
|lighting|0,...,5| 6 lighting conditions |

**Note:** The dataset is not a complete Cartesian product — **instances and views are sampled** in the original design. Each sample contains a **left image** and a **right image**, both corresponding to the same factors.

## 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 train set
train_ds = load_dataset("randall-lab/small-norb", split="train", trust_remote_code=True)

# Load test set
# test_ds = load_dataset("randall-lab/small-norb", split="test", trust_remote_code=True)

# Access a sample
example = train_ds[0]
left_image = example["left_image"]
right_image = example["right_image"]
label = example["label"]  # [category, elevation, azimuth, lighting]

# Label breakdown
category = example["category"]      # 0-4
instance = example["instance"]      # 0-9
elevation = example["elevation"]    # 0-8
azimuth = example["azimuth"]        # 0-17
lighting = example["lighting"]      # 0-5

# Visualize
left_image.show()
right_image.show()

print(f"Label (factors): {label}")
```
If you are using colab, you should update datasets to avoid errors
```
pip install -U datasets
```
## Citation
```
@inproceedings{lecun2004learning,
  title={Learning methods for generic object recognition with invariance to pose and lighting},
  author={LeCun, Yann and Huang, Fu Jie and Bottou, Leon},
  booktitle={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.},
  volume={2},
  pages={II--104},
  year={2004},
  organization={IEEE}
}
```