---
license: unknown
size_categories: 10K<n<100K
task_categories:
- image-classification
paperswithcode_id: lsun
pretty_name: LSUN (r)
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 27566116.0
num_examples: 10000
download_size: 0
dataset_size: 27566116.0
---
# Dataset Card for LSUN (r) for OOD Detection
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Original Dataset Authors**: Limin Wang, Sheng Guo, Weilin Huang, Yuanjun Xiong, Yu Qiao
- **OOD Split Authors:** Shiyu Liang, Yixuan Li, R. Srikant
- **Shared by:** Eduardo Dadalto
- **License:** unknown
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Original Dataset Paper:** http://arxiv.org/abs/1610.01119v2
- **First OOD Application Paper:** http://arxiv.org/abs/1706.02690v5
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset is not annotated.
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The goal in curating and sharing this dataset to the HuggingFace Hub is to accelerate research and promote reproducibility in generalized Out-of-Distribution (OOD) detection.
Check the python library [detectors](https://github.com/edadaltocg/detectors) if you are interested in OOD detection.
### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
Please check original paper for details on the dataset.
### Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Please check original paper for details on the dataset.
## 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:**
```bibtex
@software{detectors2023,
author = {Eduardo Dadalto},
title = {Detectors: a Python Library for Generalized Out-Of-Distribution Detection},
url = {https://github.com/edadaltocg/detectors},
doi = {https://doi.org/10.5281/zenodo.7883596},
month = {5},
year = {2023}
}
@article{1706.02690v5,
author = {Shiyu Liang and Yixuan Li and R. Srikant},
title = {Enhancing The Reliability of Out-of-distribution Image Detection in
Neural Networks},
year = {2017},
month = {6},
note = {ICLR 2018},
archiveprefix = {arXiv},
url = {http://arxiv.org/abs/1706.02690v5}
}
@article{1610.01119v2,
author = {Limin Wang and Sheng Guo and Weilin Huang and Yuanjun Xiong and Yu Qiao},
title = {Knowledge Guided Disambiguation for Large-Scale Scene Classification
with Multi-Resolution CNNs},
year = {2016},
month = {10},
note = {To appear in IEEE Transactions on Image Processing. Code and models
are available at https://github.com/wanglimin/MRCNN-Scene-Recognition},
archiveprefix = {arXiv},
url = {http://arxiv.org/abs/1610.01119v2}
}
```
## Dataset Card Authors
Eduardo Dadalto
## Dataset Card Contact
https://huggingface.co/edadaltocg
The LSUN dataset is a dataset for out-of-distribution (OOD) detection in image classification tasks. The dataset includes images and is intended for use in benchmarks for OOD detection. The original dataset and OOD split authors are mentioned, along with the datasets license status (unknown). The dataset is not annotated and is curated to promote research and reproducibility in OOD detection. The README also includes citations for related papers and a link to a Python library for OOD detection.
