---
license: cc-by-4.0
task_categories:
- image-classification
tags:
- heritage
- nepal
- cultural-heritage
- computer-vision
- image-classification
pretty_name: HeritageNet - Nepali Heritage Sites
size_categories:
- 1K<n<10K
---

# HeritageNet: A Dataset of Nepali Heritage Sites

A collection of photographs of **26 Nepali heritage sites** — temples, stupas, and
monasteries — captured by the HeritageNet team for image-classification research.

Nepali cultural heritage is severely underrepresented in existing computer-vision
datasets. Global models recognise landmarks like the Eiffel Tower or Taj Mahal, yet have
effectively never "seen" Nepal's local heritage. This dataset is a small step toward
closing that gap — putting Nepali heritage on the digital map and enabling on-device
recognition of these sites.

## Dataset summary

- **~1,044 images** across **26 classes** (one folder per heritage site).
- Photographed by the HeritageNet team.
- Intended for **image classification** (identify which heritage site a photo shows).
- Released under **CC BY 4.0** — free to use, share, and adapt with attribution.

## Structure

Images are organised in the standard image-folder format — one subfolder per class:

```
Ashok_stupa_Chaitya/
Badrinath_Temple/
Balkumari_Temple/
... (26 site folders) ...
```

Each subfolder contains JPEG photographs of that site. The folder name is the class label.
This layout loads directly with common tools (e.g. torchvision `ImageFolder`, or the
Hugging Face `datasets` `imagefolder` loader).

## How it was collected

The images were photographed directly by the team at the heritage sites, using consumer
phone cameras, as part of the HeritageNet project. Capturing our own photographs — rather
than scraping the web — is central to the project: it produces a dataset we can openly and
lawfully release, and it reflects the "citizen science" vision of ordinary people helping
to document heritage with the phones they already carry.

## Intended uses

- Training and evaluating heritage-site image classifiers.
- Educational and research use in computer vision and cultural-heritage documentation.
- A base others can extend with additional sites and images.

A companion lightweight classifier (MobileNetV3-small, transfer learning) trained on this
data reaches ~86.6% test accuracy / ~85.8% macro-F1 across the 26 classes, and runs
on-device in the browser.

## Known limitations

- **Class imbalance / thin classes.** Some sites have relatively few images. In particular,
  the **Surya_Binayak_Temple** class currently contains only 22 team-photographed images.
  Surya Binayak will be expanded with additional team photographs in a future version.
- **Placeholder class names.** A few classes retain provisional names (e.g. `Heritage_1`,
  `Heritage_2`, `Heritage_3`) and will be renamed to their proper site names in a future
  release.
- **Regional scope.** The dataset covers 26 specific sites and is not exhaustive of Nepali
  heritage. It is intended to grow over time.
- **Photographic variation.** Images vary in lighting, angle, and background; some may include
  incidental passers-by.

## Roadmap

This is an initial release. Planned future work: expand the number of sites, add more images
per class (especially the thin ones), rename placeholder classes, and open the dataset to
community photo contributions.

## License

This dataset is licensed under the **Creative Commons Attribution 4.0 International License
(CC BY 4.0)**. You are free to share and adapt the material for any purpose, even
commercially, provided you give appropriate credit.
License: https://creativecommons.org/licenses/by/4.0/

## Citation / Attribution

If you use this dataset, please credit:

> **HeritageNet: A Dataset of Nepali Heritage Sites** (2026).
> Collected by Ashutosh Chapagai, Rasim Mahato, Diva Awasthi, Kriti Puri, Suyog Adhikari,
> and Pratik Sharma. Mentors: Sijan Shrestha and Hari Om Sah. Licensed under CC BY 4.0.

## Acknowledgements

Created as a research project to help document and preserve Nepali cultural heritage.
Mentored by Sijan Shrestha and Hari Om Sah.