# OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation

arXiv: https://arxiv.org/abs/2101.05470 · HTML: https://mlsanity.com/paper/2101.05470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Coloring connections with counting mountain-valley assignments](https://mlsanity.com/paper/1601.02727/) (2016)
- [Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)](https://mlsanity.com/paper/1712.09915/) (2017)

## Built on by (most cited first, 1 of 1)

- [Batch Model Consolidation: A Multi-Task Model Consolidation Framework](https://mlsanity.com/paper/2305.16484/) (2023)

## Abstract (arXiv)

Origami is becoming more and more relevant to research. However, there is no public dataset yet available and there hasn't been any research on this topic in machine learning. We constructed an origami dataset using images from the multimedia commons and other databases. It consists of two subsets: one for classification of origami images and the other for difficulty estimation. We obtained 16000 images for classification (half origami, half other objects) and 1509 for difficulty estimation with 3 different categories (easy: 764, intermediate: 427, complex: 318). The data can be downloaded at: https://github.com/multimedia-berkeley/OriSet. Finally, we provide machine learning baselines.