OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
This addresses the problem of dataset scarcity for researchers in machine learning and origami, though it is incremental as it primarily introduces new data.
The authors tackled the lack of public datasets for origami research by constructing OrigamiSet1.0, which includes 16,000 images for classification and 1,509 images for difficulty estimation with three categories, and provided machine learning baselines.
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.