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

01/14/2021
by   Daniel Ma, et al.
12

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.

READ FULL TEXT

page 4

page 6

page 7

page 9

research
05/02/2023

Multimodal Neural Databases

The rise in loosely-structured data available through text, images, and ...
research
02/20/2020

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset

From classifying handwritten digits to generating strings of text, the d...
research
07/11/2018

Morse Code Datasets for Machine Learning

We present an algorithm to generate synthetic datasets of tunable diffic...
research
05/19/2022

Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms

We introduce the Oracle-MNIST dataset, comprising of 28×28 grayscale ima...
research
11/05/2018

Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

Classification tasks are usually analysed and improved through new model...
research
01/02/2023

EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies

Vocal Bursts – short, non-speech vocalizations that convey emotions, suc...
research
11/25/2019

Bridging the Gap between Semantics and Multimedia Processing

In this paper, we give an overview of the semantic gap problem in multim...

Please sign up or login with your details

Forgot password? Click here to reset