The HASYv2 dataset
By Martin Thoma. 2017
37 citations recorded by the source corpus.
This paper describes the HASYv2 dataset. HASY is a publicly available, free of charge dataset of single symbols similar to MNIST. It contains 168233 instances of 369 classes. HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge.
Subfields: Radiology, Nuclear Medicine and Imaging
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In Radiology, Nuclear Medicine and Imaging
- Joost van de Weijer
- Yang Yu
- Sergey Demyanov
- ZongYuan Ge
- Qiu-Ling Xu
- Zhi-Hua Zhou
- Wang-Zhou Dai
- Bartłomiej Twardowski
Citations in the corpus
- On Class Orderings for Incremental Learning (2020, methodology): “The same approach has been previously used for CM ordering for better visualization of large matrices in (Thoma, 2017a) and for HASYv2 dataset (Thoma, 2017b).”
- Generative OpenMax for Multi-Class Open Set Classification (2017, methodology): “We perform a thorough comparison of G-OpenMax to OpenMax and its variants on two datasets MNIST [14] and HASYv2 [29].”
- Tunneling Neural Perception and Logic Reasoning through Abductive Learning (2018, methodology): “The Digital Binary Additive (DBA) equations were created with images from benchmark handwritten character datasets [29, 46], while the Random Binary Additive (RBA) equations were constructed from randomly selected character sets and shared isomorphic structure with the equations in the DBA tasks.”