We attempted this challenge as part of our Deep Learning and Neural Networks Major Project.
The notebook can be found at /code/10khrs-ai-ml-dl/projects/kits19/report.html, which contains implementation details of U-Net, SamNet, VGG-Net and nnU-Net.
The corresponding report, containing a literature review along with other scientific details is embedded below:
This page is for finding a classifier on the KMNIST dataset. This dataset is more challenging than the original MNIST dataset that I have previously solved.
The details of the dataset can be found in the associated paper.
In short, since the reformation of the Japanese education in 1868, there became a standardisation of the kanji characters, and in the present day, most Japanese people cannot read the texts from 150 years ago.
The MNIST dataset (Modified National Institute of Standards and Technology) has been very influential in machine learning and computer vision. It is an easy and popular dataset that has been used since it's inception in 1998 as a benchmark for Machine Learning Models. Historically it has enhanced the evolution of OCR (Optical Character Recognition) and assisted in the emergence of neural networks.
The story of MNIST begins with the NIST dataset, developed by the United States National Institute of Standards and Technology in the late 1980s. The original dataset was created to facilitate research in OCR systems, which were becoming increasingly relevant for automating tasks like check processing and mail sorting. NIST's dataset consisted of tens of thousands of handwritten digits collected from various sources, including Census Bureau employees and high school students.