Scholarlink

<p>The aim of this paper is to propose a new image data set for assessing the quality of solutions to machine learning tasks, in particular, deep neural networks. The data set is derived from X-ray images of wheat grains, in which three species, Kama, Rosa, and Canadian, are distinguished. In this paper, the structure of the data is presented in detail and ten pretrained deep neural networks are applied to identify individual wheat species. The <i>Seeds Image</i> Data Set, due to its compact nature, can compete with well-known and quite frequently used object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet, etc.). The compactness of the data set is based on a relatively small number of data instances, which shortens the rather time-consuming computing process. The proposed data set will be made available in a public repository, and the results presented will provide a starting point for other competing solutions for exploratory data analysis in the broad sense.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Seeds Image – Introduction and Baseline Experiments with the New Labeled Benchmark for Machine Learning Tasks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月4日

查看全文
  • Piotr A. Kowalski,
  • Ernest Jeczmionek,
  • Malgorzata Charytanowicz,
  • Szymon Łukasik,
  • Piotr Kulczycki

摘要

The aim of this paper is to propose a new image data set for assessing the quality of solutions to machine learning tasks, in particular, deep neural networks. The data set is derived from X-ray images of wheat grains, in which three species, Kama, Rosa, and Canadian, are distinguished. In this paper, the structure of the data is presented in detail and ten pretrained deep neural networks are applied to identify individual wheat species. The Seeds Image Data Set, due to its compact nature, can compete with well-known and quite frequently used object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet, etc.). The compactness of the data set is based on a relatively small number of data instances, which shortens the rather time-consuming computing process. The proposed data set will be made available in a public repository, and the results presented will provide a starting point for other competing solutions for exploratory data analysis in the broad sense.