Deep learning techniques are currently the state of the art approach to deal with image classification problems. Nevertheless, non-expert users might find challenging the use of these techniques due to several reasons, including the lack of enough images, the necessity of trying different models and conducting a thorough comparison of the results obtained with them, and the technical difficulties of employing different libraries, tools and special purpose hardware like GPUs. In this work, we present FrImCla, an open-source and free tool that simplifies the construction of robust models for image classification from a dataset of images, and only using the computer CPU. Given a dataset of annotated images, FrImCla automatically constructs a classification model (both for single-label and multi-label classification problems) by trying several feature extractors (based both on transfer learning and traditional computer vision methods) and machine learning algorithms, and selecting the best combination after a thorough statistical analysis. Thus, this tool can be employed by non-expert users to create accurate models from small datasets of images without requiring any special purpose hardware. In addition, in this paper, we show that FrImCla can be employed to construct accurate models that are close, or even better, to the state-of-the-art models.

中文翻译：

---

FrImCla：使用传统和迁移学习技术的图像分类框架

深度学习技术是目前处理图像分类问题的最先进方法。然而，由于以下几个原因，非专家用户可能会发现这些技术的使用具有挑战性，包括缺乏足够的图像、必须尝试不同的模型并对所获得的结果进行彻底的比较，以及使用这些技术的技术困难不同的库、工具和专用硬件，如 GPU。在这项工作中，我们展示了 FrImCla，这是一种开源免费工具，可简化从图像数据集构建用于图像分类的稳健模型的过程，并且仅使用计算机 CPU。给定一个带注释的图像数据集，FrImCla 通过尝试多种特征提取器（基于迁移学习和传统计算机视觉方法）和机器学习算法，并在彻底统计后选择最佳组合，自动构建分类模型（针对单标签和多标签分类问题）分析。因此，非专家用户可以使用该工具从小型图像数据集创建准确的模型，而无需任何专用硬件。此外，在本文中，我们表明 FrImCla 可用于构建与最先进模型接近甚至更好的准确模型。非专家用户可以使用此工具从小型图像数据集创建准确的模型，而无需任何特殊用途的硬件。此外，在本文中，我们表明 FrImCla 可用于构建与最先进模型接近甚至更好的准确模型。非专家用户可以使用此工具从小型图像数据集创建准确的模型，而无需任何特殊用途的硬件。此外，在本文中，我们表明 FrImCla 可用于构建与最先进模型接近甚至更好的准确模型。