Classification problems are common in Computer Vision. Despite this, there is no dedicated work for the classification of beer bottles. As part of the challenge of the master course Deep Learning, a dataset of 5207 beer bottle images and brand labels was created. An image contains exactly one beer bottle. In this paper we present a deep learning model which classifies pictures of beer bottles in a two step approach. As the first step, a Faster-R-CNN detects image sections relevant for classification independently of the brand. In the second step, the relevant image sections are classified by a ResNet-18. The image section with the highest confidence is returned as class label. We propose a model, with which we surpass the classic one step transfer learning approach and reached an accuracy of 99.86% during the challenge on the final test dataset. We were able to achieve 100% accuracy after the challenge ended
翻译:计算机愿景中常见的分类问题。 尽管如此, 啤酒瓶的分类工作没有专门的专门工作。 作为深学习硕士课程挑战的一部分, 创建了一个由5 207个啤酒瓶图像和品牌标签组成的数据集。 一个图像完全包含一个啤酒瓶。 在本文中, 我们提出了一个深层次的学习模型, 将啤酒瓶的图片分解为两步方法。 作为第一步, 快速R- CNN 检测了与品牌无关的分类相关的图像部分。 第二步, 相关的图像部分由ResNet-18 分类。 具有最高可信度的图像部分被恢复为类标签。 我们提出了一个模型, 超过了经典的一步转移学习方法, 在最终测试数据集的挑战中达到了99.86%的准确度。 在挑战结束后, 我们实现了100%的准确度。