论文标题
零售产品图像分类的技巧袋
Bag of Tricks for Retail Product Image Classification
论文作者
论文摘要
零售产品图像分类是一个重要的计算机视觉和机器学习问题，用于构建现实世界系统，例如自我检查商店和自动零售执行评估。在这项工作中，我们提出了各种技巧，以提高对不同类型的零售产品图像分类数据集的深度学习模型的准确性。这些技巧使我们能够通过大幅度提高微调转向器以进行零售产品图像分类的准确性。作为最突出的技巧，我们引入了一个新的神经网络层，称为Local-Concepts-Acumulation（LCA）层，该层可在多个数据集中提供一致的收益。我们发现提高零售产品识别准确性的另外两个技巧是使用Instagram预测的Convnet，并将最大熵作为分类的辅助损失。
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product image classification datasets. These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin. As the most prominent trick, we introduce a new neural network layer called Local-Concepts-Accumulation (LCA) layer which gives consistent gains across multiple datasets. Two other tricks we find to increase accuracy on retail product identification are using an instagram-pretrained Convnet and using Maximum Entropy as an auxiliary loss for classification.
