The success stories from deep learning models increase every day spanning different tasks from image classification to natural language understanding. With the increasing popularity of these models, scientists spend more and more time finding the optimal parameters and best model architectures for their tasks. In this paper, we focus on the ingredient that feeds these machines: the data. We hypothesize that the data ordering affects how well a model performs. To that end, we conduct experiments on an image classification task using ImageNet dataset and show that some data orderings are better than others in terms of obtaining higher classification accuracies. Experimental results show that independent of model architecture, learning rate and batch size, ordering of the data significantly affects the outcome. We show these findings using different metrics: NDCG, accuracy @ 1 and accuracy @ 5. Our goal here is to show that not only parameters and model architectures but also the data ordering has a say in obtaining better results.

中文翻译：

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图像分类中数据排序的影响

深度学习模型的成功案例每天都在增加，涵盖从图像分类到自然语言理解的不同任务。随着这些模型的日益普及，科学家们花费越来越多的时间为他们的任务寻找最佳参数和最佳模型架构。在本文中，我们关注为这些机器提供营养的成分：数据。我们假设数据排序会影响模型的性能。为此，我们使用 ImageNet 数据集对图像分类任务进行了实验，并表明某些数据排序在获得更高分类精度方面比其他数据排序更好。实验结果表明，独立于模型架构、学习率和批量大小，数据的排序显着影响结果。