Bag of Tricks for Image Classification with Convolutional Neural Networks

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

1. Introduction

Since the introduction of AlexNet [15] in 2012, deep convolutional neural networks have become the dominating approach for image classification. Various new architectures have been proposed since then, including VGG [24], NiN [16], Inception [1], ResNet [9], DenseNet [13], and NASNet [35]. At the same time, we have seen a steady trend of model accuracy improvement. For example, the top-1 validation accuracy on ImageNet [23] has been raised from 62.5% (AlexNet) to 82.7% (NASNet-A).