Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency

Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps. In this work, we describe and evaluate a novel mixed-size training regime that mixes several image sizes at training time. We demonstrate that models trained using our method are more resilient to image size changes and generalize well even on small images. This allows faster inference by using smaller images at test time. For instance, we receive a 76 .43% Furthermore, for a given image size used at test time, we show this method can be exploited either to accelerate training or the final test accuracy. For example, we are able to reach a 79 .27% Figure 1: Test accuracy per image size, models trained on specific sizes (ResNet50, ImageNet). Convolutional neural networks are successfully used to solve various tasks across multiple domains such as visual (Krizhevsky et al., 2012; Ren et al., 2015), audio (van den Oord et al., 2016), language (Gehring et al., 2017) and speech (Abdel-Hamid et al., 2014).