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Convolutional neural networks (CNNs) are commonly trained using a fixedspatial image size predetermined for a given model. Although trained on imagesof aspecific size, it is well established that CNNs can be used to evaluate awide range of image sizes at test time, by adjusting the size of intermediatefeature maps. In this work, we describe and evaluate a novel mixed-sizetraining regime that mixes several image sizes at training time. We demonstratethat models trained using our method are more resilient to image size changesand generalize well even on small images. This allows faster inference by usingsmaller images attest time. For instance, we receive a 76.43% top-1 accuracyusing ResNet50 with an image size of 160, which matches the accuracy of thebaseline model with 2x fewer computations. Furthermore, for a given image sizeused at test time, we show this method can be exploited either to acceleratetraining or the final test accuracy. For example, we are able to reach a 79.27%accuracy with a model evaluated at a 288 spatial size for a relativeimprovement of 14% over the baseline.

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