Abstract
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&\#39;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.
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