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The Training Recipe
Our goal was to use the newly introduced primitives of TorchVision to derive a new strong training recipe which achieves state-of-the-art results for the vanilla ResNet50 architecture when trained from scratch on ImageNet with no additional external data. Though by using architecture specific tricks [2] one could further improve the accuracy, we’ve decided not to include them so that the recipe can be used in other architectures. Our recipe heavily focuses on simplicity and builds upon work by FAIR [3], [4], [5], [6], [7]. Our findings align with the parallel study of Wightman et al. [7], who also report major accuracy improvements by focusing on the training recipes.