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ImageNet serves as the primary dataset for evaluating the quality ofcomputer-vision models. The common practice today is training each architecturewith a tailor-made scheme, designed and tuned by an expert. In this paper, wepresent a unified scheme for training any backbone on ImageNet. The scheme,named USI (Unified Scheme for ImageNet), is based on knowledge distillation andmodern tricks. It requires no adjustments or hyper-parameters tuning betweendifferent models, and is efficient in terms of training times. We test USI on awide variety of architectures, including CNNs, Transformers, Mobile-orientedand MLP-only. On all models tested, USI outperforms previous state-of-the-artresults. Hence, we are able to transform training on ImageNet from anexpert-oriented task to an automatic seamless routine. Since USI accepts anybackbone and trains it to top results, it also enables to perform methodicalcomparisons, and identify the most efficient backbones along the speed-accuracyPareto curve. Implementation is availableat:https://github.com/Alibaba-MIIL/Solving_ImageNet

Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results