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Vision Transformers (ViT) have achieved remarkable success in large-scaleimage recognition. They split every 2D image into a fixed number of patches,each of which is treated as a token. Generally, representing an image with moretokens would lead to higher prediction accuracy, while it also results indrastically increased computational cost. To achieve a decent trade-off betweenaccuracy and speed, the number of tokens is empirically set to 16x16 or 14x14.In this paper, we argue that every image has its own characteristics, andideally the token number should be conditioned on each individual input. Infact, we have observed that there exist a considerable number of "easy" imageswhich can be accurately predicted with a mere number of 4x4 tokens, while onlya small fraction of "hard" ones need a finer representation. Inspired by thisphenomenon, we propose a Dynamic Transformer to automatically configure aproper number of tokens for each input image. This is achieved by cascadingmultiple Transformers with increasing numbers of tokens, which are sequentiallyactivated in an adaptive fashion at test time, i.e., the inference isterminated once a sufficiently confident prediction is produced. We furtherdesign efficient feature reuse and relationship reuse mechanisms acrossdifferent components of the Dynamic Transformer to reduce redundantcomputations. Extensive empirical results on ImageNet, CIFAR-10, and CIFAR-100demonstrate that our method significantly outperforms the competitive baselinesin terms of both theoretical computational efficiency and practical inferencespeed. Code and pre-trained models (based on PyTorch and MindSpore) areavailable at https://github.com/blackfeather-wang/Dynamic-Vision-Transformerand https://github.com/blackfeather-wang/Dynamic-Vision-Transformer-MindSpore.

Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition