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

Vision Transformers (ViT) have achieved remarkable success in large-scale image 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 more tokens would lead to higher prediction accuracy, while it also results in drastically increased computational cost. To achieve a decent trade-off between accuracy 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, and ideally the token number should be conditioned on each individual input. In fact, we have observed that there exist a considerable number of "easy" images which can be accurately predicted with a mere number of 4x4 tokens, while only a small fraction of "hard" ones need a finer representation. Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image. This is achieved by cascading multiple Transformers with increasing numbers of tokens, which are sequentially activated in an adaptive fashion at test time, i.e., the inference is terminated once a sufficiently confident prediction is produced. We further design efficient feature reuse and relationship reuse mechanisms across different components of the Dynamic Transformer to reduce redundant computations. Extensive empirical results on ImageNet, CIFAR-10, and CIFAR-100 demonstrate that our method significantly outperforms the competitive baselines in terms of both theoretical computational efficiency and practical inference speed. Code and pre-trained models (based on PyTorch and MindSpore) are available at https://github.com/blackfeather-wang/Dynamic-Vision-Transformer and https://github.com/blackfeather-wang/Dynamic-Vision-Transformer-MindSpore.

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Results from the Paper


Ranked #30 on Image Classification on CIFAR-100 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Image Classification CIFAR-10 DVT (T2T-ViT-24) Percentage correct 98.53 # 33
Image Classification CIFAR-100 DVT (T2T-ViT-24) Percentage correct 89.63 # 30
Image Classification ImageNet DVT (T2T-ViT-7) Top 1 Accuracy 78.48% # 603
GFLOPs 0.6 # 60
Image Classification ImageNet DVT (T2T-ViT-10) Top 1 Accuracy 79.74% # 527
GFLOPs 0.7 # 77
Image Classification ImageNet DVT (T2T-ViT-12) Top 1 Accuracy 80.43% # 494
Hardware Burden None # 1
Operations per network pass None # 1
GFLOPs 1.7 # 125

Methods