  Due to the complex attention mechanisms and model design, most existing
vision Transformers (ViTs) can not perform as efficiently as convolutional
neural networks (CNNs) in realistic industrial deployment scenarios, e.g.
TensorRT and CoreML. This poses a distinct challenge: Can a visual neural
network be designed to infer as fast as CNNs and perform as powerful as ViTs?
Recent works have tried to design CNN-Transformer hybrid architectures to
address this issue, yet the overall performance of these works is far away from
satisfactory. To end these, we propose a next generation vision Transformer for
efficient deployment in realistic industrial scenarios, namely Next-ViT, which
dominates both CNNs and ViTs from the perspective of latency/accuracy
trade-off. In this work, the Next Convolution Block (NCB) and Next Transformer
Block (NTB) are respectively developed to capture local and global information
with deployment-friendly mechanisms. Then, Next Hybrid Strategy (NHS) is
designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts
performance in various downstream tasks. Extensive experiments show that
Next-ViT significantly outperforms existing CNNs, ViTs and CNN-Transformer
hybrid architectures with respect to the latency/accuracy trade-off across
various vision tasks. On TensorRT, Next-ViT surpasses ResNet by 5.5 mAP (from
40.4 to 45.9) on COCO detection and 7.7% mIoU (from 38.8% to 46.5%) on ADE20K
segmentation under similar latency. Meanwhile, it achieves comparable
performance with CSWin, while the inference speed is accelerated by 3.6x. On
CoreML, Next-ViT surpasses EfficientFormer by 4.6 mAP (from 42.6 to 47.2) on
COCO detection and 3.5% mIoU (from 45.1% to 48.6%) on ADE20K segmentation under
similar latency. Our code and models are made public at:
https://github.com/bytedance/Next-ViT
