  The "Roaring 20s" of visual recognition began with the introduction of Vision
Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art
image classification model. A vanilla ViT, on the other hand, faces
difficulties when applied to general computer vision tasks such as object
detection and semantic segmentation. It is the hierarchical Transformers (e.g.,
Swin Transformers) that reintroduced several ConvNet priors, making
Transformers practically viable as a generic vision backbone and demonstrating
remarkable performance on a wide variety of vision tasks. However, the
effectiveness of such hybrid approaches is still largely credited to the
intrinsic superiority of Transformers, rather than the inherent inductive
biases of convolutions. In this work, we reexamine the design spaces and test
the limits of what a pure ConvNet can achieve. We gradually "modernize" a
standard ResNet toward the design of a vision Transformer, and discover several
key components that contribute to the performance difference along the way. The
outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt.
Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably
with Transformers in terms of accuracy and scalability, achieving 87.8%
ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection
and ADE20K segmentation, while maintaining the simplicity and efficiency of
standard ConvNets.
