ConvNeXt-V2

ConvNeXt-V2

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Abstract

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt [52], have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE) . However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7Mparameter Atto model with 76.7% top-1 accuracy on Im-ageNet, to a 650M Huge model that achieves a state-of-theart 88.9% accuracy using only public training data.

Results and models

Method	Backbone	Pretrain	Lr schd	Augmentation	Mem (GB)	box AP	mask AP	Config	Download
Mask R-CNN	ConvNeXt-V2-B	FCMAE	3x	LSJ	22.5	52.9	46.4	config	model | log

Note:

• This is a pre-release version of ConvNeXt-V2 object detection. The official finetuning setting of ConvNeXt-V2 has not been released yet.
• ConvNeXt backbone needs to install MMPretrain first, which has abundant backbones for downstream tasks.

pip install mmpretrain

Citation

@article{Woo2023ConvNeXtV2,
  title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
  author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
  year={2023},
  journal={arXiv preprint arXiv:2301.00808},
}