fhfeishi/ConvNeXt-V2

Code release for ConvNeXt V2 model

ConvNeXt V2
_{Official PyTorch Implementation}

This repo contains the PyTorch version of 8 model definitions (Atto, Femto, Pico, Nano, Tiny, Base, Large, Huge), pre-training/fine-tuning code and pre-trained weights (converted from JAX weights trained on TPU) for our ConvNeXt V2 paper.

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie

KAIST, Meta AI and New York University

We propose a fully convolutional masked autoencoder framework (FCMAE) 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. We also provide pre-trained ConvNeXt V2 models of various sizes.

Results and Pre-trained Models

ImageNet-1K FCMAE pre-trained weights (self-supervised)

name	resolution	#params	model
ConvNeXt V2-A	224x224	3.7M	model
ConvNeXt V2-F	224x224	5.2M	model
ConvNeXt V2-P	224x224	9.1M	model
ConvNeXt V2-N	224x224	15.6M	model
ConvNeXt V2-T	224x224	28.6M	model
ConvNeXt V2-B	224x224	89M	model
ConvNeXt V2-L	224x224	198M	model
ConvNeXt V2-H	224x224	660M	model

ImageNet-1K fine-tuned models

name	resolution	acc@1	#params	FLOPs	model
ConvNeXt V2-A	224x224	76.7	3.7M	0.55G	model
ConvNeXt V2-F	224x224	78.5	5.2M	0.78G	model
ConvNeXt V2-P	224x224	80.3	9.1M	1.37G	model
ConvNeXt V2-N	224x224	81.9	15.6M	2.45G	model
ConvNeXt V2-T	224x224	83.0	28.6M	4.47G	model
ConvNeXt V2-B	224x224	84.9	89M	15.4G	model
ConvNeXt V2-L	224x224	85.8	198M	34.4G	model
ConvNeXt V2-H	224x224	86.3	660M	115G	model

ImageNet-22K fine-tuned models

name	resolution	acc@1	#params	FLOPs	model
ConvNeXt V2-N	224x224	82.1	15.6M	2.45G	model
ConvNeXt V2-N	384x384	83.4	15.6M	7.21G	model
ConvNeXt V2-T	224x224	83.9	28.6M	4.47G	model
ConvNeXt V2-T	384x384	85.1	28.6M	13.1G	model
ConvNeXt V2-B	224x224	86.8	89M	15.4G	model
ConvNeXt V2-B	384x384	87.7	89M	45.2G	model
ConvNeXt V2-L	224x224	87.3	198M	34.4G	model
ConvNeXt V2-L	384x384	88.2	198M	101.1G	model
ConvNeXt V2-H	384x384	88.7	660M	337.9G	model
ConvNeXt V2-H	512x512	88.9	660M	600.8G	model

Installation

Please check INSTALL.md for installation instructions.

Evaluation

We provide example evaluation commands for ConvNeXt V2-Base:

Single-GPU

python main_finetune.py \
--model convnextv2_base \
--eval true \
--resume /path/to/checkpoint \
--input_size 224 \
--data_path /path/to/imagenet-1k \

Multi-GPU

python -m torch.distributed.launch --nproc_per_node=8 main_finetune.py \
--model convnextv2_base \
--eval true \
--resume /path/to/checkpoint \
--input_size 224 \
--data_path /path/to/imagenet-1k \

• For evaluating other model variants, change --model, --resume, --input_size accordingly. URLs for the pre-trained models can be found from the result tables.
• Setting model-specific --drop_path is not strictly required in evaluation, as the DropPath module in timm behaves the same during evaluation; but it is required in training. See TRAINING.md or our paper (appendix) for the values used for different models.

Training

See TRAINING.md for pre-training and fine-tuning instructions.

Acknowledgement

This repository borrows from timm, ConvNeXt and MAE.

We thank Ross Wightman for the initial design of the small-compute ConvNeXt model variants and the associated training recipe. We also appreciate the helpful discussions and feedback provided by Kaiming He.

License

The majority of ConvNeXt-V2 is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Minkowski Engine is licensed under the MIT license. Please see the LICENSE file for more information.

Citation

If you find this repository helpful, please consider citing:

@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},
}