README.md

ConvNeXt

A ConvNet for the 2020s

Introduction

ConvNeXt is initially described in A ConvNet for the 2020s, which is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers. The ConvNeXt has the pyramid structure and achieve competitive performance on various vision tasks, with simplicity and efficiency.

Abstract

Show the paper's abstract

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.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.

How to use it?

Predict image

>>> import torch
>>> from mmcls.apis import get_model, inference_model
>>>
>>> model = get_model('convnext-tiny_32xb128_in1k', pretrained=True)
>>> predict = inference_model(model, 'demo/demo.JPEG')
>>> print(predict['pred_class'])
sea snake
>>> print(predict['pred_score'])
0.8915778398513794

Use the model

>>> import torch
>>> from mmcls.apis import get_model
>>>
>>> model = get_model('convnext-tiny_32xb128_in1k', pretrained=True)
>>> inputs = torch.rand(1, 3, 224, 224))
>>> # To get classification scores.
>>> out = model(inputs)
>>> print(out.shape)
torch.Size([1, 1000])
>>> # To extract features.
>>> outs = model.extract_feat(inputs)
>>> print(outs[0].shape)
torch.Size([1, 768])

Train/Test Command

Place the ImageNet dataset to the data/imagenet/ directory, or prepare datasets according to the docs.

Train:

python tools/train.py configs/convnext/convnext-tiny_32xb128_in1k.py

Test:

```shell
python tools/test.py configs/convnext/convnext-tiny_32xb128_in1k.py https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128-noema_in1k_20220222-2908964a.pth

For more configurable parameters, please refer to the API.

Results and models

Pre-trained Models

The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on the downstream tasks.

Model	Training Data	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Download
ConvNeXt-T (convnext-tiny_32xb128-noema_in1k)	ImageNet-1k	28.59	4.46	81.95	95.89	model
ConvNeXt-S (convnext-small_32xb128-noema_in1k)	ImageNet-1k	50.22	8.69	83.21	96.48	model
ConvNeXt-B (convnext-base_32xb128-noema_in1k)	ImageNet-1k	88.59	15.36	83.64	96.61	model
ConvNeXt-B* (convnext-base_3rdparty-noema_in1k)	ImageNet-1k	88.59	15.36	83.71	96.60	model
ConvNeXt-B* (convnext-base_3rdparty_in21k)	ImageNet-21k	88.59	15.36	N/A	N/A	model
ConvNeXt-L* (convnext-large_3rdparty_in21k)	ImageNet-21k	197.77	34.37	N/A	N/A	model
ConvNeXt-XL* (convnext-xlarge_3rdparty_in21k)	ImageNet-21k	350.20	60.93	N/A	N/A	model

Models with * are converted from the official repo.

ImageNet-1k

Model	Pretrain	resolution	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
ConvNeXt-T (convnext-tiny_32xb128_in1k)	From scratch	224x224	28.59	4.46	82.14	96.06	config	model | log
ConvNeXt-T* (convnext-tiny_in21k-pre_3rdparty_in1k)	ImageNet-21k	224x224	28.59	4.46	82.90	96.62	config	model
ConvNeXt-T* (convnext-tiny_in21k-pre_3rdparty_in1k-384px)	ImageNet-21k	384x384	28.59	13.13	84.11	97.14	config	model
ConvNeXt-S (convnext-small_32xb128_in1k)	From scratch	224x224	50.22	8.69	83.16	96.56	config	model | log
ConvNeXt-S* (convnext-small_in21k-pre_3rdparty_in1k)	ImageNet-21k	224x224	50.22	8.69	84.59	97.41	config	model
ConvNeXt-S* (convnext-small_in21k-pre_3rdparty_in1k-384px)	ImageNet-21k	384x384	50.22	25.58	85.75	97.88	config	model
ConvNeXt-B (convnext-base_32xb128_in1k)	From scratch	224x224	88.59	15.36	83.66	96.74	config	model | log
ConvNeXt-B* (convnext-base_3rdparty_in1k)	From scratch	224x224	88.59	15.36	83.85	96.74	config	model
ConvNeXt-B (convnext-base_3rdparty_in1k-384px)*	From scratch	384x384	88.59	45.21	85.10	97.34	config	model
ConvNeXt-B* (convnext-base_in21k-pre_3rdparty_in1k)	ImageNet 21k	224x224	88.59	15.36	85.81	97.86	config	model
ConvNeXt-B* (convnext-base_in21k-pre-3rdparty_in1k-384px)	ImageNet-21k	384x384	88.59	45.21	86.82	98.25	config	model
ConvNeXt-L* (convnext-large_3rdparty_in1k)	From scratch	224x224	197.77	34.37	84.30	96.89	config	model
ConvNeXt-L* (convnext-large_3rdparty_in1k-384px)	From scratch	384x384	197.77	101.10	85.50	97.59	config	model
ConvNeXt-L* (convnext-large_in21k-pre_3rdparty_in1k)	ImageNet 21k	224x224	197.77	34.37	86.61	98.04	config	model
ConvNeXt-L (convnext-large_in21k-pre-3rdparty_in1k-384px)*	ImageNet-21k	384x384	197.77	101.10	87.46	98.37	config	model
ConvNeXt-XL* (convnext-xlarge_in21k-pre_3rdparty_in1k)	ImageNet 21k	224x224	350.20	60.93	86.97	98.20	config	model
ConvNeXt-XL* (convnext-xlarge_in21k-pre-3rdparty_in1k-384px)	ImageNet-21k	384x384	350.20	179.20	87.76	98.55	config	model

Models with * are converted from the official repo. The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.

Citation

@Article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {arXiv preprint arXiv:2201.03545},
  year    = {2022},
}