# [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) Official PyTorch implementation of **ConvNeXt**, from the following paper: [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545). CVPR 2022.\ [Zhuang Liu](https://liuzhuang13.github.io), [Hanzi Mao](https://hanzimao.me/), [Chao-Yuan Wu](https://chaoyuan.org/), [Christoph Feichtenhofer](https://feichtenhofer.github.io/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/) and [Saining Xie](https://sainingxie.com)\ Facebook AI Research, UC Berkeley\ [[`arXiv`](https://arxiv.org/abs/2201.03545)][[`video`](https://www.youtube.com/watch?v=QzCjXqFnWPE)] ---

We propose **ConvNeXt**, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design. ## Catalog - [x] ImageNet-1K Training Code - [x] ImageNet-22K Pre-training Code - [x] ImageNet-1K Fine-tuning Code - [x] Downstream Transfer (Detection, Segmentation) Code - [x] Image Classification [\[Colab\]](https://colab.research.google.com/drive/1CBYTIZ4tBMsVL5cqu9N_-Q3TBprqsfEO?usp=sharing) and Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/convnext) - [x] Fine-tune on CIFAR with Weights & Biases logging [\[Colab\]](https://colab.research.google.com/drive/1ijAxGthE9RENJJQRO17v9A7PTd1Tei9F?usp=sharing) ## Results and Pre-trained Models ### ImageNet-1K trained models | name | resolution |acc@1 | #params | FLOPs | model | |:---:|:---:|:---:|:---:| :---:|:---:| | ConvNeXt-T | 224x224 | 82.1 | 28M | 4.5G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth) | | ConvNeXt-S | 224x224 | 83.1 | 50M | 8.7G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth) | | ConvNeXt-B | 224x224 | 83.8 | 89M | 15.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth) | | ConvNeXt-B | 384x384 | 85.1 | 89M | 45.0G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth) | | ConvNeXt-L | 224x224 | 84.3 | 198M | 34.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth) | | ConvNeXt-L | 384x384 | 85.5 | 198M | 101.0G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth) | ### ImageNet-22K trained models | name | resolution |acc@1 | #params | FLOPs | 22k model | 1k model | |:---:|:---:|:---:|:---:| :---:| :---:|:---:| | ConvNeXt-T | 224x224 | 82.9 | 29M | 4.5G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth) | ConvNeXt-T | 384x384 | 84.1 | 29M | 13.1G | - | [model](https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth) | ConvNeXt-S | 224x224 | 84.6 | 50M | 8.7G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth) | ConvNeXt-S | 384x384 | 85.8 | 50M | 25.5G | - | [model](https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth) | ConvNeXt-B | 224x224 | 85.8 | 89M | 15.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth) | ConvNeXt-B | 384x384 | 86.8 | 89M | 47.0G | - | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth) | ConvNeXt-L | 224x224 | 86.6 | 198M | 34.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth) | ConvNeXt-L | 384x384 | 87.5 | 198M | 101.0G | - | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth) | ConvNeXt-XL | 224x224 | 87.0 | 350M | 60.9G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth) | ConvNeXt-XL | 384x384 | 87.8 | 350M | 179.0G | - | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth) ### ImageNet-1K trained models (isotropic) | name | resolution |acc@1 | #params | FLOPs | model | |:---:|:---:|:---:|:---:| :---:|:---:| | ConvNeXt-S | 224x224 | 78.7 | 22M | 4.3G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_small_1k_224_ema.pth) | | ConvNeXt-B | 224x224 | 82.0 | 87M | 16.9G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_base_1k_224_ema.pth) | | ConvNeXt-L | 224x224 | 82.6 | 306M | 59.7G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_large_1k_224_ema.pth) | ## Installation Please check [INSTALL.md](INSTALL.md) for installation instructions. ## Evaluation We give an example evaluation command for a ImageNet-22K pre-trained, then ImageNet-1K fine-tuned ConvNeXt-B: Single-GPU ``` python main.py --model convnext_base --eval true \ --resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \ --input_size 224 --drop_path 0.2 \ --data_path /path/to/imagenet-1k ``` Multi-GPU ``` python -m torch.distributed.launch --nproc_per_node=8 main.py \ --model convnext_base --eval true \ --resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \ --input_size 224 --drop_path 0.2 \ --data_path /path/to/imagenet-1k ``` This should give ``` * Acc@1 85.820 Acc@5 97.868 loss 0.563 ``` - For evaluating other model variants, change `--model`, `--resume`, `--input_size` accordingly. You can get the url to pre-trained models from the tables above. - 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](TRAINING.md) or our paper for the values used for different models. ## Training See [TRAINING.md](TRAINING.md) for training and fine-tuning instructions. ## Acknowledgement This repository is built using the [timm](https://github.com/rwightman/pytorch-image-models) library, [DeiT](https://github.com/facebookresearch/deit) and [BEiT](https://github.com/microsoft/unilm/tree/master/beit) repositories. ## License This project is released under the MIT license. Please see the [LICENSE](LICENSE) file for more information. ## Citation If you find this repository helpful, please consider citing: ``` @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 = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ```