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*This model was released on 2022-01-10 and added to Hugging Face Transformers on 2022-02-07.*

# ConvNeXT

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.

The abstract from the paper is the following:

*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.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

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg"
alt="drawing" width="600"/>

<small> ConvNeXT architecture. Taken from the <a href="https://huggingface.co/papers/2201.03545">original paper</a>.</small>

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.

<PipelineTag pipeline="image-classification"/>

- [`ConvNextForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## ConvNextConfig

[[autodoc]] ConvNextConfig

## ConvNextImageProcessor

[[autodoc]] ConvNextImageProcessor
    - preprocess

## ConvNextImageProcessorFast

[[autodoc]] ConvNextImageProcessorFast
    - preprocess

## ConvNextModel

[[autodoc]] ConvNextModel
    - forward

## ConvNextForImageClassification

[[autodoc]] ConvNextForImageClassification
    - forward
