94 lines
4.5 KiB
Markdown
94 lines
4.5 KiB
Markdown
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# ConvNeXT
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## Overview
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The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
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ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
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The abstract from the paper is the following:
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*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
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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
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(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
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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
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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
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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
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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
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and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg"
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alt="drawing" width="600"/>
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<small> ConvNeXT architecture. Taken from the <a href="https://arxiv.org/abs/2201.03545">original paper</a>.</small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
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[gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
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## Resources
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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.
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<PipelineTag pipeline="image-classification"/>
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- [`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).
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- See also: [Image classification task guide](../tasks/image_classification)
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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.
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## ConvNextConfig
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[[autodoc]] ConvNextConfig
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## ConvNextFeatureExtractor
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[[autodoc]] ConvNextFeatureExtractor
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## ConvNextImageProcessor
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[[autodoc]] ConvNextImageProcessor
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- preprocess
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<frameworkcontent>
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<pt>
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## ConvNextModel
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[[autodoc]] ConvNextModel
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- forward
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## ConvNextForImageClassification
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[[autodoc]] ConvNextForImageClassification
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- forward
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</pt>
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<tf>
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## TFConvNextModel
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[[autodoc]] TFConvNextModel
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- call
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## TFConvNextForImageClassification
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[[autodoc]] TFConvNextForImageClassification
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- call
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</tf>
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</frameworkcontent> |