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* First draft * Add conversion script * Improve conversion script * Improve docs and implement tests * Define model output class * Fix tests * Fix more tests * Add model to README * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Apply more suggestions from code review * Apply suggestions from code review * Rename dims to hidden_sizes * Fix equivalence test * Rename gamma to gamma_parameter * Clean up conversion script * Add ConvNextFeatureExtractor * Add corresponding tests * Implement feature extractor correctly * Make implementation cleaner * Add ConvNextStem class * Improve design * Update design to also include encoder * Fix gamma parameter * Use sample docstrings * Finish conversion, add center cropping * Replace nielsr by facebook, make feature extractor tests smaller * Fix integration test Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
66 lines
3.2 KiB
Plaintext
66 lines
3.2 KiB
Plaintext
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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*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.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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Tips:
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- See the code examples below each model regarding usage.
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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). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
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## ConvNeXT specific outputs
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[[autodoc]] models.convnext.modeling_convnext.ConvNextModelOutput
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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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## 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 |