b02f2d8b6a
* added dates to the models with a single hf papers link * added the dates for models with multiple papers * half of no_papers models done * rest of no_papers models also done, only the exceptions left * added copyright disclaimer to sam_hw, cohere, cohere2 + dates * some more fixes, hf links + typo * some new models + a rough script * the script looks robust, changed all paper links to hf * minor change to handle technical reports along with blogs * ran make fixup to remove the white space * refactor
4.4 KiB
This model was released on 2023-01-02 and added to Hugging Face Transformers on 2023-03-14.
ConvNeXt V2
Overview
The ConvNeXt V2 model was proposed in ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie. ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, and a successor of ConvNeXT.
The abstract from the paper is the following:
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.
ConvNeXt V2 architecture. Taken from the original paper.
This model was contributed by adirik. The original code can be found here.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.
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ConvNextV2ForImageClassification] is supported by this example script and notebook.
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.
ConvNextV2Config
autodoc ConvNextV2Config
ConvNextV2Model
autodoc ConvNextV2Model - forward
ConvNextV2ForImageClassification
autodoc ConvNextV2ForImageClassification - forward
TFConvNextV2Model
autodoc TFConvNextV2Model - call
TFConvNextV2ForImageClassification
autodoc TFConvNextV2ForImageClassification - call