ConvNeXt V2 (large sized model) ConvNeXt V2 model pretrained using the FCMAE framework and fine tuned on the ImageNet 22K dataset at resolution 224x224. It was introduced in the paper ConvNeXt V2: Co designing and Scaling ConvNets with Masked Autoencoders by Woo et al. and first released in this repository. Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team. Model description ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various recognition benchmarks. Intended uses & limitations You can use the raw model for image classification. See the model hub to look for fine tuned versions on a task that interests you. How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: For more code examples, we refer to the documentation. BibTeX entry and citation info
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