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+ ---
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+ license: apache-2.0
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+ tags:
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+ - vision
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+ - image-classification
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+ datasets:
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+ - imagenet-1k
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+ widget:
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+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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+ example_title: Tiger
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+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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+ example_title: Teapot
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+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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+ example_title: Palace
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+ ---
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+
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+ # ConvNeXt V2 (large-sized model)
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+
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+ ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2).
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+
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+ 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.
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+
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+ ## Model description
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+
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+ 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.
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+
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png)
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+
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+ ## Intended uses & limitations
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+
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+ You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) to look for
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+ fine-tuned versions on a task that interests you.
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+
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+ ### How to use
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+
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+ Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
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+
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+ ```python
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+ from transformers import AutoImageProcessor, ConvNextV2ForImageClassification
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+ import torch
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("huggingface/cats-image")
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+ image = dataset["test"]["image"][0]
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+
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+ preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-large-22k-384")
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+ model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-large-22k-384")
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+
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+ inputs = preprocessor(image, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ logits = model(**inputs).logits
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+
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+ # model predicts one of the 1000 ImageNet classes
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+ predicted_label = logits.argmax(-1).item()
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+ print(model.config.id2label[predicted_label]),
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+ ```
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+
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+ For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnextv2).
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-2301-00808,
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+ author = {Sanghyun Woo and
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+ Shoubhik Debnath and
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+ Ronghang Hu and
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+ Xinlei Chen and
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+ Zhuang Liu and
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+ In So Kweon and
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+ Saining Xie},
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+ title = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders},
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+ journal = {CoRR},
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+ volume = {abs/2301.00808},
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+ year = {2023},
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+ url = {https://doi.org/10.48550/arXiv.2301.00808},
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+ doi = {10.48550/arXiv.2301.00808},
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+ eprinttype = {arXiv},
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+ eprint = {2301.00808},
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+ timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```