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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 (large-sized model)
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+
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+ ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
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+
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+ Disclaimer: The team releasing ConvNeXT 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 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
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+
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_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=convnext) 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 ConvNextFeatureExtractor, ConvNextForImageClassification
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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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+ feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-large-224")
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+ model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224")
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+
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+ inputs = feature_extractor(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/convnext).
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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-2201-03545,
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+ author = {Zhuang Liu and
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+ Hanzi Mao and
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+ Chao{-}Yuan Wu and
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+ Christoph Feichtenhofer and
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+ Trevor Darrell and
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+ Saining Xie},
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+ title = {A ConvNet for the 2020s},
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+ journal = {CoRR},
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+ volume = {abs/2201.03545},
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+ year = {2022},
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+ url = {https://arxiv.org/abs/2201.03545},
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+ eprinttype = {arXiv},
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+ eprint = {2201.03545},
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+ timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```