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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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# ConvNeXT (large-sized model) |
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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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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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## Model description |
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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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## Intended uses & limitations |
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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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### How to use |
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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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```python |
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from transformers import ConvNextImageProcessor, ConvNextForImageClassification |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-large-224") |
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model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224") |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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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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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext). |
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### BibTeX entry and citation info |
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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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``` |
