readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - imagenet-21k
  - imagenet-1k
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  ---
  
  # ConvNeXT (base-sized model) 
  
  ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. 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). 
  
  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.
  
  ## Model description
  
  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.
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png)
  
  ## Intended uses & limitations
  
  You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) 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:
  
  ```python
  from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
  import torch
  from datasets import load_dataset
  
  dataset = load_dataset("huggingface/cats-image")
  image = dataset["test"]["image"][0]
  
  feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-base-384-22k-1k")
  model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-384-22k-1k")
  
  inputs = feature_extractor(image, return_tensors="pt")
  
  with torch.no_grad():
      logits = model(**inputs).logits
  
  # model predicts one of the 1000 ImageNet classes
  predicted_label = logits.argmax(-1).item()
  print(model.config.id2label[predicted_label]),
  ```
  
  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-2201-03545,
    author    = {Zhuang Liu and
                 Hanzi Mao and
                 Chao{-}Yuan Wu and
                 Christoph Feichtenhofer and
                 Trevor Darrell and
                 Saining Xie},
    title     = {A ConvNet for the 2020s},
    journal   = {CoRR},
    volume    = {abs/2201.03545},
    year      = {2022},
    url       = {https://arxiv.org/abs/2201.03545},
    eprinttype = {arXiv},
    eprint    = {2201.03545},
    timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```