readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - vision
  datasets:
  - cats_vs_dogs
  metrics:
  - accuracy
  model-index:
  - name: convnext-base-224-22k-1k-orig-cats-vs-dogs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cats_vs_dogs
        type: cats_vs_dogs
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9973333333333333
  ---
  
  # convnext-base-224-22k-1k-orig-cats-vs-dogs
  
  This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on the cats_vs_dogs dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0103
  - Accuracy: 0.9973
  
  <p align="center">
      <img src="https://files.ocula.com/anzax/09/09f77133-7740-4130-a567-84fb56736362_650_544.jpg" width="600"> </br>
      Jockum Nordström, Cat Dog Cat, 2016
  </p>
  
  ## Model description
  
  The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  
  
  ### 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}
  }
  ```