readme: ---
  library_name: keras
  tags:
  - image-classification
  - computer-vision
  - convmixer
  - cifar10
  ---
  
  ## Model description
  
  ### Image classification with ConvMixer
  
  [Keras Example Link](https://keras.io/examples/vision/convmixer/)
  
  In the [Patches Are All You Need paper](https://arxiv.org/abs/2201.09792), the authors extend the idea of using patches to train an all-convolutional network and demonstrate competitive results. Their architecture namely ConvMixer uses recipes from the recent isotrophic architectures like ViT, MLP-Mixer (Tolstikhin et al.), such as using the same depth and resolution across different layers in the network, residual connections, and so on.
  
  ConvMixer is very similar to the MLP-Mixer, model with the following key differences: Instead of using fully-connected layers, it uses standard convolution layers. Instead of LayerNorm (which is typical for ViTs and MLP-Mixers), it uses BatchNorm.
  
  Full Credits to <a href = "https://twitter.com/RisingSayak" target='_blank'> Sayak Paul </a> for this work.
  
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Trained and evaluated on [CIFAR-10](https://keras.io/api/datasets/cifar10/) dataset.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | weight_decay | exclude_from_weight_decay | training_precision |
  |----|-------------|-----|------|------|-------|-------|------------|-------------------------|------------------|
  |AdamW|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|9.999999747378752e-05|None|float32|
  
   ## Training Metrics
  Model history needed
   ## Model Plot
  
  <details>
  <summary>View Model Plot</summary>
  
  ![Model Image](./model.png)
  
  </details>