# Tensorflow Keras implementation of : Image classification with ConvMixer

The full credit goes to: Sayak Paul

## Short description:

ConvMixer is a simple model based on the ideas of representing an image as patches( used in ViT) and separating the mixing of Spatial and channel dimensions (used in MLP-Mixer). Unlike ViT and MLP-Mixer, they use only standard Convolution operations. The full paper is a submission to ICLR 22 and can be found here

## Model and Dataset used

The Dataset used here is CIFAR-10. The model is called ConvMixer-256/8 where 256 is the hidden dimension (the dimension of patches) and 8 is the depth(number of repetitions of ConvMix layers)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

Hyperparameters | Value |
---|---|

name | AdamW |

learning_rate | 0.0010000000474974513 |

decay | 0.0 |

beta_1 | 0.8999999761581421 |

beta_2 | 0.9990000128746033 |

epsilon | 1e-07 |

amsgrad | False |

weight_decay | 9.999999747378752e-05 |

exclude_from_weight_decay | None |

training_precision | float32 |

## Training Metrics

After 10 Epocs, the test accuracy of the model is 83.57%

## Model Plot

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