conv_mixer_image_classification

Model description

Image classification with ConvMixer

Keras Example Link

In the Patches Are All You Need paper, 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 dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training Metrics

Model history needed

Model Plot

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