keras-io/conv_mixer_image_classification
Model description
Image classification with ConvMixer
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
<details> <summary>View Model Plot</summary>
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