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 Sayak Paul 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:
| 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
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