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---
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>
</details> |