
# ConvMixer model

The ConvMixer model is trained on Cifar10 dataset and is based on [the paper](https://arxiv.org/abs/2201.09792v1), [github](https://github.com/locuslab/convmixer). 

Disclaimer : This is a demo model for Sayak Paul's keras [example](https://keras.io/examples/vision/convmixer/). Please refrain from using this model for any other purpose.

## Description

The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like [ViT](https://arxiv.org/abs/2010.11929v2). One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost.

### Intended Use

This model is intended to be used as a demo model for keras-io.

{"language": "en", "license": "apache-2.0", "tags": ["ConvMixer", "keras-io"], "datasets": ["cifar10"]}
keras ConvMixer keras-io en dataset:cifar10 arxiv:2201.09792 arxiv:2010.11929 license:apache-2.0 region:us
TAGS
#keras #ConvMixer #keras-io #en #dataset-cifar10 #arxiv-2201.09792 #arxiv-2010.11929 #license-apache-2.0 #region-us 


# ConvMixer model

The ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. 

Disclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose.

## Description

The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like ViT. One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost.

### Intended Use

This model is intended to be used as a demo model for keras-io.

# ConvMixer model

The ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. 

Disclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose. ## Description

The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like ViT. One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost. ### Intended Use

This model is intended to be used as a demo model for keras-io.
TAGS
#keras #ConvMixer #keras-io #en #dataset-cifar10 #arxiv-2201.09792 #arxiv-2010.11929 #license-apache-2.0 #region-us 
 # ConvMixer model

The ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. 

Disclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose. ## Description

The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like ViT. One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost. ### Intended Use

This model is intended to be used as a demo model for keras-io.
passage: TAGS
#keras #ConvMixer #keras-io #en #dataset-cifar10 #arxiv-2201.09792 #arxiv-2010.11929 #license-apache-2.0 #region-us 
# ConvMixer model

The ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. 

Disclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose.## Description

The paper uses 'patches' (square group of pixels) extracted from the image, which has been done in other Vision Transformers like ViT. One notable dawback of such architectures is the quadratic runtime of self-attention layers which takes a lot of time and resources to train for usable output. The ConvMixer model, instead uses Convolutions along with the MLP-mixer to obtain similar results to that of transformers at a fraction of cost.### Intended Use

This model is intended to be used as a demo model for keras-io.