
## 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>

![Model Image](./model.png)

</details>
{"library_name": "keras", "tags": ["image-classification", "computer-vision", "convmixer", "cifar10"]}
keras tensorboard image-classification computer-vision convmixer cifar10 arxiv:2201.09792 has_space region:us
TAGS
#keras #tensorboard #image-classification #computer-vision #convmixer #cifar10 #arxiv-2201.09792 #has_space #region-us 

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 = "URL target='\_blank'> 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:



Training Metrics
----------------


Model history needed


Model Plot
----------



View Model Plot
!Model Image



### 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 = "URL target='\_blank'> 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:



Training Metrics
----------------


Model history needed


Model Plot
----------



View Model Plot
!Model Image
TAGS
#keras #tensorboard #image-classification #computer-vision #convmixer #cifar10 #arxiv-2201.09792 #has_space #region-us 
 ### 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 = "URL target='\_blank'> 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:



Training Metrics
----------------


Model history needed


Model Plot
----------



View Model Plot
!Model Image
passage: TAGS
#keras #tensorboard #image-classification #computer-vision #convmixer #cifar10 #arxiv-2201.09792 #has_space #region-us 
### 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 = "URL target='\_blank'> 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:



Training Metrics
----------------


Model history needed


Model Plot
----------



View Model Plot
!Model Image