| library_name: tf-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> |