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--- |
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library_name: keras |
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tags: |
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- image-classification |
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- computer-vision |
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- convmixer |
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- cifar10 |
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--- |
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## Model description |
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### Image classification with ConvMixer |
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[Keras Example Link](https://keras.io/examples/vision/convmixer/) |
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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. |
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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. |
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Full Credits to <a href = "https://twitter.com/RisingSayak" target='_blank'> Sayak Paul </a> for this work. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Trained and evaluated on [CIFAR-10](https://keras.io/api/datasets/cifar10/) dataset. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | weight_decay | exclude_from_weight_decay | training_precision | |
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|----|-------------|-----|------|------|-------|-------|------------|-------------------------|------------------| |
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|AdamW|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|9.999999747378752e-05|None|float32| |
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## Training Metrics |
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Model history needed |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
