readme: ---
  library_name: keras
  tags:
  - semi-supervised
  - image classification
  - domain adaption
  datasets:
  - MNIST
  - SVHN
  ---
  
  ## Model description
  
  This is an image classification model based on a [WideResNet-2-28](https://arxiv.org/abs/1605.07146v4), trained using the [AdaMatch](https://arxiv.org/abs/2106.04732) method by Berthelot et al. 
  
   The training was based on the example [Semi-supervision and domain adaptation with AdaMatch]('https://keras.io/examples/vision/adamatch/') on keras.io by [Sayak Paul](https://twitter.com/RisingSayak). 
  
  The main difference to the training in the keras.io example is that here I increased the number of Epochs to 30, for a better target dataset performance.
  
  
  ## Intended uses & limitations
  
  AdaMatch attempts to combine *semi-supervised learning*, i.e. learning with a partially labelled dataset and *unsupersived domain adaption*, i.e. adapting a model to a different domain dataset without any labels.
  
  So it actually performs **semi-supervised domain adaptation (SSDA)**.
  
  The model is inteded to show that AdaMatch is able to carry out SSDA, with a accuracy on the target domain (SVHN) that is exceeding or competitive with other methods.
  
  ### Limitations
  The model was trained on MNIST as source and SVHN as target dataset. Thus, the classification performance on MNIST is very good (98.46%), while the accuracy on SVHN is "only" at 26.51%. Compared to the training of the same architecture without AdaMatch, this still is about 17% better
  
  ## Training and evaluation data
  
  ### Training Data
  The model was trained using the [MNIST](https://huggingface.co/datasets/mnist) (as source domain) and [SVHN cropped](http://ufldl.stanford.edu/housenumbers/) (as target domain) datasets. For training the images were used at a resolution of (32,32,3).
  
  Augmented versions of the source and target data were created in two versions - weakly and strongly augmented, as written in the original paper.
  
  ### Training Procedure
  This image from the original paper shows the workflow of AdaMatch:
  ![](https://i.imgur.com/1QsEm2M.png)
  For more information, refer to the [paper](https://arxiv.org/abs/2106.04732) or the original example at [keras.io]('https://keras.io/examples/vision/adamatch/').
  
  ### Hyperparameters
  
  The following hyperparameters were used during training:
  
  - Epochs: 30
  - Source Batch Size: 64
  - Target Batch Size: 3 * 64
  - Learning Rate: 0.03
  - Weight Decay: 0.0005
  - Network Depth: 28
  - Network Width Multiplier = 2
  
  ## Evaluation
  
  Accuracy on **source** test set: **98.46%**
  
  Accuracy on **target** test set: **26.51%**
  
  