updated README

README.md

@@ -3,26 +3,59 @@ library_name: keras
 tags:
 - semi-supervised
 - image classification
+- domain adaption
+datasets:
+- MNIST
+- SVHN
 ---
 
 ## Model description
 
-More information needed
+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
 
-More information needed
+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
 
-More information needed
+### 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).
 
-## Training procedure
+Augmented versions of the source and target data were created in two versions - weakly and strongly augmented, as written in the original paper.
 
-### Training hyperparameters
+### 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:
 
-| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
-|----|-------------|-----|---|--------|-------|--------|------------------|
-|RMSprop|0.001|0.0|0.9|0.0|1e-07|False|float32|
+- 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%**
+