Improve a classification model using unlabelled images

This code example shows how to use this library to exploit unlabelled data to increase the performance of an image classifier. The main procedure consists in first training a classifier using the labelled data (or using an already trained one). Then this model is used to make predictions for the unlabelled data, which are used as pseudo labels to create a new dataset which consists of both the pseudo labels and the training data previously used to train the classifier. This larger dataset is then used to train a larger and more performant classifier.

Preliminary steps

The first ingredient necessary to use semi-supervised learning is a labelled dataset, exactly as described in Build a dataset from scratch, saved at ‘data/image_dataset’. This dataset is used as in Train and optimize model to train the best possible model, which is called in this context the teacher and is saved as ‘model.h5’. Of course in the context where you want to improve a model, you skip this step and use your own existing model.

The other ingredient is a dataset of unlabelled images. This is ideally (but not necessarily) composed of relevant images to the problem that are not split into categories. The images are saved in a single folder called ‘data/unlabeled’.

Generating pseudo labels

The most important step in semi-supervised learning consists in labelling the unlabelled data, which is done using the following code:

generator = decavision.dataset_preparation.generate_pseudolabels.PseudoLabelGenerator()

This uses the teacher model to predict the label of each unlabelled image. These predictions are then saved in a csv file at ‘outputs/data.csv’.

Since the unlabelled images can come from different places, it is important to make sure that bad images are not used further. To ensure data quality, it can be helpful to plot the distribution of the predictions using:


A chart will be saved in the ‘outputs’ folder with the highest probability predicted for each image. If some probabilities are too low, it means that the image is so bad that the teacher model does not recognize it. Such image should ideally be discarded. This chart thus helps pick the threshold variable that will be used to create the new dataset with only relevant images.

The final step is to use the predictions along with the threshold (if one is used) to distribute the unlabelled images into classes. This pseudo labelled dataset is then combined with the original training data to make a larger dataset:


Note that the original datasets are kept intact. The larger dataset is made from copies of the images.

All these steps can also be done directly with the following code:

generator.generate_pseudolabel_data(plot_confidences=True, threshold=None, move_images=True)

Training the student model

With the larger dataset in hand the last step is to train a new and improved model, the student model, using the same method that was used for the teacher model. There are only a few points that are important to keep in mind to achieve the best model possible:

  • Make sure that you use data augmentation when training the student model.

  • Use a larger model. For example if you used EfficientNet B3 for the teacher, try B5 for the student.