Workflow when using AnomalyMatch. We leverage both labelled and unlabelled data from a user dataset to train an EfficientNet architecture and include an active learning loop. Here, the unlabelled data is ranked by anomaly score, the user can extract more examples of the object they are searching for and add them to their training data. Once the desired model metrics are achieved, the model can be saved and then run across all images in their dataset.