  While building convolutional network-based systems, the toll it takes to
train the network is something that cannot be ignored. In cases where we need
to append additional capabilities to the existing model, the attention
immediately goes towards retraining techniques. In this paper, I show how to
leverage knowledge about the dataset to append the class faster while
maintaining the speed of inference as well as the accuracies; while reducing
the amount of time and data required. The method can extend a class in the
existing object detection model in 1/10th of the time compared to the other
existing methods. S-Extension patch not only offers faster training but also
speed and ease of adaptation, as it can be appended to any existing system,
given it fulfills the similarity threshold condition.
