Even though we used only 50% of the dataset (1651 images) we were able to model the training behaviour of our model and predict the model accuracy for a given amount of images. This same methodology can be used to predict the amount of images needed to reach a desired accuracy. This is very useful when a smaller set of data is available, and it has been shown that convergence on a deep learning model is possible, but more images are needed. The image count prediction can be used to plan and budget for further image collection initiatives.