Camera traps are used around the world to provide data on species, population sizes and how species are interacting. However this creates a lot of work in identifying which animal was actually spotted near the camera. Attempts have been made to use deep-learning to identify animals and work correctly for animals which are not rare but the lack of training data of rare species is a hurdle yet to be overcome. This research is focused how well the MIT Data-Efficient Generative Adversarial Network or MITGAN for short can generate realistic samples to be used as training data. For this we use a modified version of the CCT20 data-set. Which has artificially made a single rare class: the deer class. We trained a MITGAN model to generate images of our artificial rare class and used these images to train a classification model. This model was then compared to a baseline model as well as an oversampled model. From this it follows that using the MITGAN model to generate extra samples for our rare class is not worth the effort it takes compared to the precision increase in the rare class.