Motion Triggered Wildlife Camera traps are rapidly being used to remotely track animals and help perform different ecological studies across the globe. The system captures animal visuals that enable the forest department of the respective country to keep track of critically endangered species, record their actions, research environmental changes in order to generate methods This piece of equipment is typically deployed within the forest area in large numbers, resulting in millions of recorded images and videos. It normally takes days, if not months, to go through the dataset completely and, identify the captured animals. In this paper, we study some classifiers of the fauna image that use the convolution neural network to process and identify the wildlife captured by these camera traps.