A study was conducted using a TrapCam dataset to develop a model to assist biologist(s) with sorting TrapCam pictures of interest from blank pictures. This study utilized a dataset of over 50,000 pictures. There were eight categories of interest identified for this study: baby owls, dogs, owls, people, cars, equipment, trucks, and feeding with owls being the primary class. The first seven categories are objects detected by the model and the last category is a behavior which was treated the same way as the objects. The model utilized is the yolo v4 model which is an open-source computer vision model designed for object detection utilizing bounding boxes. The model was ‘trained’ utilizing a data set from the project site and pictures found on google. This dataset can be used as a base for other models to be hand trained as well. Much of the code developed during the training process can be reused for other projects cutting down significantly on development time. The dataset generation can be conducted by anyone with minimal set up and training. This project was conducted utilizing open-source software along with the google cloud which resulted in minimal costs for development and a path to a ‘low cost’ commercial product via the cloud. The google cloud can be swapped out for other ‘clouds’ if ever needed. Open source licenses almost always allow for use of the code in commercial products, so there should not be any licensing issues for use in project work.