Training a neural network requires a large amount of labeled data that has to be created by either human annotation or by very specifically created methods. Currently, there is a vast abundance of unlabeled data that is neglected sitting on servers, hard drives, websites, etc. These untapped data sources serve as the inspiration for this paper. The goal of this thesis is to explore and test various methods of semi-supervised learning (SSL) for convolutional neural networks (CNN). These methods will be analyzed and evaluated based on their accuracy on a test set of data. Since this particular neural network will be used to offer paths for an autonomous robot, it is important for the networks to be lightweight in scale. This paper will then take this assortment of smaller neural networks and run them through a variety of semi-supervised training methods. The first method is to have a teacher model that is trained on properly labeled data create labels for unlabeled data and add this to the training set for the next student model. From this base method, a few variations were tried in the hopes of getting a significant improvement. The first variation tested by this thesis is the effects of having this teacher and student cycle run more than one iteration. After that, the effects of using the confidence values that the models produced were explored by both including only data with confidence above a certain value and in a different test, relabeling data below a confidence threshold. The last variation this thesis explored was to have two teacher models concurrently and have the combination of those two models decide on the proper label for the unlabeled data. Through exploration and testing, these methods are evaluated in the results section as to which one produces the best results for SSL.