Deep learning is a field within machine learning that has grown in popularity. It is used in areas such as: image classification, speech recognition, market price predictions, object detection and much more. The main objective of this study has been to, on the requests of a company, train a model using deep learning to be able to classify and produce masks of objects of interest within images. A comparison of different optimization algorithms was done in order to identify the optimal one for the task at hand. Pixel-wise annotations of the objects were produced in order to train the model. By altering the code of Matterports implementation of Mask R-CNN to train on the dataset (of images) provided by HIAB, the goals were achieved. The optimization algorithm best suited for the conditions of this study was concluded to be AdaGrad. This was concluded based on the mean value of the total loss for each optimization algorithm. In future work, the dataset would preferably be larger in order to increase the predictive quality of the model.