Semantic segmentation is the process of labeling the images pixel-wise. The result is a mask, from which we can depict what is located in certain parts of the image. In this work we dive into semantic segmentation of sclera, using state-of-the-art neural network arhitectures. Publicly available implementations of SegNet, DeepLabv3+, HRNetV2 and UPerNet are adapted and trained on the SBVPI and MASD datasets. We measure their success at the binary classification of the pixels as sclera or background. For this we use performance metrics mIoU, precision, recall and f1-score. We find the model UPerNet most succesful at this task, which is also show in the qualitative analysis. Models are also tested on a subset of the test set of the sclera benchmarking competition SSBC 2019. The results are compared to the winning model, where Segnet takes the lead.