Early-exit Deep Neural Networks (DNNs) and DNNs partitioning are valuable options to ease DNN implementation for image classification in resource-constrained devices and latency-sensitive applications. By inserting side branches into the usual DNN architecture, the resulting model can perform early classification attempts, using features extracted in layers that come before the model’s output. DNN partitioning spreads DNN layers subsets across devices, the edge, and the cloud, bringing branches closer to the device. Offloading to the cloud is limited to hard-to-classify data. Given the success in image classification, it is natural to try to replicate these results with semantic segmentation. In this direction, we propose Branched-DeepLabV3 (BranDeepLabV3) as an early attempt to bridge the gap between early-exit DNNs and semantic segmentation. Our results show that, by adding side branches to a semantic segmentation DNN, we can deliver adequate outputs without traversing the whole network. Moreover, our qualitative results show the usefulness of early-exits in semantic segmentation, as even coarse segmentations can be good enough to supply time-sensitive applications.