Accelerating urban scene understanding for autonomous vehicles with Multi-GPU training of Semantic Segmentation

An important challenge in the field of computer vision revolves around semantic segmentation, which involves the partitioning of an image into distinct semantic regions or objects. This paper introduces a semantic segmentation model utilizing the well-regarded Cityscapes dataset for urban scene comprehension, coupled with the UNet architecture. Additionally, a PyTorch-based approach is developed to facilitate efficient training of the model on multiple GPUs within a single node. The UNet architecture comprises an encoder and a decoder pathway, responsible for feature extraction and subsequent upsampling, respectively. Customized for the Cityscapes dataset, the architecture employs a combination of convolutional and pooling layers to adeptly capture both global and local contextual details. To streamline the training process of the proposed model, a PyTorch-based strategy for single node multi-GPU distributed data training is outlined. This strategy utilizes the torch.nn.parallel.DistributedDataParallel module, permitting the model to be trained on multiple GPUs within a single node. The proposed single node multi-GPU distributed data training technique not only accelerates the training process but also enhances efficiency. In comparison to a single GPU setup, it demonstrates the potential to cut down training time by as much as 50%. This advancement paves the way for more rapid and effective training of complex models, contributing to the advancement of semantic segmentation tasks in computer vision.