VNet: A Versatile Network for Efficient Real Time Semantic Segmentation
Many recent excellent methods for efficient real time semantic segmentation are of low precision and heavily rely on multiple GPUs for training. In this paper, we rethink the critical factors affecting the accuracy of efficient segmentation models. The previous works usually reduce the input resolution prior to training the parameters of models by cropping or resizing the images. On the contrary, our empirical study shows that the reduced images lose the important content information and details, which are vital to the high precision. However, the previous methods are unable to train the original high resolution images due to the memory limited GPUs. To tackle this problem, we propose a novel versatile network (VNet) which employs reversible mechanism and asymmetric convolution to achieve highly efficient and extremely low memory consumption in backward propagation. In particular, we keep all the detailed spatial information of the input images without cropping or resizing to pursue decent prediction accuracy. It is worth noting that VNet can train multiple 1024x2048 high resolution images on only one standard GPU card. Under the same conditions, our model achieves a new state of the art result on Cityscapes datasets. Specifically, it can process the 1024x2048 high resolution inputs at a rate of 37.4 and 15.5 frames per second (fps) on a standard GPU and an edge device, respectively, with only 0.16 million parameters.
2019 IEEE 37th International Conference on Computer Design (ICCD)