We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on three different tasks: (1) object classification, (2) semantic segmentation, and (3) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network has better generalization properties than ShuffleNetv2 when tested on the MSCOCO multi-object classification task and the Cityscapes urban scene semantic segmentation task. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at \url{https://github.com/sacmehta/ESPNetv2}

介绍了一种轻量级、低功耗、通用卷积神经网络，用于建模视觉和序列数据。通过使用组点智慧和深度智慧扩张分离卷积学习来自更大有效感受野的表示方法，实现更少浮点计算和参数。实验结果表明，ESPNetv2在物体分类、语义分割、物体检测和语言建模四个任务中均优于现有的最先进方法，且比ESPINet快4-5％，在PASCAL VOC和Cityscapes数据集上的FLOPS少2-4倍。与MS-COCO物体检测的YOLOv2相比，ESPNetv2在6倍以下的FLOPS下提供了4.4％的更高精度，且功耗更低。

ESPNetv2: 一种轻型、高效、通用的卷积神经网络