  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
four different tasks: (1) object classification, (2) semantic segmentation, (3)
object detection, and (4) 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 outperforms ESPNet by 4-5% and has
2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to
YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy
with 6x fewer FLOPs. 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
https://github.com/sacmehta/ESPNetv2
