ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
This work addresses the need for light-weight and power-efficient neural networks for deployment in resource-constrained environments, representing an incremental improvement over prior efficient methods.
The paper tackles the problem of designing efficient convolutional neural networks by introducing ESPNetv2, which achieves superior performance with fewer FLOPs and parameters across tasks like object classification, semantic segmentation, object detection, and language modeling, delivering up to 4.4% higher accuracy with 6x fewer FLOPs compared to YOLOv2 on MS-COCO.
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