Original article was published by Sik-Ho Tsang on Artificial Intelligence on Medium
Reading: ESPNetv2 — A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network (Image Classification, Semantic Segmentation, etc)
In this story, “ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network” (ESPNetv2), by University of Washington, Allen Institute for AI (AI2), and XNOR.AI, is presented. In this paper:
- EESP: Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions are proposed to learn representations from a large effective receptive field with fewer FLOPs and parameters.
- It can be used in four tasks: (1) image/multi-object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling.
- In particular, ESPNetv2 outperforms ESPNet by 4–5% and has 2−4× fewer FLOPs on the PASCAL VOC and the Cityscapes dataset.
This is a paper in 2019 CVPR with over 70 citations. (Sik-Ho Tsang @ Medium)