Reading: ERFNet — Efficient Residual Factorized ConvNet for Real-time (Semantic Segmentation)

Original article was published by Sik-Ho Tsang on Artificial Intelligence on Medium


Reading: ERFNet — Efficient Residual Factorized ConvNet for Real-time (Semantic Segmentation)

Outperforms DilatedNet, DPN, FCN, DeepLabv1, ENet & SegNet, Similar accuracy to SOTA, RefineNet & DeepLabv2, While Taking Only 24ms Per Image on a Single GPU

From Authors: https://www.youtube.com/watch?v=AbXzU9ZzqF4

In this story, “ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation” (ERFNet), by University of Alcal´a (UAH), and CSIRO-Data61, is shortly presented. In this paper:

  • A novel layer that uses residual connections and factorized convolutions, is proposed in order to remain efficient while retaining remarkable accuracy.
  • ERFNet is able to run at over 83 FPS in a single Titan X, and 7 FPS in a Jetson TX1 (embedded GPU).

This is a paper in 2017 TITS (IEEE Transactions on Intelligent Transportation Systems) with over 300 citations and with high impact factor of 6.319. (Sik-Ho Tsang @ Medium)