ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Last update: Jan 04, 2023

Overview

ENet in Caffe

Execution times and hardware requirements

Network	1024x512	1280x720	Parameters	Model size (fp32)
ENet	20.4 ms	32.9 ms	0.36 M	1.5 MB
SegNet	66.5 ms	114.3 ms	29.4 M	117.8 MB

A comparison of computational time, number of parameters and model size required for ENet and SegNet. The caffe time command was used to compute time requirement averaged over 100 iterations. Hardware setup: Intel Xeon E5-1620v3, Titan X Pascal with cuDNN v5.1.

Tutorial

For a detailed introduction on how to train and test ENet please see the tutorial.

Publication

This work has been published in arXiv: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

ModelDepot

Also available on ModelDepot.

License

This software is released under a creative commons license which allows for personal and research use only. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/

Owner

Timo Sämann

GitHub Repository

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