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

Related tags

Deep Learningreal-timecaffeenetsemantic-segmentation
Overview

ENet in Caffe

Alt text

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
Timo Sämann
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