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TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

By Vladimir Iglovikov and Alexey Shvets

Introduction

TernausNet is a modification of the celebrated UNet architecture that is widely used for binary Image Segmentation. For more details, please refer to our arXiv paper.

UNet11

loss_curve

Pre-trained encoder speeds up convergence even on the datasets with a different semantic features. Above curve shows validation Jaccard Index (IOU) as a function of epochs for Aerial Imagery

This architecture was a part of the winning solutiuon (1st out of 735 teams) in the Carvana Image Masking Challenge.

Installation

pip install ternausnet

Citing TernausNet

Please cite TernausNet in your publications if it helps your research:

@ARTICLE{arXiv:1801.05746,
         author = {V. Iglovikov and A. Shvets},
          title = {TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation},
        journal = {ArXiv e-prints},
         eprint = {1801.05746},
           year = 2018
        }

Example of the train and test pipeline

https://github.com/ternaus/robot-surgery-segmentation