UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
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README.md

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

(Network architecure)

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.

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