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

arXiv: https://arxiv.org/abs/1801.05746 · HTML: https://mlsanity.com/paper/1801.05746/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2018.

## Built on by (most cited first, 30 of 76)

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- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning](https://mlsanity.com/paper/2408.07931/) (2024)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [Understanding 3D Object Articulation in Internet Videos](https://mlsanity.com/paper/2203.16531/) (2022)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
- [Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches](https://mlsanity.com/paper/2405.04771/) (2024)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- [Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge](https://mlsanity.com/paper/2102.13644/) (2021)
- [Can pre-trained models assist in dataset distillation?](https://mlsanity.com/paper/2310.03295/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video](https://mlsanity.com/paper/2109.13593/) (2021)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data](https://mlsanity.com/paper/2103.09276/) (2021)
- [Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery](https://mlsanity.com/paper/2404.14040/) (2024)
- [Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark](https://mlsanity.com/paper/2502.12003/) (2025)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder](https://mlsanity.com/paper/2407.16298/) (2024)
- [Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty](https://mlsanity.com/paper/2211.04161/) (2022)
- [Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp](https://mlsanity.com/paper/2302.09835/) (2023)
- [Deep Learning for Spatiotemporal Modeling of Urbanization](https://mlsanity.com/paper/2112.09668/) (2021)
- [BriFiSeg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images](https://mlsanity.com/paper/2211.03072/) (2022)
- [Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis](https://mlsanity.com/paper/2501.00525/) (2024)
- [Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame Context-driven Deep Learning Models](https://mlsanity.com/paper/2501.18361/) (2025)
- [A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals](https://mlsanity.com/paper/2111.08480/) (2021)
- [Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation](https://mlsanity.com/paper/2204.01712/) (2022)
- [Cross-dimensional transfer learning in medical image segmentation with deep learning](https://mlsanity.com/paper/2307.15872/) (2023)

## Abstract (arXiv)

Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net architectures composed of encoders and decoders are very popular for segmentation of medical images, satellite images etc. Typically, neural network initialized with weights from a network pre-trained on a large data set like ImageNet shows better performance than those trained from scratch on a small dataset. In some practical applications, particularly in medicine and traffic safety, the accuracy of the models is of utmost importance. In this paper, we demonstrate how the U-Net type architecture can be improved by the use of the pre-trained encoder. Our code and corresponding pre-trained weights are publicly available at https://github.com/ternaus/TernausNet. We compare three weight initialization schemes: LeCun uniform, the encoder with weights from VGG11 and full network trained on the Carvana dataset. This network architecture was a part of the winning solution (1st out of 735) in the Kaggle: Carvana Image Masking Challenge.