This repository contains the source code of our paper, ESPNet.

This repository is organized as:

• train This directory contains the source code for trainig the ESPNet-C and ESPNet models.
• test This directory contains the source code for evaluating our model on RGB Images.
• pretrained This directory contains the pre-trained models on the CityScape dataset
  • encoder This directory contains the pretrained ESPNet-C models
  • decoder This directory contains the pretrained ESPNet models

Our model ESPNet achives an class-wise mIOU of 60.336 and category-wise mIOU of 82.178 on the CityScapes validation dataset and runs at

• 112 fps on the NVIDIA TitanX (30 fps faster than ENet)
• 9 FPS on TX2
• With the same number of parameters as ENet, our model is 2% more accurate

To run this code, you need to have following libraries:

• OpenCV - We tested our code with version > 3.0.
• PyTorch

We recommend to use Anaconda. We have tested our code on Ubuntu 16.04.

If ESPNet is useful for your research, then please cite our paper.

@article{mehta2018espnet,
  title={ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation},
  author={Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh Hajishirzi},
  journal={arXiv preprint arXiv:1803.06815},
  year={2018}
}