ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

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

Structure of this repository

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

Performance on the CityScape dataset

Our model ESPNet achives an class-wise mIOU of 60.336 and category-wise mIOU of 82.178 on the CityScapes test 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

Performance on the CamVid dataset

Our model achieves an mIOU of 55.64 on the CamVid test set. We used the dataset splits (train/val/test) provided here. We trained the models at a resolution of 480x360. For comparison with other models, see SegNet paper.

Note: We did not use the 3.5K dataset for training which was used in the SegNet paper.

Model	mIOU	Class avg.
ENet	51.3	68.3
SegNet	55.6	65.2
ESPNet	55.64	68.30

Pre-requisite

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.

Citation

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}
}