Neural Architecture Search of Semantic Segmentation Models (in PyTorch)

This repository provides official models from the paper Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, available here

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
Vladimir Nekrasov, Hao Chen, Chunhua Shen, Ian Reid
To appear in CVPR, 2019

Getting Started

For flawless reproduction of our results, the Ubuntu OS is recommended. The models have been tested using Python 3.6.

Dependencies

pip3
Cython
cv2
jupyter-notebook
matplotlib
numpy
Pillow
torch>=1.0
torchvision

Inference Examples

For the ease of reproduction, we have embedded all our examples inside Jupyter notebooks.

Segmentation

Please refer to results on PASCAL VOC

Depth Estimation

Please refer to results on NYUD-v2

License

This project is free to use for non-commercial purposes - see the LICENSE file for details.

Acknowledgments

• University of Adelaide and Australian Centre for Robotic Vision (ACRV) for making this project happen
• HPC Phoenix cluster at the University of Adelaide for making the training of the models possible
• PyTorch developers
• Yerba mate tea