MultiNet-A-Multitask_leraning-architecture

This repository contains the source code of Multinet.

Dataset

Download pre-processed NYUv2 dataset here which we evaluated in the paper. Dataroot should be like this:

data
├───train
│ ├───depth
│ ├───image
│ ├───label
│ |───normal
|───val
├───depth
├───image
├───label
|───normal

To run any multinet: python3 train_multinet.py --dataroot /data --apply_augmentation --ckpt_dir /ckpt --epochs 200 --batch_size 4 --backbone resnet101 --architecture fcn

To run SingleNet: python3 train_singlenet.py --dataroot /data --apply_augmentation --ckpt_dir /ckpt_dir --epochs 200 --batch_size 4 --backbone resnet101 --task semantic

Results

Citation

@inproceedings{liu2019end,
  title={End-to-End Multi-task Learning with Attention},
  author={Liu, Shikun and Johns, Edward and Davison, Andrew J},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1871--1880},
  year={2019}
}

Acknowledgement

We would like to thank Prof. Jacob Whitehill for his help on this project.

Contact

If you have any questions, please contact kgnandanwar@wpi.edu.