Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation

M.Naseer Subhani and Mohsen Ali

Contents

0. Introduction
1. Requirements
2. Setup
3. Implementation
4. Note
5. Contact

Introduction

This repo contains implementation of paper titled "Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation" introduced in ECCV2020

If you use this code in your research, please cite us.

@article{subhani2020learning,
  title={Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation},
  author={Subhani, M Naseer and Ali, Mohsen},
  journal={arXiv preprint arXiv:2007.14449},
  year={2020}
}

Requirements:

• Ubuntu 16.04 with NVIDIA Tesla K80 GPU.
• PyTorch 1.0.0
• Python 3.5

Setup

The root directory supposed to be "LSE/".

a. Datasets:

• Download GTA5 dataset.
• Download Cityscapes.
• Download SYNTHIA-RAND-CITYSCAPES. Make sure to change class id similar to cityscapes. or download synthia labels from this Link
• Put all datasets to "dataset/" folder.

b. Pretrained Initial Source Models:

• GTA5_VGG16-FCN8 Init.

• SYNTHIA_VGG16-FCN8 Init.

• Download all pretrained models and put in "init_models/"

Implementation

a. Run pip3 install -r requirements.txt.

b. Change root directory in init.py from utils folder.

c. Training:

• GTA_to_Cityscapes without Focal Loss:

  python3.5 LSE.py --model VGG --source gta5 --gamma 3 --beta 0.1 --focal-loss False --batch-size 1
  

• GTA_to_Cityscapes with Focal Loss:

  python3.5 LSE.py --model VGG --source gta5 --gamma 3 --beta 0.1 --focal-loss True --batch-size 1
  

• SYNTHIA_to_Cityscapes without Focal Loss:

  python3.5 LSE.py --model VGG --source synthia --gamma 3 --beta 0.1 --focal-loss False --batch-size 1
  

• SYNTHIA_to_Cityscapes with Focal Loss:

  python3.5 LSE.py --model VGG --source synthia --gamma 3 --beta 0.1 --focal-loss True --batch-size 1
  

d. Evaluation:

python3.5 eval.py --model VGG --model-name #model file name in .pth from snapshot folder#

Note

Increase the batch size as per your hardware requirements. Running algorithm with different initial conditions and parameters can vary the results.

Contact

M.Naseer Subhani : msee16021@itu.edu.pk