Awesome
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
A pytorch implementation of LTIR.
<img width="534" alt="image" src="https://user-images.githubusercontent.com/39029444/78094147-c9123800-740e-11ea-83b0-3ee28c2d305b.png">Requirements
- Python 3.6
- torch==1.2
- torchvision==0.4
- Pillow==6.1.0
Preparing dataset
We used code from Style-swap and CycleGAN.
Training
Initial weight
python train_gta2cityscapes.py --translated-data-dir /Path/to/translated/source --stylized-data-dir /Path/to/stylized/source
Evalutation
python evaluate_cityscapes.py --restore-from /Path/to/weight
python compute_iou.py /Path/to/Cityscapes/gtFine/val /Path/to/results
Weight of Final Model
GTA5 to Cityscapes
SYNTHIA to Cityscapes
Acknowledgement
This code is based on AdaptSegNet and BDL.