Official implementation of "Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation". here
Accepted by ACM MM'22.
We released the code for edge enhancement loss
| GTA5-to-CityScapes | SYNTHIA-to-CityScapes | |||
|---|---|---|---|---|
| mIoU | mIoU_13 (mIoU_16) | |||
| Ours | 58.8 | Model | 64.6 (57.8) | Model |
| Ours* | 62.0 | Model | 69.2 (61.3) | Model |
*Indicates a new edge enhancement loss is added and still no distillation technology is required.
To run on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes, you need to download the respective datasets. Once they are downloaded, you can either modify the config files directly, or organize/symlink the data in the datasets/ directory as follows:
datasets
├── cityscapes
│ ├── gtFine
│ │ ├── train
│ │ │ ├── aachen
│ │ │ └── ...
│ │ └── val
│ └── leftImg8bit
│ ├── train
│ └── val
├── GTA5
│ ├── images
│ ├── labels
│ └── list
├── SYNTHIA
│ └── RAND_CITYSCAPES
│ ├── Depth
│ │ └── Depth
│ ├── GT
│ │ ├── COLOR
│ │ └── LABELS
│ ├── RGB
│ └── synthia_mapped_to_cityscapes
├── city_list
├── gta5_list
└── synthia_list
ln -s /data/lijj/pixmatch_output_61.5/ ./outputs
requirement.txt
- ImageNet pretrain: Download
- For GTA5-to-Cityscapes, we start with a model pretrained on the source (GTA5): Download
- For SYNTHIA-to-Cityscapes, we start with a model pretrained on the source (SYNTHIA): Download
sh train.sh
This code is based on the implementations of PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training and DACS: Domain Adaptation via Cross-domain Mixed Sampling.