CGSNet

Getting Started

Data Preparation

Pre-trained Model

ResNet-50 | ResNet-101 | DeepLabv2-ResNet-101

Dataset

Pascal JPEGImages | Pascal SegmentationClass | Cityscapes leftImg8bit | Cityscapes gtFine

File Organization

├── ./pretrained
    ├── resnet50.pth
    ├── resnet101.pth
    └── deeplabv2_resnet101_coco_pretrained.pth
    
├── [Your Pascal Path]
    ├── JPEGImages
    └── SegmentationClass
    
├── [Your Cityscapes Path]
    ├── leftImg8bit
    └── gtFine

Training and Testing

export semi_setting='pascal/1_8/split_0'

CUDA_VISIBLE_DEVICES=0,1 python -W ignore main.py \
  --dataset pascal --data-root [Your Pascal Path] \
  --batch-size 16 --backbone resnet50 --model deeplabv3plus \
  --labeled-id-path dataset/splits/$semi_setting/labeled.txt \
  --unlabeled-id-path dataset/splits/$semi_setting/unlabeled.txt \
  --pseudo-mask-path outdir/pseudo_masks/$semi_setting \
  --save-path outdir/models/$semi_setting

This script is for our ST framework. To run ST++, add --plus --reliable-id-path outdir/reliable_ids/$semi_setting.

Acknowledgement

The DeepLabv2 MS COCO pre-trained model is borrowed and converted from AdvSemiSeg. The image partitions are borrowed from Context-Aware-Consistency and PseudoSeg. Part of the training hyper-parameters and network structures are adapted from PyTorch-Encoding. The strong data augmentations are borrowed from MoCo v2 and PseudoSeg.

• AdvSemiSeg: https://github.com/hfslyc/AdvSemiSeg.
• Context-Aware-Consistency: https://github.com/dvlab-research/Context-Aware-Consistency.
• PseudoSeg: https://github.com/googleinterns/wss.
• PyTorch-Encoding: https://github.com/zhanghang1989/PyTorch-Encoding.
• MoCo: https://github.com/facebookresearch/moco.
• OpenSelfSup: https://github.com/open-mmlab/OpenSelfSup.

Thanks a lot for their great works!