This is the official PyTorch implementation of our paper:

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation.
Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi and Yang Gao.

Getting Started

Data Preparation

Pre-trained Model

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


Pascal | Augmented Masks | Cityscapes | Class Mapped Masks

File Organization

├── ./pretrained
    ├── resnet50.pth
    ├── resnet101.pth
    └── deeplabv2_resnet101_coco_pretrained.pth
├── [Your Pascal Path]
    ├── JPEGImages
    └── SegmentationClass    # replace the official folder with above augmented masks 
├── [Your Cityscapes Path]
    ├── gtFine               # replace the official folder with above class mapped masks 
    └── leftImg8bit

Training and Testing

export semi_setting='pascal/1_8/split_0'

CUDA_VISIBLE_DEVICES=0,1 python -W ignore \
  --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.


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.

Thanks a lot for their great works!


If you find this project useful, please consider citing:

  title={ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation},
  author={Yang, Lihe and Zhuo, Wei and Qi, Lei and Shi, Yinghuan and Gao, Yang},
  journal={arXiv preprint arXiv:2106.05095},