Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC)

Xin Lai^*, Zhuotao Tian^*, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, Jiaya Jia

This is the official PyTorch implementation of our paper Semi-supervised Semantic Segmentation with Directional Context-aware Consistency that has been accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). [Paper]

Highlight

Our method achives the state-of-the-art performance on semi-supervised semantic segmentation.
Based on CCT, this Repository also supports efficient distributed training with multiple GPUs.

Get Started

Environment

The repository is tested on Ubuntu 18.04.3 LTS, Python 3.6.9, PyTorch 1.6.0 and CUDA 10.2

pip install -r requirements.txt

Datasets Preparation

Firstly, download the PASCAL VOC Dataset, and the extra annotations from SegmentationClassAug.
Extract the above compression files into your desired path, and make it follow the directory tree as below.

-VOCtrainval_11-May-2012
    -VOCdevkit
        -VOC2012
            -Annotations
            -ImageSets
            -JPEGImages
            -SegmentationClass
            -SegmentationClassAug
            -SegmentationObject

Set 'data_dir' in the config file into '[YOUR_PATH]/VOCtrainval_11-May-2012'.

Training

Firsly, you should download the PyTorch ResNet101 or ResNet50 ImageNet-pretrained weight, and put it into the 'pretrained/' directory using the following commands.

cd Context-Aware-Consistency
mkdir pretrained
cd pretrained
wget https://download.pytorch.org/models/resnet50-19c8e357.pth # ResNet50
wget https://download.pytorch.org/models/resnet101-5d3b4d8f.pth # ResNet101

Run the following commands for training.

train the model on the 1/8 labeled data (the 0-th data list) of PASCAL VOC with the segmentation network and the backbone set to DeepLabv3+ and ResNet50 respectively.

python3 train.py --config configs/voc_cac_deeplabv3+_resnet50_1over8_datalist0.json

train the model on the 1/8 labeled data (the 0-th data list) of PASCAL VOC with the segmentation network and the backbone set to DeepLabv3+ and ResNet101 respectively.

python3 train.py --config configs/voc_cac_deeplabv3+_resnet101_1over8_datalist0.json

Testing

For testing, run the following command.

python3 train.py --config [CONFIG_PATH] --resume [CHECKPOINT_PATH] --test True

Pre-trained Models

For your convenience, you can download some of the pre-trained models from Here.

Related Repositories

This repository highly depends on the CCT repository at https://github.com/yassouali/CCT. We thank the authors of CCT for their great work and clean code.

Besides, we also borrow some codes from the following repositories.

MoCo at https://github.com/facebookresearch/moco.
Deeplabv3+ at https://github.com/jfzhang95/pytorch-deeplab-xception.
Semseg at https://github.com/hszhao/semseg

Thanks a lot for their great work.

Citation

If you find this project useful, please consider citing:

@inproceedings{lai2021cac,
  title     = {Semi-supervised Semantic Segmentation with Directional Context-aware Consistency},
  author    = {Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang and Jiaya Jia},
  booktitle = {CVPR},
  year      = {2021}
}

dvlab-research/Context-Aware-Consistency