Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

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]

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

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

1. Firstly, download the PASCAL VOC Dataset, and the extra annotations from SegmentationClassAug.
2. Extract the above compression files into your desired path, and make

    

    

    

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