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https://github.com/dvlab-research/Context-Aware-Consistency
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
https://github.com/dvlab-research/Context-Aware-Consistency
cvpr2021 semantic-segmentation semi-supervised-learning semi-supervised-segmentation
Last synced: 11 days ago
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Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
- Host: GitHub
- URL: https://github.com/dvlab-research/Context-Aware-Consistency
- Owner: dvlab-research
- License: mit
- Created: 2021-04-02T05:11:50.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-08-24T05:52:07.000Z (about 1 year ago)
- Last Synced: 2024-08-03T01:11:52.415Z (4 months ago)
- Topics: cvpr2021, semantic-segmentation, semi-supervised-learning, semi-supervised-segmentation
- Language: Python
- Homepage:
- Size: 1.03 MB
- Stars: 155
- Watchers: 5
- Forks: 19
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Semi-Supervised-Semantic-Segmentation - Code - research/Context-Aware-Consistency)|[Paper](https://arxiv.org/pdf/2106.14133.pdf)| (2021)
README
# 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**](https://jiaya.me/papers/semiseg_cvpr21.pdf) that has been accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). [[Paper]](https://jiaya.me/papers/semiseg_cvpr21.pdf)
# Highlight
1. Our method achives the state-of-the-art performance on semi-supervised semantic segmentation.
2. Based on [**CCT**](https://github.com/yassouali/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
1. Firstly, download the [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) Dataset, and the extra annotations from [SegmentationClassAug](https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip?dl=0).
2. 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
```
3. 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](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155154502_link_cuhk_edu_hk/EpHdT2JFT11FpsUc4jHE3CoB2wUZ5tQo_W0QzzqHdNtF-A?e=yx2Xha).
# 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}
}
```