Update README.md

README.md

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-# Causal Intervention for Weakly Supervised Semantic Segmentation 
-The main code for:
 
-Causal Intervention for Weakly Supervised Semantic Segmentation, Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun, NeurIPS 2020. [[CONTA]](xxx)
-
-## Requirements
-* Python 3.6, pytorch 1.2.0, torchvision 0.4.0, and more in requirements.txt
-* PASCAL VOC 2012 devkit and COCO
-* 8 NVIDIA GPUs with more than 1024MB of memory
-
-## Usage
-
-### Install python dependencies
-```
-pip install -r requirements.txt
-```
-
-### Download PASCAL VOC 2012 devkit and COCO
-* PASCAL VOC 2012 in https://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit
-* COCO in https://cocodataset.org/
-
-### To generate pseudo_mask:
-
-```
-cd pseudo_mask
-```
-
-```
-python run_sample.py
-```
-* You can either mannually edit the file, or specify commandline arguments.
-* Replace the ground_truth annotation in PASCAL VOC 2012 with the generated pseudo_mask.
-
-### To train DeepLab-v2:
-
-```sh
-cd segmentation
-```
-
-```sh
-python main.py train --config-path configs/voc12.yaml
-```
-
-### To evaluate the performance on validation set:
-
-```sh
-python main.py test --config-path configs/voc12.yaml \
-    --model-path data/models/voc12/deeplabv2_resnet101_msc/train_aug/checkpoint_final.pth
-```
-
-### To re-evaluate with a CRF post-processing:<br>
-
-```sh
-python main.py crf --config-path configs/voc12.yaml
-```
-
-### Inference Demo
-
-To process a single image:
-
-```bash
-python demo.py single \
-    --config-path configs/voc12.yaml \
-    --model-path model.pth \
-    --image-path image.jpg
-```
-
-To run on a webcam:
-
-```bash
-python demo.py live \
-    --config-path configs/voc12.yaml \
-    --model-path model.pth
-```
-
-## References
-
-1. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. DeepLab: Semantic Image
-Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. *IEEE TPAMI*,
-2018.<br>
-[Project](https://liangchiehchen.com/projects/DeepLab.html) /
-[Code](https://bitbucket.org/aquariusjay/deeplab-public-ver2) / [arXiv
-paper](https://arxiv.org/abs/1606.00915)
-
-2. H. Caesar, J. Uijlings, V. Ferrari. COCO-Stuff: Thing and Stuff Classes in Context. In *CVPR*, 2018.<br>
-[Project](https://github.com/nightrome/cocostuff) / [arXiv paper](https://arxiv.org/abs/1612.03716)
-
-3. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman. The PASCAL Visual Object
-Classes (VOC) Challenge. *IJCV*, 2010.<br>
-[Project](https://host.robots.ox.ac.uk/pascal/VOC) /
-[Paper](https://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf)
-
-4. Ahn, Jiwoon and Cho, Sunghyun and Kwak, Suha. Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations. *CVPR*, 2019.<br>
-[Project](https://github.com/jiwoon-ahn/irn) /
-[Paper](https://arxiv.org/abs/1904.05044)
-
-## Citation
-If you find the code useful, please consider citing our paper using the following BibTeX entry.
-```
-@InProceedings{dong_2020_conta,
-author = {Dong, Zhang and Hanwang, Zhang and Jinhui, Tang and Xiansheng, Hua and Qianru, Sun},
-title = {Causal Intervention for Weakly Supervised Semantic Segmentation},
-booktitle = {NeurIPS},
-year = 2020
-}
-```
-
-## Questions
-Please contact '[email protected]'