BECO - Official Pytorch Implementation

Boundary-enhanced Co-training for Weakly Supervised Semantic Segmentation (To appear in CVPR 2023)

Shenghai Rong, Bohai Tu, Zilei Wang, Junjie Li

Prerequisite

• Python 3.8, PyTorch 1.11.0, and more in requirements.txt
• PASCAL VOC 2012 devkit
• NVIDIA GPU with more than 24GB of memory

Usage

Install python dependencies

$ pip install -r requirements.txt

Download PASCAL VOC 2012 devkit

• Download Pascal VOC2012 dataset from the official dataset homepage.

Download ImageNet pretrained model of DeeplabV3+

• Download ImageNet pretrained model of DeeplabV3+ from mmclassification .
• And rename the downloaded pth as "resnetv1d101_mmcv.pth"

Generate pseudo-labels and confidence masks

• Pleaase refer to ./first-stage/irn/README.md for details.
• After generating pseudo-labels and confidence masks, please rename their directories as "irn_pseudo_label" and "irn_mask" respectively.
• The generated irn_pseudo_label and irn_mask are also provided here for reproducing our method more directly. [Google Drive] / [Baidu Drive]

Prepare the data directory

$ cd BECO/
$ mkdir data
$ mkdir data/model_zoo
$ mkdir data/logging

And put the data and pretrained model in the corresponding directories like:

data/
    --- VOC2012/
        --- Annotations/
        --- ImageSet/
        --- JPEGImages/
        --- SegmentationClass/
        --- ...
    --- irn_pseudo_label/
        --- ****.png
        --- ****.png
    --- irn_mask/
        --- ****.png
        --- ****.png
    --- model_zoo/
        --- resnetv1d101_mmcv.pth
    --- logging/

Weakly Supervised Semantic Segmentation on VOC2012

Train

$ CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py -dist --logging_tag beco1

This code also supports AMP acceleration to reduce the GPU memory cost in half. Note that the "batch_size" in main.py refers to the batch_size of per GPU. So you should modify it when using different numbers of GPUs to keep the total batch_size of 16.

$ CUDA_VISIBLE_DEVICES=0,1 python main.py -dist --logging_tag beco1 --amp

Test

$ CUDA_VISIBLE_DEVICES=0 python main.py --test --logging_tag beco1 --ckpt best_ckpt.pth

Please refer to pydensecrf to install CRF python library for testing with the CRF post-processing.

$ python test.py --crf --logits_dir ./data/logging/beco1/logits --mode "val"

Main Results

Method	Dataset	Backbone	Weights	Val mIoU (w/o CRF)
BECO	VOC2012	ResNet101	[Google Drive] / [Baidu Drive]	70.9
BECO	COCO2014	ResNet101	[Google Drive] / [Baidu Drive]	45.6