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Symmetry-Aware Transformer-based Mirror Detection

This repo is the official implementation of AAAI 2023 paper "Symmetry-Aware Transformer-based Mirror Detection"

Installation

Our project is based on MMsegmentation and Swin-Transformer-Semantic-Segmentation. Please follow the official get_started.md for installation and dataset preparation. We recommend to create a conda environment and install dependencies in Linux as follows:

conda create -n satnet python=3.7 -y
conda activate satnet

conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch -y
pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
git clone https://github.com/tyhuang0428/SATNet
cd SATNet
pip install -e .  # or "python setup.py develop"
pip install -r requirements/optional.txt

mkdir data

Data preparation

We train and evaluate our SATNet on Mirror Segmentation Dataset (MSD), Progressive Mirror Dataset (PMD), RGB-D Mirror Dataset (RGBD-Mirror). You can download zip files for corresponding datasets here and unpack them to SATNet/data

Results and Models

Pre-trained Swin-S are available here.

Get Started

Train

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} --load-from ${Swin-S_CHECKPOINT_FILE}

# Config file of our SATNet is in the folder: ./configs/satnet/
# For example, train our SATNet on the MSD dataset with 8 GPUs
./tools/dist_train.sh ./configs/satnet/msd_satnet.py 8 --load-from ./swin.pth

• Tensorboard

  If you want to use tensorboard, you need to pip install tensorboard and uncomment the Line 6 dict(type='TensorboardLoggerHook') of SATNet/configs/_base_/default_runtime.py.

Testing

python ./tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --eval mIoU

# For example, test our SATNet on MSD dataset
python ./tools/test.py ./configs/satnet/msd_satnet.py ./msd.pth --eval mIoU

Visualization

python ./tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --show-dir ${SAVE_PATH}

# For example, visulaize the results of our SATNet on MSD dataset
python ./tools/test.py ./configs/satnet/msd_satnet.py ./msd.pth --show --show-dir ./results/msd

Please see getting_started.md for the more basic usage of training and testing.