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  • Transparent Transformer Segmentation

    Introduction

    This repository contains the data and code for IJCAI 2021 paper Segmenting transparent object in the wild with transformer.

    Environments

    • python 3
    • torch = 1.4.0
    • torchvision
    • pyyaml
    • Pillow
    • numpy

    INSTALL

    python setup.py develop --user

    Data Preparation

    1. create dirs './datasets/transparent/Trans10K_v2'
    2. put the train/validation/test data under './datasets/transparent/Trans10K_v2'. Data Structure is shown below.
    Trans10K_v2
    ├── test
    │   ├── images
    │   └── masks_12
    ├── train
    │   ├── images
    │   └── masks_12
    └── validation
        ├── images
        └── masks_12

    Download Dataset: Google Drive. Baidu Drive. code: oqms

    Network Define

    The code of Network pipeline is in segmentron/models/trans2seg.py.

    The code of Transformer Encoder-Decoder is in segmentron/modules/transformer.py.

    Train

    Our experiments are based on one machine with 8 V100 GPUs with 32g memory, about 1 hour training time.

    bash tools/dist_train.sh $CONFIG-FILE $GPUS

    For example:

    bash tools/dist_train.sh configs/trans10kv2/trans2seg/trans2seg_medium.yaml 8

    Test

    bash tools/dist_train.sh $CONFIG-FILE $GPUS --test TEST.TEST_MODEL_PATH $MODEL_PATH

    Citations

    Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.

    @article{xie2021segmenting,
      title={Segmenting transparent object in the wild with transformer},
      author={Xie, Enze and Wang, Wenjia and Wang, Wenhai and Sun, Peize and Xu, Hang and Liang, Ding and Luo, Ping},
      journal={arXiv preprint arXiv:2101.08461},
      year={2021}
    }