Awesome
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
- create dirs './datasets/transparent/Trans10K_v2'
- 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}
}