The Auto-Bio Challenge: the final project of PKU Computer Vision course

Firstly ...

This repository is forked and modified from the Trans2Seg official repository, the environment, installation and using method are almost the same. However there are several different points:

• The dataset is our own AUtoBio dataset, you should place the data folder in the correct position in the data in the root path.
• When training and testing the model, you should use the train_autobio.py in the tools folder, and other parameters are the same to use. (I mean the command in the terminal)
• In the original repository, the demo.py cannot be used but here you can use the demo.py to conduct inference, the using method is the same as training.

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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

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}
}