[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Related tags

Deep LearningSETR
Overview

SEgmentation TRansformers -- SETR

image

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers,
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, Li Zhang,
CVPR 2021

Installation

Our project is developed based on mmsegmentation. Please follow the official mmsegmentation INSTALL.md and getting_started.md for installation and dataset preparation.

Main results

Cityscapes

Method Crop Size Batch size iteration set mIoU
SETR-Naive 768x768 8 40k val 77.37 model config
SETR-Naive 768x768 8 80k val 77.90 model config
SETR-MLA 768x768 8 40k val 76.65 model config
SETR-MLA 768x768 8 80k val 77.24 model config
SETR-PUP 768x768 8 40k val 78.39 model config
SETR-PUP 768x768 8 80k val 79.34 model config
SETR-Naive-DeiT 768x768 8 40k val 77.85 model config
SETR-Naive-DeiT 768x768 8 80k val 78.66 model config
SETR-MLA-DeiT 768x768 8 40k val 78.04 model config
SETR-MLA-DeiT 768x768 8 80k val 78.98 model config
SETR-PUP-DeiT 768x768 8 40k val 78.79 model config
SETR-PUP-DeiT 768x768 8 80k val 79.45 model config

ADE20K

Method Crop Size Batch size iteration set mIoU mIoU(ms+flip)
SETR-Naive 512x512 16 160k Val 48.06 48.80 model config
SETR-MLA 512x512 8 160k val 48.27 50.03 model config
SETR-MLA 512x512 16 160k val 48.64 50.28 model config
SETR-PUP 512x512 16 160k val 48.58 50.09 model config

Pascal Context

Method Crop Size Batch size iteration set mIoU mIoU(ms+flip)
SETR-Naive 480x480 16 80k val 52.89 53.61 model config
SETR-MLA 480x480 8 80k val 54.39 55.39 model config
SETR-MLA 480x480 16 80k val 54.87 55.83 model config
SETR-PUP 480x480 16 80k val 54.40 55.27 model config

Get Started

Train

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} 
# For example, train a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_train.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py 8

Single-scale testing

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

Multi-scale testing

Use the config file ending in _MS.py in configs/SETR.

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8_MS.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

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

Reference

@inproceedings{SETR,
    title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers}, 
    author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip H.S. and Zhang, Li},
    booktitle={CVPR},
    year={2021}
}

License

MIT

Acknowledgement

Thanks to previous open-sourced repo:
mmsegmentation
pytorch-image-models

Owner
Fudan Zhang Vision Group
Zhang Vision Group at the School of Data Science of the Fudan University, led by Professor Li Zhang
Fudan Zhang Vision Group
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