U^2-Net (U square net)

Paper

U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

Authors

Xuebin Qin,
Zichen Zhang,
Chenyang Huang,
Masood Dehghan,
Osmar R. Zaiane and
Martin Jagersand.

Original Repo

https://github.com/xuebinqin/U-2-Net

(1) To run the human segmentation model, please first downlowd the u2net_human_seg.pth model weights into ./saved_models/u2net_human_seg/.
(2) Prepare the to-be-segmented images into the corresponding directory, e.g. ./test_data/test_human_images/.
(3) Run the inference by command: python u2net_human_seg_test.py and the results will be output into the corresponding dirctory, e.g. ./test_data/u2net_test_human_images_results/
Notes: Due to the labeling accuracy of the Supervisely Person Dataset, the human segmentation model (u2net_human_seg.pth) here won't give you hair-level accuracy. But it should be more robust than u2net trained with DUTS-TR dataset on general human segmentation task. It can be used for human portrait segmentation, human body segmentation, etc.

Required libraries

Python 3.6
numpy 1.15.2
scikit-image 0.14.0
python-opencv PIL 5.2.0
PyTorch 0.4.0
torchvision 0.2.1
glob

Citation

@InProceedings{Qin_2020_PR,
title = {U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection},
author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood and Zaiane, Osmar and Jagersand, Martin},
journal = {Pattern Recognition},
volume = {106},
pages = {107404},
year = {2020}
}