Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device". @ CAD&Graphics 2019

We propose a real-time portrait segmentation model, called PortraitNet, that can run effectively and efficiently on mobile device. PortraitNet is based on a lightweight U-shape architecture with two auxiliary losses at the training stage, while no additional cost is required at the testing stage for portrait inference.

• python 3.9
• CUDA 12.0
• conda create -n port python=3.9
• pip install -r requirements.txt

If you want to use another pip sources in China: pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

Or another pip sources:

• **科学技术大学 : https://pypi.mirrors.ustc.edu.cn/simple
• 豆瓣：http://pypi.douban.com/simple/
• 阿里云：http://mirrors.aliyun.com/pypi/simple/

• EG1800 Since several image URL links are invalid in the original EG1800 dataset, we finally use 1447 images for training and 289 images for validation.

• Supervise-Portrait Supervise-Portrait is a portrait segmentation dataset collected from the public human segmentation dataset Supervise.ly using the same data process as EG1800.

python train.py --batchsize 32 

Test for the image and video:

• Test for a single image python test.py
• Test for a video Coming soon...

Using tensorboard to visualize the training process:

cd path_to_save_model
tensorboard --logdir='./log'

from Dropbox:

• mobilenetv2_eg1800_with_two_auxiliary_losses(Training on EG1800 with two auxiliary losses)