Fast (aimed to "real time") Portrait Segmentation at mobile phone
This project is not normal semantic segmentation but focus on real-time protrait segmentation.All the experimentals works with pytorch.
I hope to find a effcient network which can run on mobile phone. Currently, successfull application of person body/protrait segmentation can be find in APP like SNOW&B612, whose technology is proposed by a Korea company Nalbi.
Encoder : mobilenet_v2(os: 32)
Decoder : unet(concat low level feature) use dilate convolution at different stage(d = 2, 6, 12, 18)
Encoder : shufflenet
Decoder : skip connection (add low level feature)
Attention model is a potential module in the segmentation task. I use a very light residual-dense net as the backbone of the Context Path. The details about fussion of last features in Contxt Path is not clear in the paper(BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation).
Hard segmentation + Soft matting.(coming soon)
Real-time ! ! ! :tada::tada::tada:
Platform : ncnn.
Mobile phone: Samsung Galaxy S8+(cpu).
model size (M) | time(ms) | |
---|---|---|
model_seg_matting | 3.3 | ~40 |
update : 2018/12/27: Demo video on my iphone 6 (baiduyun)
HUAWEI Mate 20 released recently can keep color on human and make the bacgrand gray in real time (click to view ). I test my model using cpu on my MAC, getting some videos here.
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。