Awesome
Human Instance Segmentation
A Single-Human Instance Segmentor runs at 50 FPS on GV100. Both training and inference are included in this repo.
Install
# via pip
pip install git+https://github.com/Project-Splinter/human_inst_seg --upgrade
# via git clone
git clone https://github.com/Project-Splinter/human_inst_seg
cd human_inst_seg
python setup.py develop
Note to run demo.py
, you also need to install streamer_pytorch through:
pip install git+https://github.com/Project-Splinter/streamer_pytorch --upgrade
Train
First Download dataset from here
git clone https://github.com/Project-Splinter/human_inst_seg; cd human_inst_seg;
mkdir ./data # put all dataset zip under here and unzip them. It should contain two folders: `ATR_RemoveBG` and `alignment`
python human_inst_seg/train.py
Usage
# images
python demo.py --images <IMAGE_PATH> <IMAGE_PATH> <IMAGE_PATH> --loop --vis
# videos
python demo.py --videos <VIDEO_PATH> <VIDEO_PATH> <VIDEO_PATH> --vis
# capture device
python demo.py --camera --vis
API
seg_engine = Segmentation(ckpt=None, device="cuda:0", init=True):
seg_engine.init(pretrained="")
seg_engine.forward(input)
Note: Segmentation
is an instance of nn.Module
, so you need to be carefull when you want to integrate this to other trainable model.