csldali / headshoulder

Head Shoulder detection

Head and Shoulder Detection Base on MTCNN

use coco dataset to detect head and shoulder. This implements is base on MTCNN Pretrain model has been placed in models.

prepare

• download coco keypoints dataset
• python preprocess/coco.py --data-dir {your coco dataset } --anotation {anotation} -o {coco.feather} # collect keypoints and gen boundbox
• python preprocess/image_process.py -n {pnet,rnet,onet} --preprocess-path {./data/coco.feather} # gen data for one stage

train

• train pnet

python nets/net.py -n pnet -lr 0.002 -w 2

• train rnet

python nets/net.py -n rnet -lr 0.002 -w 2

• train onet

python nets/net.py -n onet -lr 0.002 -w 2

hard mining

python preprocess/hard_mining.py -n rnet python preprocess/hard_mining.py -n onet

test

python nets/test.py -p video

Warning

• Prediction is much more slower than expected in keras, but when predicts it on arm-rk3399, it only cost about 100ms totally.(python is really slow)
• how to improve the performace
  • batch norm
  • change the prediction of bound box, taking consideration of yolo v2/3.
  • cleaning data (it`s really important!!!!!. Our generator scripts exist a lot of noisy)
  • pruning model

current result

metric	pnet	rnet	onet
acc	94%	96.1%	98.5%