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
- attention (channel or feature attention)
current result
| metric | pnet | rnet | onet |
|---|---|---|---|
| acc | 94% | 96.1% | 98.5% |