Person segmentation with Keras (SegNet, Unet, etc.)
HumanParsing-Dataset is adopted in this repo.
Origin HumanParsing-Dataset contains 16+1 object classes. But in this repo, i just segment person which is a binary classification task.
I generate new label images by my self, you can download new label imgages at https://pan.baidu.com/s/1Y6bKUznsVc7xNWb9tqWaHA passwd: p8ks
Of course you can generate label images by yourself using convert_labels.py , gen_train_test_list.py.
I use 12706 images of HumanParsing-Dataset as training set, the remaining images as test set.
During training, i resize images with unchanged aspect ratio using padding, for details you can see this script.
Origin HumanParsing-Dataset contains 16+1 object classes.
background 0
hat 1
hair 2
sunglass 3
upper-clothes 4
skirt 5
pants 6
dress 7
belt 8
left-shoe 9
right-shoe 10
face 11
left-leg 12
right-leg 13
left-arm 14
right-arm 15
bag 16
scarf 17
I have simplified the parsing task. Now it contains 4 + 1 classes.
background 0 (background, bag)
head 1 (hat, hair, sunglass, face, scarf)
upper body 2 (upper-clothes)
both hands 3 (left-arm, right-arm)
lower body 4 (skirt, pants, dress, belt, left-shoe, right-shoe, left-leg, right-leg)
New label images can be downloaded from this link https://pan.baidu.com/s/1jhqpOn8oBmiJiwzohhkfww passwd: gamc
All models are defined in 'models' directory.
An example for training Unet.
python train_segmentation.py --model='unet'
For visiualsizing the predictions, you can use the follow command
python predict.py --model='unet'
mIU: 0.8918
Origin images, ground truth images and predictions.
Val accuracy during training.
Val loss during training.
| Part | mIoU | |
| Unet | head | 0.66476 |
| upper body | 0.48639 | |
| both hands | 0.27016 | |
| lower body | 0.66536 | |
| mean | 0.52167 |
Origin images, ground truth images and predictions.
Todo