/hed-tf

an implementation of holistically nested edge detection using TensorFlow

Primary LanguagePython

Train HED on Your Own Data

training data

  1. put training data and labels in ./data/dataset/train_data/
  2. generate train.txt like the already existed one

vgg16 weights

google for vgg16.npy weights file and put it in ./data/weights/initial_weights/

p.s. if you don't use vgg16 weights just comment 'hed_class.assign_init_weights(sess)' in train.py

train

cd to the root directory './hed-tf'

python train.py -gpu '0' # default gpu 0

p.s. it seems that if you do not have gpu ,tf will run it in cpu in this program

Attention

change the learning rate in train.py

this repository is not exactly identical to hed paper.

I use dilate conv at conv1_1, you can change it easily.

for other changes , to see the paper and official code carefully.