Edge Detection using Deep Learning using tensorflow_gpu

Author = {'Chang, Dekuan'} Email = {"cdk2708@gmail.com"}

Input image	Final fused Edge maps	Edge maps from side layers

This repository contains tensorflow implementation of the HED model.

Details of hyper-paramters are available in the paper

@InProceedings{xie15hed,
  author = {"Xie, Saining and Tu, Zhuowen"},
  Title = {Holistically-Nested Edge Detection},
  Booktitle = "Proceedings of IEEE International Conference on Computer Vision",
  Year  = {2015},
}

Get this repo

git clone https://github.com/harsimrat-eyeem/holy-edge.git

Installing requirements

Its recommended to install the requirements in a conda virtual environment

Setting up

The HED model is trained on augmented training set created by the authors.

# location where training data : http://vcl.ucsd.edu/hed/HED-BSDS.tar would be downloaded and decompressed
download_path: '<path>'
# location of snapshot and tensorbaord summary events
save_dir: '<path>'
# location where to put the generated edgemaps during testing
test_output: '<path>'

Training data & Models

You can train the model to simply generate edgemaps.

This downloads the augmented training set created by authors of HED. Augmentation strategies include rotation to 16 predefined angles and cropping largest rectangle from the image. Details in section (4.1). To download training data run

VGG-16 base model

VGG base model available here is used for producing multi-level features. The model is modified according with Section (3.) of the paper. Deconvolution layers are set with tf.nn.conv2d_transpose. T he model uses single deconvolution layer in each side layers.

Training

Launch training

parser.add_argument('--train', dest='run_train', action='store_true', default=True, help='Launch training')
parser.add_argument('--test', dest='run_test', action='store_true', default=False, help='Launch testing on a list of images')

Launch tensorboard

tensorboard --logdir=<save_dir>

Testing

Edit the snapshot you want to use for testing in hed/configs/hed.yaml

parser.add_argument('--train', dest='run_train', action='store_true', default=False, help='Launch training') parser.add_argument('--test', dest='run_test', action='store_true', default=True, help='Launch testing on a list of images')

test_snapshot: <snapshot number>

save_dir: <path_to_repo_on_disk>/hed test_snapshot: 50

location where to put the generated edgemaps during testing

test_output: ''