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Holistically-Nested Edge Detection
| Date | Stars |
|---|---|
| 2026-07-24 | 354 |
| 2026-07-25 | 354 |
| 2026-07-28 | 354 |
| 2026-07-30 | 354 |
| 2026-08-06 | 354 |
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## Holistically-Nested Edge Detection
The code is also hosted at `https://github.com/harsimrat-eyeem/holy-edge` - Harsimrat Sandhawalia
Input image | Final fused Edge maps | Edge maps from side layers
:-------------------------:|:-------------------------:|:-------------------------:
<img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/35049.jpg" width="480"> | <img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/animated-7.gif" width="480"> | <img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/animated-levels-7.gif" width="480">
<img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/201080.jpg" width="480"> | <img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/animated-9.gif" width="480"> | <img src="https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/example-results/animated-levels-9.gif" width="480">
- Final fused edge maps generated at 100, 500, 1k, 1.5k, 2k, 2.5k, 3k, 3.5k, 4k, 4.5k iterations.
- Edge maps form side layers generated at 5k iterations.
This repository contains tensorflow implementation of the [HED model](https://github.com/s9xie/hed). Details of hyper-paramters are available in the [paper](https://arxiv.org/pdf/1504.06375.pdf)
@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](https://conda.io/docs/using/envs.html#create-an-environment)
```
cd holy-edge
pip install -r requirements.txt
export OMP_NUM_THREADS=1
```
## Setting up
Edit the [config file](https://github.com/harsimrat-eyeem/holy-edge/blob/master/hed/configs/hed.yaml) located at `hed/configs/hed.yaml`. Set the paths below. Make sure the directories exist and you have read/write permissions on them.
The HED model is trained on [augmented training](http://vcl.ucsd.edu/hed/HED-BSDS.tar) 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 or you can fetch a pre-trained HED snapshot provided in the repo to simply generate edgemaps.
Fetch VGG-16 models weights trained on ImageNet via git-lfs
```
git lfs fetch && git lfs pull
md5sum hed/models/vgg16.npy
```
`19ff134af12b6ea0c0ff35664b031ba5 hed/models/vgg16.npy`
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
```
python run-hed.py --download-data --config-file hed/configs/hed.yaml
```
## VGG-16 base model
VGG base model available [here](https://github.com/machrisaa/tensorflow-vgg) is used for producing multi-level features. The model is modified according with Section (3.) of the [paper](https://arxiv.org/pdf/1504.06375.pdf). Deconvolution layers are set with [tf.nn.conv2d_transpose](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). The model uses single deconvolution layer in each side layers. Another implementation uses [stacked](https://github.com/ppwwyyxx/tensorpack/blob/master/examples/HED/hed.py#L35) bilinear deconvolution layers. In this implementation the upsampling parameters are learned while finetuning of the model. Pre-trained weights for [VGG-16](https://mega.nz/#!YU1FWJrA!O1ywiCS2IiOlUExcerpt of 5,128 characters
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