290 - Deep Learning based edge detection using HED

Deep Learning based edge detection using holistically nested edge detection (HED)

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_for_microscopists/tree/master/290-Deep%20Learning%20based%20edge%20detection%20using%20HED

All other code:
https://github.com/bnsreenu/python_for_microscopists

Original HED paper: https://arxiv.org/pdf/1504.06375.pdf

Caffe model is encoded into two files
1. Proto text file: https://github.com/s9xie/hed/blob/master/examples/hed/deploy.prototxt
2. Pretrained caffe model: http://vcl.ucsd.edu/hed/hed_pretrained_bsds.caffemodel
NOTE: In future, if these links do not work, I cannot help. Please Google
and find updated links (information current as of October 2022)

HED is a deep learning model that uses fully convolutional neural networks and deeply-supervised nets to do image-to-image prediction.

The output of earlier layers is called side output.
HED makes use of the side outputs of intermediate layers.
The output of all 5 convolutional layers is fused to generate the final predictions.
Since the feature maps generated at each layer is of different size, it’s effectively looking at the image at different scales.

The model is VGGNet with few modifications:
Side output layer is connected to the last convolutional layer in each stage, respectively conv1_2, conv2_2, conv3_3, conv4_3,conv5_3. The receptive field size of each of these convolutional layers is identical to the corresponding side-output layer.

Last stage of VGGNet is removed including the 5th pooling layer and all the fully connected layers.

The final HED network architecture has 5 stages, with strides 1, 2, 4, 8 and 16, respectively, and with different receptive field sizes, all nested in the VGGNet.