Liked on YouTube: 290 - Deep Learning based edge detection using HED

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://ift.tt/5ODv29A All other code: https://ift.tt/MxSgzaQ Original HED paper: https://ift.tt/iCqg2VN Caffe model is encoded into two files 1. Proto text file: https://ift.tt/AliNXUy 2. Pretrained caffe model: https://ift.tt/zFSIgnV 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. ​
via YouTube https://www.youtube.com/watch?v=UIrvEG9Oj1s