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: github.com/bnsreenu/python_for_microscopists/tree/… All other code: github.com/bnsreenu/python_for_microscopists Original HED paper: arxiv.org/pdf/1504.06375.pdf Caffe model is encoded into two files 1. Proto text file: github.com/s9xie/hed/blob/master/examples/hed/depl… 2. Pretrained caffe model: 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. ​

• #science_technology
• #microscopy
• #python
• #image_processing