Fixed VGGNet spec in BDCN description
"cell_type": "markdown",
"metadata": {},
"source": [
- "A BDCN model uses a VGG16 as the base architecture. Each of the five convolutional blocks leads to a pooling layer and then evolves into an Incremental Detection Block (ID Block). These ID Blocks comprise of several convolutional layers followed by a Scale Enhancement Module (SEM). SEM is made of multiple parallel convolution layers using dilation at different rates to create receptive fields at varying scales. Using dilation is an effective way of creating varying receptive fields without increasing the total parameters. ID Blocks eventually provide two edge predictions where one is propagated from shallow to deep layer and another from deep to shallow layer, thus forming a bi-directional cascade structure. The final prediction is a fusion of the intermediate edge map predictions. [1]"
+ "Like HED, BDCN model uses a VGGNet as the base architecture. Each of the five convolutional blocks leads to a pooling layer and then evolves into an Incremental Detection Block (ID Block). These ID Blocks comprise of several convolutional layers followed by a Scale Enhancement Module (SEM). SEM is made of multiple parallel convolution layers using dilation at different rates to create receptive fields at varying scales. Using dilation is an effective way of creating varying receptive fields without increasing the total parameters. ID Blocks eventually provide two edge predictions where one is propagated from shallow to deep layer and another from deep to shallow layer, thus forming a bi-directional cascade structure. The final prediction is a fusion of the intermediate edge map predictions. [1]"
]
},
{
