Hands-On Image Processing with Python

DeepLab V3+

DeepLab presents an architecture for controlling signal decimation and learning multi-scale contextual features. DeepLab uses an ResNet-50 model, pre-trained on the ImageNet dataset, as its main feature extractor network. However, it proposes a new residual block for multi-scale feature learning, as shown in the following diagram. Instead of regular convolutions, the last ResNet block uses atrous convolutions. Also, each convolution (within this new block) uses different dilation rates to capture multi-scale context. Additionally, on top of this new block, it uses Atrous Spatial Pyramid Pooling (ASPP). ASPP uses dilated convolutions with different rates as an attempt of classifying regions of an arbitrary scale. Hence, the DeepLab ...