Methods > Computer Vision > Image Model Blocks

ENet Bottleneck is an image model block used in the ENet semantic segmentation architecture. Each block consists of three convolutional layers: a 1 × 1 projection that reduces the dimensionality, a main convolutional layer, and a 1 × 1 expansion. We place Batch Normalization and PReLU between all convolutions. If the bottleneck is downsampling, a max pooling layer is added to the main branch. Also, the first 1 × 1 projection is replaced with a 2 × 2 convolution with stride 2 in both dimensions. We zero pad the activations, to match the number of feature maps.

Source: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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