ENet Bottleneck

Introduced by Paszke et al. in ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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

Read Paper See Code

Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Semantic Segmentation	12	26.09%
Autonomous Driving	5	10.87%
Quantization	2	4.35%
Autonomous Vehicles	2	4.35%
Real-Time Semantic Segmentation	2	4.35%
Instance Segmentation	2	4.35%
Scene Understanding	2	4.35%
Optical Character Recognition (OCR)	1	2.17%
Multi-Task Learning	1	2.17%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Convolution	Convolutions
Max Pooling	Pooling Operations
PReLU	Activation Functions
SpatialDropout	Regularization

Categories

Add Remove

Image Model Blocks