**SqueezeNeXt** is a type of convolutional neural network that uses the [SqueezeNet](https://paperswithcode.com/method/squeezenet) architecture as a baseline, but makes a number of changes. First, a more aggressive channel reduction is used by incorporating a two-stage squeeze module. This significantly reduces the total number of parameters used with the 3×3 convolutions. Secondly, it uses separable 3 × 3 convolutions to further reduce the model size, and removes the additional 1×1 branch after the squeeze module. Thirdly, the network use an element-wise addition skip connection similar to that of [ResNet](https://paperswithcode.com/method/resnet) architecture.

**Dilated Bottleneck with Projection Block** is an image model block used in the [DetNet](https://paperswithcode.com/method/detnet) convolutional neural network architecture. It employs a bottleneck structure with dilated convolutions to efficiently enlarge the receptive field. It uses a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) to ensure the spatial size stays fixed.

Dilated Bottleneck with Projection Block

DetNet: A Backbone network for Object Detection

SqueezeNeXt

SqueezeNext: Hardware-Aware Neural Network Design

**DetNASNet** is a convolutional neural network designed to be an object detection backbone and discovered through [DetNAS](https://paperswithcode.com/method/detnas) architecture search. It uses [ShuffleNet V2](https://paperswithcode.com/method/shufflenet-v2) blocks as its basic building block.

Source	SqueezeNext: Hardware-Aware Neural Network Design
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com