**DenseNAS-A** is a mobile convolutional neural network discovered through the [DenseNAS](https://paperswithcode.com/method/densenas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

F2DNet, a novel two-stage object detection architecture which eliminates redundancy of classical two-stage detectors by replacing the region proposal network with focal detection network and
bounding box head with fast suppression head.

F2DNet

F2DNet: Fast Focal Detection Network for Pedestrian Detection

DenseNAS-A

Densely Connected Search Space for More Flexible Neural Architecture Search

An **LR-Net** is a type of non-convolutional neural network that utilises local relation layers instead of convolutions for image feature extraction. Otherwise, the architecture follows the same design as a [ResNet](https://paperswithcode.com/method/resnet).

Source	Densely Connected Search Space for More Flexible Neural Architecture Search
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com