**FBNet Block** is an image model block used in the [FBNet](https://paperswithcode.com/method/fbnet) architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). The basic building blocks employed are [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution) and a [residual connection](https://paperswithcode.com/method/residual-connection).

**YOLOv3** is a real-time, single-stage object detection model that builds on [YOLOv2](https://paperswithcode.com/method/yolov2) with several improvements. Improvements include the use of a new backbone network, [Darknet-53](https://paperswithcode.com/method/darknet-53) that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an [FPN](https://paperswithcode.com/method/fpn)).

YOLOv3

YOLOv3: An Incremental Improvement

FBNet Block

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):

$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$

For **RegNetX** we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).

For **RegNetY** we make one change, which is to include Squeeze-and-Excitation blocks.

Source	FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com