A **SqueezeNeXt Block** is a two-stage bottleneck module used in the [SqueezeNeXt](https://paperswithcode.com/method/squeezenext) architecture to reduce the number of input channels to the 3 × 3 [convolution](https://paperswithcode.com/method/convolution). We decompose with separable convolutions to further reduce the number of parameters (orange parts), followed by a 1 × 1 expansion module.

The Fast-YOLOv4-SmallObj model is a modified version of Fast-[YOLOv4](https://paperswithcode.com/method/yolov4) to improve the detection of small objects. Seven layers were added so that it predicts bounding boxes at 3 different scales instead of 2.

Fast-YOLOv4-SmallObj

Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

SqueezeNeXt Block

SqueezeNext: Hardware-Aware Neural Network Design

**Position-Sensitive RoIAlign** is a positive sensitive version of [RoIAlign](https://paperswithcode.com/method/roi-align) - i.e. it performs selective alignment, allowing for the learning of position-sensitive region of interest aligning.

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