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.

**Sharpness-Aware Minimization**, or **SAM**, is a procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness. SAM functions by seeking parameters that lie in neighborhoods having uniformly low loss value (rather than parameters that only themselves have low loss value).

Sharpness-Aware Minimization

Sharpness-Aware Minimization for Efficiently Improving Generalization

Fast-YOLOv4-SmallObj

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

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.

Source	Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com