YOLO-NAS: How to Achieve the Best Performance on Object Detection Tasks

A foundational model generated through neural architecture search, innovative quantization blocks, and a robust pre-training paradigm

Photo by Anubhav Saxena on Unsplash. Processed with YOLO-NAS-L by the author.

In the domain of object detection, YOLO (You Only Look Once) has become a household name. Since the release of the first model in 2015, the YOLO family has been growing steadily, with each new model outperforming its predecessor in mean average precision (mAP) and inference latency.

Two weeks ago, the YOLO family has welcomed yet another member: YOLO-NAS, a novel and foundational model developed by the deep learning company Deci.

In this article, we’ll explore its advantages over previous YOLO models and demonstrate how it can be used for your own object detection tasks.

YOLO-NAS: What’s New?

While previous YOLO models were leading in innovation and performance when it comes to object detection, they did have some limitations. One of the main issues was the lack of proper quantization support, which aims to decrease the model’s memory and computation requirements. Another issue was the insufficient trade-off between accuracy and latency, whereby an improvement in one often resulted in a…