Hello trend,
This is Satya Mallick from LearnOpenCV.com.
YOLO-NAS is a new real-time state-of-the-art object detection model that outperforms both YOLOv6 & YOLOv8 models in terms of mAP (mean average precision) and inference latency.
According to Deci.ai team,
The NAS stands for Neural Architecture Search which is a technique used to automate the design of Neural Network architecture. It employs optimization algorithms to discover the most suitable architecture for a given task. NAS aims to find an architecture that achieves the best trade-off between accuracy, computational complexity, and model size.
In today's blog post, you will learn about the architecture of YOLO-NAS, how it was trained, what dataset was used, how it compares with the other YOLO models, and how to perform inference using Google Colab. So without further ado, let's jump into the post
| Understanding and Using YOLO-NAS Object Detector |
Accompanying code for the blog post can be found here:
| Download Code |
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Cheers,
Satya
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