Neural Architecture Search: Train the Right Vision Model for Your Hardware

In this video, Grant Nelson, Project Manager at Roboflow, introduces Neural Architecture Search (NAS). A constant challenge for developers is balancing a vision model's speed (latency and cost) with its accuracy. Instead of manually running experiments and guessing the right parameters, NAS automatically generates and evaluates thousands of different architectures to help you find the optimal model for your deployment environment. Watch a live demonstration using a screw-counting dataset from Roboflow Universe to see how to kick off a NAS training run. You'll learn how to filter the top-performing models, evaluate the data, and select the best architecture based on metrics like the F1 score. = Additional Resources = Documentation: https://docs.roboflow.com/train/neural-architecture-search#when-to-use-neural-architecture-search = Chapters = 00:00 Introduction: The Speed vs. Accuracy Trade-off 02:13 What is Neural Architecture Search (NAS)? 04:26 How to Set Up a NAS Training Run 07:03 Understanding the Latency vs. Accuracy Graph 08:05 Filtering and Selecting the Best Model (F1 Score)