Active Learning Strategies Compared for YOLOv8 on Lincolnbeet

Learn how different data selection strategies impact model accuracy. We use the lincolnbeet dataset and YOLOv8 model for our experiments.

Agriculture is one of the domains that could benefit a lot from recent breakthroughs in computer vision. Having machines that can analyze millions of crops throughout the year to optimise yield and minimise the required amount of pesticides that are required has a big impact!

We show how ML teams can save up to 77% of labeling costs or improve the model by up to 14.6x per additional labeled batch when using active learning compared to random selection!

We take a closer look at one application of computer vision in agriculture: Using robots equipped with cameras to optimise precision spraying of weeds on large fields of crops. In this example we use the lincolnbeet dataset and set out with the goal of building a reliable computer vision system.

This showcase aims to illustrate how using a smart data selection strategy like active learning yields significant benefits compared to random selection. We show how ML teams can save up to 77% of labeling costs or improve the model by up to 14.6x per additional labeled batch when using Active Learning compared to random selection!

For benchmarking different data selection strategies, we will use Lightly. Lightly…