From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
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Solution: Implementation - PyTorch Tutorial
From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
Solution: Implementation
(upbeat music) - [Instructor] Welcome back. In this final solution video, we'll build a comprehensive object detection system that can classify wheat heads by maturity and handle challenging conditions. Let's bring all our skills together for this project. Let's begin by extending our dataset annotations to include maturity stages. For this we'll zoom, our wheat heads are categorized as young, mature, or overripe. In the annotation file, we'll modify our dataset class to load both bonding boxes and maturity labels for each wheat head. This class modification allows us to handle both bounding box and maturity stage information, preparing our data for the dual task training process further. Next, we'll modify your YOLOv5 model to classify each detected wheat head by its maturity stage. This setup involves adjusting the model's output layer to predict both location and maturity class. Here we are updating the model's output layer to predict multiple class based on wheat maturity stages…
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