From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
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Fine-tuning object detection models - PyTorch Tutorial
From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
Fine-tuning object detection models
- [Instructor] Now let's explore how to fine tune object detection models to improve their performance and accuracy on specific data sets. Fine tuning is an essential technique for adapting pre-trained models to new tasks or dataset with unique characteristics. Fine tuning involves taking a pre-trained model and adapting it to a specific dataset. For example, we might have a model that's trained on a large dataset like COCO, but we wanted to perform well on a smaller specialized dataset like the global wheat head dataset. When we fine tune a model, we leverage its learned features while training it further on the new data. This process not only saves time, but also improves the model's performance by focusing on the characteristics of a specific dataset. Let's start by loading a pre-trained object detection model. For this demonstration, we'll use YOLOv5 again. We'll freeze the initial layers to retain the learned features and only update the deeper layers to focus on the specifics of…
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