From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
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Transfer learning in object detection - PyTorch Tutorial
From the course: Deep Learning and Computer Vision: Object Detection with PyTorch
Transfer learning in object detection
- [Instructor] Great job so far. Now let's take a look at the concept of transfer learning to see how it applies to object detection. Transfer learning is a powerful technique in deep learning that allows us to use pre-trained models as a starting point for new task, rather than training a model from scratch. This not only saves time, but also enhances performance. Transfer learning involves taking a model that has been trained on a large generic data set and adapting it to a specific task. The idea is to leverage the knowledge gained during training on the original data set to improve the model's performance on a new related task, thus making things easy and fast. For example, a model trained on a large dataset like COCO or ImageNet already knows how to identify common features like edges, textures, and basic shapes. By reusing this knowledge, we can focus on training the model for a specific task or dataset, which usually involves fine tuning the later layers of the network…