Transfer learning in computer vision involves leveraging pre-trained models, typically trained on large datasets like ImageNet, to solve new, related tasks with smaller datasets. Instead of training a model from scratch, the pre-trained model's knowledge is fine-tuned for the target task by adjusting some or all of its layers. This approach significantly reduces training time and improves performance, especially when data is limited. Transfer learning has proven highly effective in tasks like object detection, image classification, and segmentation, where the features learned from one task can be applied to another.
Transfer learning can be structured into three core sections. First, introduce the concept by explaining how transfer learning allows leveraging pre-trained models to solve new tasks with limited data, highlighting its benefits in computer vision applications like image classification. Next, focus on popular pre-trained models such as ResNet and VGG, discussing how to fine-tune them by freezing certain layers and adjusting hyperparameters for specific tasks. Finally, cover practical implementation using a framework like PyTorch, along with real-world examples and best practices for successfully applying transfer learning in various computer vision tasks. Here are some resources to understand this topic:
The content mentioned in this section is to be covered within 3 days time (19th October - 21st October) and the 3rd assignment is due on 21st October, 11:59 P.M.
Code Files: https://drive.google.com/file/d/1hvWpQtOfnKOYaxvygN9_sRRMf5x_smF4/view?usp=sharing
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