Labeling images using transfer learning

Transfer learning is a machine learning technique where a model trained on one task is adapted for a second related task. Instead of starting the learning process from scratch, transfer learning leverages knowledge gained from solving one problem and applies it to a different but related problem. This approach has become increasingly popular in deep learning and has several advantages:

• Faster training: Transfer learning can significantly reduce the time and computational resources required to train a model. Instead of training a deep neural network from random initialization, you start with a pre-trained model, which already has learned features and representations.
• Better generalization: Models pre-trained on large datasets, such as ImageNet for image recognition, have learned general features that are useful for various related tasks. These features tend to generalize well to new tasks, leading to better performance.
• Lower data...