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Transfer Learning with MobileNet
Deep learning has revolutionized the field of computer vision by enabling machines to understand and interpret images. However, training deep neural networks from scratch can be computationally expensive and require a vast amount of labeled data. This is where transfer learning comes to the rescue. Transfer learning leverages pre-trained models that have been trained on large datasets to solve similar problems. This approach not only saves time but also benefits from the learned features of the pre-trained models.
In this tutorial, we will dive into the world of transfer learning and explore two powerful pre-trained models: MobileNet and VGG16. We will use these models to tackle a fascinating problem: classifying different types of fruits and vegetables from the Kaggle dataset. By the end of this guide, you’ll have a solid understanding of how to harness the power of transfer learning and apply it to real-world image classification tasks.
Why Transfer Learning?
Transfer learning has gained immense popularity in the deep learning community due to its ability to accelerate model development and improve performance, even with limited data. Rather than starting from scratch, transfer learning allows us to leverage the feature extraction capabilities of models that have been trained on large and diverse datasets. These features can be fine-tuned and adapted to new tasks, making it especially useful when you have a relatively small dataset.