Transfer Learning for Image Classification

This project uses transfer learning, a powerful technique for leveraging pre-trained deep learning models to solve new problems efficiently. By using a model already trained on a massive dataset, we can save significant time and improve performance. This notebook demonstrates this by fine-tuning a pre-trained convolutional neural network (CNN) to classify a small dataset of flower images and compares the results to a model trained from scratch.

Dataset

The project uses the flower_photos dataset, which contains images of five different flower types. This small dataset is ideal for showing how transfer learning can achieve high accuracy with minimal training.

Technologies Used

• Python

• TensorFlow: The primary framework for building and training the model.
• TensorFlow Hub: A library for reusable machine learning modules.

• Matplotlib: For visualizing the results.

• Running the Notebook

To run this project, simply open the transfer learning.ipynb notebook in a Jupyter environment and execute all the cells. The notebook is self-contained and will handle all the necessary data downloads and model training steps.