The Power of Transfer Learning Unleashed!

3 min readJul 11, 2020

The story of Face Recognition….

Transfer Learning: The reuse of a pre-trained model on a new problem.Transferring of learning which actually means the use of a previously acquired knowledge and skills in new learning or problem-solving situations.

Wikipedia says…..

It is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.

For example,knowledge gained while learning to recognize cars could apply while trying to recognize trucks.

Similar,in this article, I have used the model which usually recognizes different leaves for recognizing faces .

In Deep Learning,It is a technique whereby a neural network model is first trained on a problem similar to the problem that is being solved. One or more layers from the trained model are then used in a new model trained on the problem of interest.

Why is transfer learning useful?

Transfer learning has several benefits, but the main advantages are saving training time, better performance of neural networks (in most cases), and not needing a lot of data.

How does transfer learning work?

Transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task. It is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.

In transfer learning, we first train a base network on a base data-set and task, and then we re-purpose the learned features, or transfer them, to a second target network to be trained on a target data-set and task. This process will tend to work if the features are general, meaning suitable to both base and target tasks, instead of specific to the base task.

Now,Coming to the task,

The first and most important part is “Creating Dataset”.

There are many ways to generate a dataset. I have used “harcasscade” for generating dataset.