hikariakio/tensorflow-transferlearning

Transfer Learning for 5 classes of flower dataset.

Tensorflow-TransferLearning

Overview

Transfer Learning is a machine learning method where we reuse a model trained on a first dataset called the source dataset as the starting point for training a model on a second dataset called the target dataset.

In this project, the source dataset is a large dataset like ImageNet and the target dataset is a much smaller dataset is 5 flower categories.

Transfer Learning

• Take a slice of layers from a previously trained model.
• Freeze their weights, so as to avoid destroying any of the information they contain during future training rounds on your new dataset.
• Add some new, trainable layers on top of the frozen layers. They will learn to turn the old features into predictions on a new dataset.
• Train the new layers on your new dataset.
• Unfreezing the entire model obtained above and re-training it on the new data with a very low learning rate

The last step is known as Fine Tuning.

Approach

Network Architecture	MobileNetV2
Target Dataset	5 classes of flower dataset
Compiler	SGD optimizer
Fine Tuning	NOT INCLUDED

License

Distributed under the MIT License.

Acknowledgement

• Dataset Images are from tf_flower