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.
- 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.
| Network Architecture | MobileNetV2 |
| Target Dataset | 5 classes of flower dataset |
| Compiler | SGD optimizer |
| Fine Tuning | NOT INCLUDED |
Distributed under the MIT License.
- Dataset Images are from tf_flower