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Afroz Chakure
Afroz Chakure

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Transfer Learning with MobileNet-v2

What is Transfer Learning ?

  • In transfer learning, we use a pre-trained model which is trained on a large and general enough dataset to serve as a generic model for our needs.
  • We can use these pre-trained models without having to train a model from scratch on a large dataset.



Find the Jupyter Notebook with Code for Transfer Learning here.

Model used to perform transfer learning - MobileNet V2

  • It is a model developed at Google.
  • It is trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes.

Using the “Bottleneck Layer” for Feature Extraction

  • Here in the cats_vs_dogs dataset we use the very last layer before the flatten operation for feature extraction. This layer is called a ‘bottleneck layer’.
  • The bottleneck layer features retain more generality as compared to the final/top layer.
  • Here we set the include_top=False, so that we load a model that doesn’t include the classification layers at the top.

General Workflow for using a Pre-trained Model:

  1. Examine and understand the data.
  2. Build an input pipeline
  3. Compose a. Loading the pre-trained model and pre-trained weights. b. Stack the classification layers on top.
  4. Train
  5. Evaluate

Advantages of using a pre-trained model for feature extraction:

  • When working with a small dataset, we can take advantage of features learned by a model trained on a larger dataset in the same domain.
  • This is done by instantiating the pre-trained model and adding a fully-connected classifier on top.
  • The pre-trained model is ‘frozen’ and only the weights of the classifier get updated during training.
  • This in turn helps us achieve better accuracy for our model, even if we have a small dataset and perform only fewer computations for training.

Accuracy and loss of model

  • The model has a initial Accuracy of 0.54 and initial loss of 0.63 for the Validation set before training.
  • After training the model on Train set, it has an accuracy of 0.9501 and loss of 0.1020.

Link to Dockerfile:

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