/Fine-tuning-and-feature-extraction-from-pretrained-models

In this example, we use the pre-trained ResNet50 model, which is pretrained on the ImageNet dataset. The implementation is in TensorFlow-Keras.

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Fine-tuning-and-feature-extraction-from-pretrained-models

In this example, we use the pre-trained ResNet50 and VGG16 model, which is pretrained on the ImageNet dataset. The implementation is in TensorFlow-Keras.

  1. We download the weights from "https://github.com/fchollet/deep-learning-models/releases" (resnet50_weights_tf_dim_ordering_tf_kernels_notop_updated.h5).
  2. We set the paths to the train, validation and test files, with respect to our local system path.
  3. Using the pretrained Resnet50 model, we fine-tune the model according to our own dataset, and then extract the features from the entire dataset.
  4. We save the features as a .Mat file.

Requirements-

  1. Tensorflow
  2. Numpy
  3. Scipy.io

Happy coding!!