Transfer_Learning

Transfer learning using inception_resnet_v2

This project obtained from https://github.com/kwotsin/transfer_learning_tutorial enables the application of a transfer learning approach using a inception_resnet_v2 pre-trained model.

Summary

Run create_tfrecord.py to create tfrecords.

Run train_dais.py for training and eval_dais.py for evaluation.

Images are stored in dais\dais_photos

Requirements

1. Python 2.7.x
2. TensorFlow >= 0.12

NOTE: If you want to run this program on Python 3, clone and run git checkout python-3.0 for the Python 3 branch instead.

Arguments

Required arguments:

• dataset_dir (string): The directory to your dataset that is arranged in a structured way where your subdirectories keep classes of your images.

For example:

dais\
    dais_photos\
        cars\
            ....jpg
            ....jpg
            ....jpg
        empty_road\
            ....jpg

Note: Your dataset_dir should be /path/to/dais and not /path/to/dais/dais_photos

• tfrecord_filename (string): The output name of your TFRecord files.