GoogLeNet in Keras

Keras model of GoogLeNet (a.k.a Inception V1).

GoogLeNet paper:

Going deeper with convolutions.
Szegedy, Christian, et al. 
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

Requirements

The code now runs with Python 3.6, Keras 2.2.4, and either Theano 1.0.4 or Tensorflow 1.14.0. You will also need to install the following:

pip install pillow numpy imageio

To switch to the Theano backend, change your ~/.keras/keras.json file to

{"epsilon": 1e-07, "floatx": "float32", "backend": "theano", "image_data_format": "channels_first"}

Or for the Tensorflow backend,

{"epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow", "image_data_format": "channels_first"}

Note that in either case, the code requires the channels_first option for image_data_format.

Running the Demo (googlenet.py)

To create a GoogLeNet model, call the following from within Python:

from googlenet import create_googlenet
model = create_googlenet()

googlenet.py also contains a demo image classification. To run the demo, you will need to install the pre-trained weights and the class labels. You will also need this test image. Once these are downloaded and moved to the working directory, you can run googlenet.py from the terminal:

$ python googlenet.py

which will output the predicted class label for the image.