Pool1_helper = PoolHelper()( conv1_zero_pad) conv_utils import convert_kernelĭef create_googlenet( weights_path = None): layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation Which will output the predicted class label for the image.įrom keras. Once these are downloaded and moved to the working directory, you can run googlenet.py from the terminal: $ python googlenet.py
Nedit number pad does not work raspbian install#
To run the demo, you will need to install the pre-trained weights and the class labels. Googlenet.py also contains a demo image classification. To create a GoogLeNet model, call the following from within Python: from googlenet import create_googlenet
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Note that in either case, the code requires the channels_first option for image_data_format.
To switch to the Theano backend, change your ~/.keras/keras.json file to You will also need to install the following: pip install pillow numpy imageio The code now runs with Python 3.6, Keras 2.2.4, and either Theano 1.0.4 or Tensorflow 1.14.0. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. GoogLeNet paper: Going deeper with convolutions. I created it by converting the GoogLeNet model from Caffe.
Here is a Keras model of GoogLeNet (a.k.a Inception V1).
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