Mobile Networks in Keras
Keras implementation of the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
Benefits of Mobile Nets
As explained in the paper, large neural networks can be exorbitant, both in the amount of memory they require to perform predictions, to the actual size of the model weights.
Therefore, by using Depthwise Convolutions, we can reduce a significant portion of the model size while still retaining very good performance.
Creating MobileNets
The default MobileNet corresponds to the model pre-trained on ImageNet. It has an input shape of (224, 224, 3).
from mobilenets import MobileNets
model = MobileNets()
There are two hyperparameters that you can change - alpha (the widening factor), and depth_multiplier. The ImageNet model uses the default values of 1 for both of the above.
from mobilenets import MobileNets
model = MobileNets(alpha=1, depth_multiplier=1)
Testing
The model can be tested by running the predict_imagenet.py script, using the given elephant image. It will return a top 5 prediction score, where "African Elephant" score will be around 97.9%.
| Image | Predictions |
|
('Indian_elephant', 0.97889256), ('African_elephant', 0.014472792), ('tusker', 0.0066345367), ('warthog', 9.1309346e-08), ('ram', 1.1704416e-08) |
Conversion of Tensorflow Weights
The weights were originally from https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md, which used Tensorflow checkpoints.
There are scripts and some documentation for how the weights were converted in the _weight_extraction folder.