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https://github.com/titu1994/mobilenetworks
Keras implementation of Mobile Networks
https://github.com/titu1994/mobilenetworks
imagenet keras mobilenets neural-network tensorflow
Last synced: 9 days ago
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Keras implementation of Mobile Networks
- Host: GitHub
- URL: https://github.com/titu1994/mobilenetworks
- Owner: titu1994
- License: apache-2.0
- Created: 2017-06-12T22:32:02.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-10-05T03:44:23.000Z (about 6 years ago)
- Last Synced: 2024-10-13T14:17:03.771Z (23 days ago)
- Topics: imagenet, keras, mobilenets, neural-network, tensorflow
- Language: Python
- Size: 27.9 MB
- Stars: 132
- Watchers: 9
- Forks: 43
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Mobile Networks (V1 and V2) in Keras
Keras implementation of the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf) + ported weights.
Contains the Keras implementation of the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) + ported weights.
# 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).
You can now create either the original version of MobileNet or the MobileNetV2 recently released using the appropriate method.
```
from mobilenets import MobileNet, MobileNetV2
# for V1
model = MobileNet()
# for V2
model = MobileNetV2()
```
## MobileNet V1
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 MobileNet
model = MobileNet(alpha=1, depth_multiplier=1)
```
## MobileNet V2
There are three hyperparameters that you can change - `alpha` (the widening factor), `expansion_factor` (multiplier by which the inverted residual block is multiplied) and `depth_multiplier`. The ImageNet model uses the default values of 1 for `alpha` and `depth_multiplied` and a default of 6 for `expansion_factor`.
```
from mobilenets import MobileNetV2
model = MobileNetV2(alpha=1, expansion_factor=6, 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
('African_elephant', 0.814673136),
('tusker', 0.15983042),
('Indian_elephant', 0.025479317),
('Weimaraner', 6.0817301e-06),
('bison', 3.7597524e-06)
('cheetah', 0.99743026),
('leopard', 0.0010753422),
('lion', 0.00069186132),
('snow_leopard', 0.00059767498),
('lynx', 0.00012871811)
## 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.
The weights for V2 model were originally from https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet, which used Tensorflow checkpoints. There are scripts and some documentation for how the weights were converted in the `_weight_extraction_v2` folder.