Reference implementations of popular deep learning models.
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toddrme2178 and taehoonlee Include license and tests in sdists (#51)
The license requires all copies of the program include a copy of the license.  And the tests are useful for downstreams, such as linux packagers, to make sure everything is running correctly.

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Latest commit 4cef245 Nov 15, 2018

Keras Applications

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Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular archictures, such as VGG16, ResNet50, Xception, MobileNet, and more.

Read the documentation at:

Keras Applications may be imported directly from an up-to-date installation of Keras:

from keras import applications

Keras Applications is compatible with Python 2.7-3.6 and is distributed under the MIT license.


  • The top-k errors were obtained using Keras Applications with the TensorFlow backend on the 2012 ILSVRC ImageNet validation set and may slightly differ from the original ones. The input size used was 224x224 for all models except NASNetLarge (331x331), InceptionV3 (299x299), InceptionResNetV2 (299x299), and Xception (299x299).
    • Top-1: single center crop, top-1 error
    • Top-5: single center crop, top-5 error
    • 10-5: ten crops (1 center + 4 corners and those mirrored ones), top-5 error
    • Size: rounded the number of parameters when include_top=True
    • Stem: rounded the number of parameters when include_top=False
Top-1 Top-5 10-5 Size Stem References
VGG16 28.732 9.950 8.834 138.4M 14.7M [paper] [tf-models]
VGG19 28.744 10.012 8.774 143.7M 20.0M [paper] [tf-models]
ResNet50 25.072 7.940 6.828 25.6M 23.6M [paper] [tf-models]
InceptionV3 22.102 6.280 5.038 23.9M 21.8M [paper] [tf-models]
InceptionResNetV2 19.744 4.748 3.962 55.9M 54.3M [paper] [tf-models]
Xception 20.994 5.548 4.738 22.9M 20.9M [paper]
MobileNet(alpha=0.25) 48.418 24.208 21.196 0.5M 0.2M [paper] [tf-models]
MobileNet(alpha=0.50) 35.708 14.376 12.180 1.3M 0.8M [paper] [tf-models]
MobileNet(alpha=0.75) 31.588 11.758 9.878 2.6M 1.8M [paper] [tf-models]
MobileNet(alpha=1.0) 29.576 10.496 8.774 4.3M 3.2M [paper] [tf-models]
MobileNetV2(alpha=0.35) 39.914 17.568 15.422 1.7M 0.4M [paper] [tf-models]
MobileNetV2(alpha=0.50) 34.806 13.938 11.976 2.0M 0.7M [paper] [tf-models]
MobileNetV2(alpha=0.75) 30.468 10.824 9.188 2.7M 1.4M [paper] [tf-models]
MobileNetV2(alpha=1.0) 28.664 9.858 8.322 3.5M 2.3M [paper] [tf-models]
MobileNetV2(alpha=1.3) 25.320 7.878 6.728 5.4M 3.8M [paper] [tf-models]
MobileNetV2(alpha=1.4) 24.770 7.578 6.518 6.2M 4.4M [paper] [tf-models]
DenseNet121 25.028 7.742 6.522 8.1M 7.0M [paper] [torch]
DenseNet169 23.824 6.824 5.860 14.3M 12.6M [paper] [torch]
DenseNet201 22.680 6.380 5.466 20.2M 18.3M [paper] [torch]
NASNetLarge 17.502 3.996 3.412 93.5M 84.9M [paper] [tf-models]
NASNetMobile 25.634 8.146 6.758 7.7M 4.3M [paper] [tf-models]

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