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Co-authored-by: Aleksandr <slink21@mail.ru>
Co-authored-by: Taehoon Lee <me@taehoonlee.com>
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README.md

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: https://keras.io/applications/

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.

Performance

  • The top-k accuracies were obtained using Keras Applications with the TensorFlow backend on the 2012 ILSVRC ImageNet validation set and may slightly differ from the original ones.
    • Input: input size fed into models
    • Top-1: single center crop, top-1 accuracy
    • Top-5: single center crop, top-5 accuracy
    • Size: rounded the number of parameters when include_top=True
    • Stem: rounded the number of parameters when include_top=False
Input Top-1 Top-5 Size Stem References
VGG16 224 71.268 90.050 138.4M 14.7M [paper] [tf-models]
VGG19 224 71.256 89.988 143.7M 20.0M [paper] [tf-models]
ResNet50 224 74.928 92.060 25.6M 23.6M [paper] [tf-models] [torch] [caffe]
ResNet101 224 76.420 92.786 44.7M 42.7M [paper] [tf-models] [torch] [caffe]
ResNet152 224 76.604 93.118 60.4M 58.4M [paper] [tf-models] [torch] [caffe]
ResNet50V2 299 75.960 93.034 25.6M 23.6M [paper] [tf-models] [torch]
ResNet101V2 299 77.234 93.816 44.7M 42.6M [paper] [tf-models] [torch]
ResNet152V2 299 78.032 94.162 60.4M 58.3M [paper] [tf-models] [torch]
ResNeXt50 224 77.740 93.810 25.1M 23.0M [paper] [torch]
ResNeXt101 224 78.730 94.294 44.3M 42.3M [paper] [torch]
InceptionV3 299 77.898 93.720 23.9M 21.8M [paper] [tf-models]
InceptionResNetV2 299 80.256 95.252 55.9M 54.3M [paper] [tf-models]
Xception 299 79.006 94.452 22.9M 20.9M [paper]
MobileNet(alpha=0.25) 224 51.582 75.792 0.5M 0.2M [paper] [tf-models]
MobileNet(alpha=0.50) 224 64.292 85.624 1.3M 0.8M [paper] [tf-models]
MobileNet(alpha=0.75) 224 68.412 88.242 2.6M 1.8M [paper] [tf-models]
MobileNet(alpha=1.0) 224 70.424 89.504 4.3M 3.2M [paper] [tf-models]
MobileNetV2(alpha=0.35) 224 60.086 82.432 1.7M 0.4M [paper] [tf-models]
MobileNetV2(alpha=0.50) 224 65.194 86.062 2.0M 0.7M [paper] [tf-models]
MobileNetV2(alpha=0.75) 224 69.532 89.176 2.7M 1.4M [paper] [tf-models]
MobileNetV2(alpha=1.0) 224 71.336 90.142 3.5M 2.3M [paper] [tf-models]
MobileNetV2(alpha=1.3) 224 74.680 92.122 5.4M 3.8M [paper] [tf-models]
MobileNetV2(alpha=1.4) 224 75.230 92.422 6.2M 4.4M [paper] [tf-models]
MobileNetV3(small) 224 68.076 87.800 2.6M 0.9M [paper] [tf-models]
MobileNetV3(large) 224 75.556 92.708 5.5M 3.0M [paper] [tf-models]
DenseNet121 224 74.972 92.258 8.1M 7.0M [paper] [torch]
DenseNet169 224 76.176 93.176 14.3M 12.6M [paper] [torch]
DenseNet201 224 77.320 93.620 20.2M 18.3M [paper] [torch]
NASNetLarge 331 82.498 96.004 93.5M 84.9M [paper] [tf-models]
NASNetMobile 224 74.366 91.854 7.7M 4.3M [paper] [tf-models]
EfficientNet-B0 224 77.190 93.492 5.3M 4.0M [paper] [tf-tpu]
EfficientNet-B1 240 79.134 94.448 7.9M 6.6M [paper] [tf-tpu]
EfficientNet-B2 260 80.180 94.946 9.2M 7.8M [paper] [tf-tpu]
EfficientNet-B3 300 81.578 95.676 12.3M 10.8M [paper] [tf-tpu]
EfficientNet-B4 380 82.960 96.260 19.5M 17.7M [paper] [tf-tpu]
EfficientNet-B5 456 83.702 96.710 30.6M 28.5M [paper] [tf-tpu]
EfficientNet-B6 528 84.082 96.898 43.3M 41.0M [paper] [tf-tpu]
EfficientNet-B7 600 84.430 96.840 66.7M 64.1M [paper] [tf-tpu]

Reference implementations from the community

Object detection and segmentation

Sequence learning

Reinforcement learning

Generative adversarial networks

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