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.
- The top-k errors were obtained using by Keras Applications with TensorFlow backend on ImageNet validation set and may slightly differ from the original ones. The crop size is 224x224 for all but 331x331 for NASNetLarge, 299x299 for InceptionV3, InceptionResNetV2, Xception.
- 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 | 25.296 | 7.980 | 6.852 | 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) | 60.180 | 35.388 | 30.442 | 0.5M | 0.2M | [paper] [tf-models] |
| MobileNet(alpha=0.50) | 43.144 | 19.986 | 16.416 | 1.3M | 0.8M | [paper] [tf-models] |
| MobileNet(alpha=0.75) | 38.404 | 16.752 | 13.586 | 2.6M | 1.8M | [paper] [tf-models] |
| MobileNet(alpha=1.0) | 34.180 | 13.858 | 10.798 | 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 | 25.480 | 8.022 | 6.842 | 8.1M | 7.0M | [paper] [torch] |
| DenseNet169 | 23.926 | 6.892 | 6.140 | 14.3M | 12.6M | [paper] [torch] |
| DenseNet201 | 22.936 | 6.542 | 5.724 | 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] |
- SSD by @rykov8 [paper]
- YOLOv2 by @allanzelener [paper]
- YOLOv3 by @qqwweee [paper]
- Mask RCNN by @matterport [paper]
- U-Net by @zhixuhao [paper]
- RetinaNet by @fizyr [paper]
- keras-rl by @keras-rl
- RocAlphaGo by @Rochester-NRT [paper]
- Keras-GAN by @eriklindernoren