# Keras Applications
[](https://travis-ci.org/keras-team/keras-applications)
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 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](keras_applications/vgg16.py) | 28.732 | 9.950 | 8.834 | 138.4M | 14.7M | [[paper]](https://arxiv.org/abs/1409.1556) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py) |
| [VGG19](keras_applications/vgg19.py) | 28.744 | 10.012 | 8.774 | 143.7M | 20.0M | [[paper]](https://arxiv.org/abs/1409.1556) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py) |
| [ResNet50](keras_applications/resnet50.py) | 25.072 | 7.940 | 6.828 | 25.6M | 23.6M | [[paper]](https://arxiv.org/abs/1512.03385) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua) [[caffe]](https://github.com/KaimingHe/deep-residual-networks/blob/master/prototxt/ResNet-50-deploy.prototxt) |
| [ResNet101](keras_applications/resnet.py) | 23.580 | 7.214 | 6.092 | 44.7M | 42.7M | [[paper]](https://arxiv.org/abs/1512.03385) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua) [[caffe]](https://github.com/KaimingHe/deep-residual-networks/blob/master/prototxt/ResNet-101-deploy.prototxt) |
| [ResNet152](keras_applications/resnet.py) | 23.396 | 6.882 | 5.908 | 60.4M | 58.4M | [[paper]](https://arxiv.org/abs/1512.03385) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua) [[caffe]](https://github.com/KaimingHe/deep-residual-networks/blob/master/prototxt/ResNet-152-deploy.prototxt) |
| [ResNet50V2](keras_applications/resnet_v2.py) | 24.040 | 6.966 | 5.896 | 25.6M | 23.6M | [[paper]](https://arxiv.org/abs/1603.05027) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua) |
| [ResNet101V2](keras_applications/resnet_v2.py) | 22.766 | 6.184 | 5.158 | 44.7M | 42.6M | [[paper]](https://arxiv.org/abs/1603.05027) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua) |
| [ResNet152V2](keras_applications/resnet_v2.py) | 21.968 | 5.838 | 4.900 | 60.4M | 58.3M | [[paper]](https://arxiv.org/abs/1603.05027) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py) [[torch]](https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua) |
| [ResNeXt50](keras_applications/resnext.py) | 22.260 | 6.190 | 5.410 | 25.1M | 23.0M | [[paper]](https://arxiv.org/abs/1611.05431) [[torch]](https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua) |
| [ResNeXt101](keras_applications/resnext.py) | 21.270 | 5.706 | 4.842 | 44.3M | 42.3M | [[paper]](https://arxiv.org/abs/1611.05431) [[torch]](https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua) |
| [InceptionV3](keras_applications/inception_v3.py) | 22.102 | 6.280 | 5.038 | 23.9M | 21.8M | [[paper]](https://arxiv.org/abs/1512.00567) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |
| [InceptionResNetV2](keras_applications/inception_resnet_v2.py) | 19.744 | 4.748 | 3.962 | 55.9M | 54.3M | [[paper]](https://arxiv.org/abs/1602.07261) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |
| [Xception](keras_applications/xception.py) | 20.994 | 5.548 | 4.738 | 22.9M | 20.9M | [[paper]](https://arxiv.org/abs/1610.02357) |
| [MobileNet(alpha=0.25)](keras_applications/mobilenet.py) | 48.418 | 24.208 | 21.196 | 0.5M | 0.2M | [[paper]](https://arxiv.org/abs/1704.04861) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py) |
| [MobileNet(alpha=0.50)](keras_applications/mobilenet.py) | 35.708 | 14.376 | 12.180 | 1.3M | 0.8M | [[paper]](https://arxiv.org/abs/1704.04861) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py) |
| [MobileNet(alpha=0.75)](keras_applications/mobilenet.py) | 31.588 | 11.758 | 9.878 | 2.6M | 1.8M | [[paper]](https://arxiv.org/abs/1704.04861) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py) |
| [MobileNet(alpha=1.0)](keras_applications/mobilenet.py) | 29.576 | 10.496 | 8.774 | 4.3M | 3.2M | [[paper]](https://arxiv.org/abs/1704.04861) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py) |
| [MobileNetV2(alpha=0.35)](keras_applications/mobilenet_v2.py) | 39.914 | 17.568 | 15.422 | 1.7M | 0.4M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [MobileNetV2(alpha=0.50)](keras_applications/mobilenet_v2.py) | 34.806 | 13.938 | 11.976 | 2.0M | 0.7M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [MobileNetV2(alpha=0.75)](keras_applications/mobilenet_v2.py) | 30.468 | 10.824 | 9.188 | 2.7M | 1.4M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [MobileNetV2(alpha=1.0)](keras_applications/mobilenet_v2.py) | 28.664 | 9.858 | 8.322 | 3.5M | 2.3M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [MobileNetV2(alpha=1.3)](keras_applications/mobilenet_v2.py) | 25.320 | 7.878 | 6.728 | 5.4M | 3.8M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [MobileNetV2(alpha=1.4)](keras_applications/mobilenet_v2.py) | 24.770 | 7.578 | 6.518 | 6.2M | 4.4M | [[paper]](https://arxiv.org/abs/1801.04381) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py) |
| [DenseNet121](keras_applications/densenet.py) | 25.028 | 7.742 | 6.522 | 8.1M | 7.0M | [[paper]](https://arxiv.org/abs/1608.06993) [[torch]](https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) |
| [DenseNet169](keras_applications/densenet.py) | 23.824 | 6.824 | 5.860 | 14.3M | 12.6M | [[paper]](https://arxiv.org/abs/1608.06993) [[torch]](https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) |
| [DenseNet201](keras_applications/densenet.py) | 22.680 | 6.380 | 5.466 | 20.2M | 18.3M | [[paper]](https://arxiv.org/abs/1608.06993) [[torch]](https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) |
| [NASNetLarge](keras_applications/nasnet.py) | 17.502 | 3.996 | 3.412 | 93.5M | 84.9M | [[paper]](https://arxiv.org/abs/1707.07012) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py) |
| [NASNetMobile](keras_applications/nasnet.py) | 25.634 | 8.146 | 6.758 | 7.7M | 4.3M | [[paper]](https://arxiv.org/abs/1707.07012) [[tf-models]](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py) |
## Reference implementations from the community
### Object detection and segmentation
- [SSD](https://github.com/rykov8/ssd_keras) by @rykov8 [[paper]](https://arxiv.org/abs/1512.02325)
- [YOLOv2](https://github.com/allanzelener/YAD2K) by @allanzelener [[paper]](https://arxiv.org/abs/1612.08242)
- [YOLOv3](https://github.com/qqwweee/keras-yolo3) by @qqwweee [[paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
- [Mask RCNN](https://github.com/matterport/Mask_RCNN) by @matterport [[paper]](https://arxiv.org/abs/1703.06870)
- [U-Net](https://github.com/zhixuhao/unet) by @zhixuhao [[paper]](https://arxiv.org/abs/1505.04597)
- [RetinaNet](https://github.com/fizyr/keras-retinanet) by @fizyr [[paper]](https://arxiv.org/abs/1708.02002)
### Sequence learning
- [Seq2Seq](https://github.com/farizrahman4u/seq2seq) by @farizrahman4u
- [WaveNet](https://github.com/basveeling/wavenet) by @basveeling [[paper]](https://arxiv.org/abs/1609.03499)
### Reinforcement learning
- [keras-rl](https://github.com/keras-rl/keras-rl) by @keras-rl
- [RocAlphaGo](https://github.com/Rochester-NRT/RocAlphaGo) by @Rochester-NRT [[paper]](https://doi.org/10.1038/nature16961)
### Generative adversarial networks
- [Keras-GAN](https://github.com/eriklindernoren/Keras-GAN) by @eriklindernoren