# 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 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](keras_applications/vgg16.py) | 224 | 71.268 | 90.050 | 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) | 224 | 71.256 | 89.988 | 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) | 224 | 74.928 | 92.060 | 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) | 224 | 76.420 | 92.786 | 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) | 224 | 76.604 | 93.118 | 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) | 299 | 75.960 | 93.034 | 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) | 299 | 77.234 | 93.816 | 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) | 299 | 78.032 | 94.162 | 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) | 224 | 77.740 | 93.810 | 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) | 224 | 78.730 | 94.294 | 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) | 299 | 77.898 | 93.720 | 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) | 299 | 80.256 | 95.252 | 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) | 299 | 79.006 | 94.452 | 22.9M | 20.9M | [[paper]](https://arxiv.org/abs/1610.02357) | | [MobileNet(alpha=0.25)](keras_applications/mobilenet.py) | 224 | 51.582 | 75.792 | 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) | 224 | 64.292 | 85.624 | 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) | 224 | 68.412 | 88.242 | 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) | 224 | 70.424 | 89.504 | 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) | 224 | 60.086 | 82.432 | 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) | 224 | 65.194 | 86.062 | 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) | 224 | 69.532 | 89.176 | 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) | 224 | 71.336 | 90.142 | 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) | 224 | 74.680 | 92.122 | 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) | 224 | 75.230 | 92.422 | 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) | | [MobileNetV3(small)](keras_applications/mobilenet_v3.py) | 224 | 68.076 | 87.800 | 2.6M | 0.9M | [[paper]](https://arxiv.org/abs/1905.02244) [[tf-models]](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet/mobilenet_v3.py) | | [MobileNetV3(large)](keras_applications/mobilenet_v3.py) | 224 | 75.556 | 92.708 | 5.5M | 3.0M | [[paper]](https://arxiv.org/abs/1905.02244) [[tf-models]](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet/mobilenet_v3.py) | | [DenseNet121](keras_applications/densenet.py) | 224 | 74.972 | 92.258 | 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) | 224 | 76.176 | 93.176 | 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) | 224 | 77.320 | 93.620 | 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) | 331 | 82.498 | 96.004 | 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) | 224 | 74.366 | 91.854 | 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) | | [EfficientNet-B0](keras_applications/efficientnet.py) | 224 | 77.190 | 93.492 | 5.3M | 4.0M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B1](keras_applications/efficientnet.py) | 240 | 79.134 | 94.448 | 7.9M | 6.6M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B2](keras_applications/efficientnet.py) | 260 | 80.180 | 94.946 | 9.2M | 7.8M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B3](keras_applications/efficientnet.py) | 300 | 81.578 | 95.676 | 12.3M | 10.8M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B4](keras_applications/efficientnet.py) | 380 | 82.960 | 96.260 | 19.5M | 17.7M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B5](keras_applications/efficientnet.py) | 456 | 83.702 | 96.710 | 30.6M | 28.5M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B6](keras_applications/efficientnet.py) | 528 | 84.082 | 96.898 | 43.3M | 41.0M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | [EfficientNet-B7](keras_applications/efficientnet.py) | 600 | 84.430 | 96.840 | 66.7M | 64.1M | [[paper]](https://arxiv.org/abs/1905.11946) [[tf-tpu]](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | ## 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