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	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]