keras EfficientNet介绍，在ImageNet任务上涨点明显 | keras efficientnet introduction

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keras efficientnet introduction

Guide

About EfficientNet Models

compared with resnet50, EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint.

Using Pretrained EfficientNet Checkpoints

Keras Models 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), Xception (299x299),
EfficientNet-B0 (224x224), EfficientNet-B1 (240x240), EfficientNet-B2 (260x260), EfficientNet-B3 (300x300), EfficientNet-B4 (380x380), EfficientNet-B5 (456x456), EfficientNet-B6 (528x528), and EfficientNet-B7 (600x600).

notice

• 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	25.072	7.940	6.828	25.6M	23.6M	[paper] [tf-models] [torch] [caffe]
ResNet101	23.580	7.214	6.092	44.7M	42.7M	[paper] [tf-models] [torch] [caffe]
ResNet152	23.396	6.882	5.908	60.4M	58.4M	[paper] [tf-models] [torch] [caffe]
ResNet50V2	24.040	6.966	5.896	25.6M	23.6M	[paper] [tf-models] [torch]
ResNet101V2	22.766	6.184	5.158	44.7M	42.6M	[paper] [tf-models] [torch]
ResNet152V2	21.968	5.838	4.900	60.4M	58.3M	[paper] [tf-models] [torch]
ResNeXt50	22.260	6.190	5.410	25.1M	23.0M	[paper] [torch]
ResNeXt101	21.270	5.706	4.842	44.3M	42.3M	[paper] [torch]
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)	48.418	24.208	21.196	0.5M	0.2M	[paper] [tf-models]
MobileNet(alpha=0.50)	35.708	14.376	12.180	1.3M	0.8M	[paper] [tf-models]
MobileNet(alpha=0.75)	31.588	11.758	9.878	2.6M	1.8M	[paper] [tf-models]
MobileNet(alpha=1.0)	29.576	10.496	8.774	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	25.028	7.742	6.522	8.1M	7.0M	[paper] [torch]
DenseNet169	23.824	6.824	5.860	14.3M	12.6M	[paper] [torch]
DenseNet201	22.680	6.380	5.466	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]
EfficientNet-B0	22.810	6.508	5.858	5.3M	4.0M	[paper] [tf-tpu]
EfficientNet-B1	20.866	5.552	5.050	7.9M	6.6M	[paper] [tf-tpu]
EfficientNet-B2	19.820	5.054	4.538	9.2M	7.8M	[paper] [tf-tpu]
EfficientNet-B3	18.422	4.324	3.902	12.3M	10.8M	[paper] [tf-tpu]
EfficientNet-B4	17.040	3.740	3.344	19.5M	17.7M	[paper] [tf-tpu]
EfficientNet-B5	16.298	3.290	3.114	30.6M	28.5M	[paper] [tf-tpu]
EfficientNet-B6	15.918	3.102	2.916	43.3M	41.0M	[paper] [tf-tpu]
EfficientNet-B7	15.570	3.160	2.906	66.7M	64.1M	[paper] [tf-tpu]

Reference

• tf efficientnet
• efficientnet keras pre-trained weights
• Implementation of EfficientNet model. Keras and TensorFlow Keras.

History

• 20190912: created.

Copyright

• Post author: kezunlin
• Post link: https://kezunlin.me/post/88fbc049/
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