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

params
flops

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

b0-b7 top-1 on imagenet

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

History

  • 20190912: created.

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posted @ 2019-12-04 08:14  kezunlin  阅读(817)  评论(0)  编辑  收藏  举报