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EfficientNet: ã¢ãã«ã¹ã±ã¼ãªã³ã°ã¨ AutoML ã§æé«ç²¾åº¦ãéæãã Google ã®ç»åèªèæè¡
2019å¹´7æ2æ¥ç«ææ¥
ãã®è¨äºã¯ Mingxing Tan 㨠Quoc V. Le ã«ãã Google AI Blog ã®è¨äº "
EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
" ãå ã«ç¿»è¨³・å çãããã®ã§ã。詳ããã¯å è¨äºãã覧ãã ãã。
æ稿è : Google AI ã¹ã¿ãã ã½ããã¦ã§ã¢ ã¨ã³ã¸ãã¢、Mingxing Tan、Google AI 主å¸ãµã¤ã¨ã³ãã£ã¹ã、Quoc V. Le
ç³ã¿è¾¼ã¿ãã¥ã¼ã©ã« ãããã¯ã¼ã¯
（CNN）ã®éçºã§ã¯、ã¾ãã¯ä¸å®ã®ãªã½ã¼ã¹ã³ã¹ããæå ¥ã、ãã®å¾ããã«å¤ãã®ãªã½ã¼ã¹ãå©ç¨å¯è½ã«ãªã£ã段éã§ã¹ã±ã¼ã«ã¢ããã精度ãä¸ãã¦ããæ¹æ³ãä¸è¬çã§ã。ä¾ãã°
ResNet
ã§ã¯、ã¬ã¤ã¤ã¼æ°ãå¢ãããã¨ã§ ResNet-18 ãã ResNet-200 ã¾ã§ã¹ã±ã¼ã«ã¢ããã§ãã¾ã。ã¾ãæè¿çºè¡¨ããã
GPipe
ã§ã¯、ãã¼ã¹ã©ã¤ã³ CNN ã 4 åã¹ã±ã¼ã«ã¢ãããããã¨ã§
ImageNet
top-1 精度 84.3% ãéæãã¦ãã¾ã。ããããã¢ãã«ã®ã¹ã±ã¼ã«ã¢ããã§ã¯、CNN ã®æ·±ããå¹ ãé©å½ã«å¢ãããã、å¦ç¿ãè©ä¾¡æã®ç»å解å度ã大ããããã、ã¨ãã£ãããæ¹ãè¡ããã¾ã。確ãã«ããããæ¹æ³ã§ã精度ã¯åä¸ãã¾ãã、æä½æ¥ã«ããé¢åãªãã¥ã¼ãã³ã°ãå¿ è¦ãªãã、ãã®æ§è½ããã¹ãã§ããã¨ã¯è¨ãã«ããã§ã。ãã、CNN ã®ç²¾åº¦ã¨å¹çãããæ¹åããããã®「ã¹ã±ã¼ã«ã¢ããã®åå」ãããã°ã©ãã§ãããã。
ç§ãã¡ã
ICML2019
ã§çºè¡¨ããè«æ「
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
（EfficientNet: ç³ã¿è¾¼ã¿ãã¥ã¼ã©ã« ãããã¯ã¼ã¯ ã¢ãã«ã®ã¹ã±ã¼ãªã³ã°ãåèãã）」ã§ã¯、ã¢ãã«ã®æ°ããªã¹ã±ã¼ãªã³ã°ææ³ãææ¡ãã¦ãã¾ã。å ·ä½çã«ã¯、ã·ã³ãã«ãã¤ã¨ã¦ãå¹ççãªè¤åä¿æ°（compound coefficient）ã使ã、ããçµ±å¶ãããææ³ã§ CNN ãã¹ã±ã¼ã«ã¢ãããã¾ã。
å¾æ¥ã®ã¢ããã¼ãã§ã¯、å¹ 、æ·±ã、解å度ãªã©ã®ãããã¯ã¼ã¯ã®æ¬¡å ãä»»æã®å¤§ããã«ã¹ã±ã¼ã«ã¢ãããã¦ãã¾ãã。ä¸æ¹、ç§ãã¡ã®ææ³ã§ã¯ä¸å®ã®ã¹ã±ã¼ãªã³ã°ä¿æ°ã使ã£ã¦ããããã®æ¬¡å ãåä¸ã«ã¹ã±ã¼ã«ã¢ãããã¦ããã¾ã。ãã®æ°ããªã¹ã±ã¼ãªã³ã°ææ³ã¨、æè¿ã®
AutoML
ã®é²å±ãæ´»ç¨ã、EfficientNet ã¨å¼ã°ããã¢ãã«ç¾¤ãéçºãã¾ãã。ãã®ã¢ãã«ã¯ç¾å¨ã®æé«æ°´æºã®ç²¾åº¦ãä¸åã、å¹çãæ大㧠10 ååä¸ãã¦ãã¾ã（å°ååã¨é«éå）。
è¤åã¢ãã« ã¹ã±ã¼ãªã³ã°：CNN ãã¹ã±ã¼ã«ã¢ããããåªããææ³
ä»åã®ç 究ã§ã¯、ãããã¯ã¼ã¯ã®ã¹ã±ã¼ãªã³ã°ã«ããå¹æãç解ãããã、ã¢ãã«ã®å次å ãå¤åãããå ´åã®å½±é¿ã系統çã«èª¿æ»ãã¾ãã。ãã®çµæ、åã ã®æ¬¡å ãã¹ã±ã¼ãªã³ã°ããã¨ã¢ãã«æ§è½ã¯åä¸ãã¾ãã、ã¢ãã«å ¨ä½ã¨ãã¦æ¹åã®å¹æãæã大ãããªãã®ã¯、å©ç¨å¯è½ãªãªã½ã¼ã¹ããããã¯ã¼ã¯ã®ãã¹ã¦ã®æ¬¡å （å¹ 、æ·±ã、ã¤ã¡ã¼ã¸ã®è§£å度）ã«åçã«å²ãå½ã¦ãå ´åã§ãããã¨ããããã¾ãã。
è¤åã¹ã±ã¼ãªã³ã°ææ³ã§ã¯ã¾ã、ããä¸å®ã®ãªã½ã¼ã¹å¶ç´（ä¾:
FLOPS
ã 2 åã«è¨å®ãã）ã®ãã¨ã§
ã°ãªãããµã¼ã
ãè¡ã、ãã¼ã¹ã©ã¤ã³ã¨ãªããããã¯ã¼ã¯ã®åã ã®æ¬¡å éã®é¢ä¿ãè¦ã¤ãã¾ã。ããã«ãã、å次å ã«å¯¾ããé©åãªã¹ã±ã¼ãªã³ã°ä¿æ°ã決ã¾ãã¾ã。ãã®å¾ãã®ä¿æ°ãé©ç¨ã、å¸æã®ã¢ãã«ãµã¤ãºã¾ãã¯è¨ç®ãªã½ã¼ã¹ã«åããã¦ãã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ãã¹ã±ã¼ã«ã¢ãããã¾ã。
ãã¾ãã¾ãªã¹ã±ã¼ãªã³ã°ææ³ã®æ¯è¼。ãããã¯ã¼ã¯ã®åã ã®æ¬¡å ãä»»æã«ã¹ã±ã¼ãªã³ã°ããå¾æ¥åã®ã¹ã±ã¼ãªã³ã°ææ³（b）～（d）ã¨ã¯ç°ãªã、
è¤åã¹ã±ã¼ãªã³ã°ææ³ã§ã¯
ãã¹ã¦ã®æ¬¡å ãåä¸çãã¤ä¸å®ã®ååã«åºã¥ãã¦ã¹ã±ã¼ã«ã¢ãããã
ãã®è¤åã¹ã±ã¼ãªã³ã°ææ³ã§ã¯、å¾æ¥ã®ã¹ã±ã¼ãªã³ã°ææ³ã«æ¯ã¹ã¦、ã¢ãã«ã®ç²¾åº¦ã¨å¹çãã³ã³ã¹ã¿ã³ãã«æ¹åãã¦ããã¾ã。ãã¨ãã°
MobileNet
ã§ã¯ ImageNet 精度ã 1.4% åä¸、
ResNet
ã§ã¯ 0.7% åä¸ãã¾ãã。
EfficientNet ã®ã¢ã¼ããã¯ãã£
ã¢ãã«ã®ã¹ã±ã¼ãªã³ã°ã®å¹çæ§ã¯、ãã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ã®è¨è¨ã«ã大ããä¾åãã¾ã。ããã§、æ§è½ãããã«æ¹åãã¹ãæ°ãããã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ãéçºãã¾ãã。精度ã¨å¹ç（FLOPS）ã®ä¸¡æ¹ãæé©åãã
AutoML MNAS ãã¬ã¼ã ã¯ã¼ã¯
ã«ãã
ãã¥ã¼ã©ã« ã¢ã¼ããã¯ãã£æ¤ç´¢
ãè¡ããã¨ã§å®ç¾ãã¦ãã¾ã。ãã®çµæã¨ãã¦å¾ãããã¢ã¼ããã¯ãã£ã¯、
MobileNetV2
ã
MnasNet
ã¨åãããã« Mobile Inverted Bottleneck Convolution（MBConv）ã使ã£ã¦ãã¾ãã、å©ç¨ããè¨ç®ãªã½ã¼ã¹ã®å¢å ã«ã¨ããªããããã«å¤§ããªãã®ã«ãªã£ã¦ãã¾ã。ãã®å¾、ãã®ãã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ãã¹ã±ã¼ã«ã¢ããããã¨、
EfficientNet
ã¨å¼ã°ããã¢ãã«ç¾¤ãå¾ããã¾ã。
ãã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ã§ãã EfficientNet-B0 ç¨ã®ã¢ã¼ããã¯ãã£ã¯、ã·ã³ãã«ã§ã¯ãªã¼ã³ãªè¨è¨ã§ã。
ã¹ã±ã¼ãªã³ã°ãæ±ç¨åã容æã§ã。
EfficientNet ã®ããã©ã¼ãã³ã¹
ImageNet
ã対象ã«、EfficientNet ãããã¤ãã®æ¢å CNN ã¨æ¯è¼ãã¾ãã。EfficientNet ã¢ãã«ã¯、æ¢åã® CNN ãããé«ã精度ã¨åªããå¹çã®ä¸¡æ¹ãå®ç¾ãã¦ãã、ãã©ã¡ã¼ã¿ã®ãµã¤ãºã¨è¨ç®éã 1 æ¡å°ãªããªã£ã¦ãã¾ã。ãã¨ãã°é«ç²¾åº¦çã® EfficientNet-B7 ã¯、ImageNet ã® top-1 㧠84.4%、top-5 㧠97.1% ã¨ããæé«ã¬ãã«ã®ç²¾åº¦ãå®ç¾ãã¦ãã¾ã。ã¾ã、CPU ã«ããæ¨è«ã§æ¯è¼ããã¨、ããã¾ã§ã®
Gpipe
ã¨æ¯ã¹ã¦ãµã¤ãºã 8.4 åã® 1、é度㯠6.1 å。ã¾ã、ä¸è¬çãª
ResNet-50
㨠EfficientNet-B4 ãæ¯ã¹ãã¨、è¨ç®éã¯ã»ã¼åãã§ãã top-1 精度㯠ResNet-50 ã® 76.3% ãã 82.6%（6.3% åä¸）ã«å¢å ãã¦ãã¾ã。
ã¢ãã«ã®ãµã¤ãºã¨ç²¾åº¦ã®æ¯è¼。EfficientNet-B0 ã¯、
AutoML MNAS
ãä½æãããã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯。ä¸æ¹ã® Efficient-B1 ãã B7 ã¯、ãã¼ã¹ã©ã¤ã³ ãããã¯ã¼ã¯ãã¹ã±ã¼ã«ã¢ãããããã®。ç¹ã«、EfficientNet-B7 ã¯、ããã 1 㧠84.4%、ããã 5 㧠97.1% ã¨ããæé«ã¬ãã«ã®ç²¾åº¦ãå®ç¾。åæã«、æ¢åã®æé«ã® CNN ã¨æ¯ã¹ã¦ãµã¤ãºã 8.4 åã® 1 ã«ãªã£ã¦ãã。
EfficientNet 㯠ImageNet ã§é«ãæ§è½ãçºæ®ãã¾ãã、使ãããããèããã¨、ä»ã®ãã¼ã¿ã»ããã§è»¢ç§»å¦ç¿ããå ´åã®æ§è½ãéè¦ã§ã。ãã®ç¹ãè©ä¾¡ãããã、åºã使ããã¦ãã 8 ã¤ã®è»¢ç§»å¦ç¿ãã¼ã¿ã»ãã㧠EfficientNet ããã¹ããã¾ãã。EfficientNet ã¯、
CIFAR-100
（91.7%）、
Flowers
（98.8%）ãªã©、8 ã¤ã®ãã¡ 5 ã¤ã®ãã¼ã¿ã»ããã§æé«ã¬ãã«ã®ç²¾åº¦ãå®ç¾ãã¾ãã。ä¸ã«ã¯ãã©ã¡ã¼ã¿æ°ãã²ã¨æ¡å°ãªããªããªã£ãä¾ãããã¾ãã（æ大㧠21 åã® 1）。ããã«ãã、EfficientNet ã¯è»¢ç§»å¦ç¿ã§ãæ§è½ãçºæ®ã§ãããã¨ããããã¾ã。
ä»åã®ç 究ã«ããã¢ãã«ã®å¹çãå¤§å¹ ã«æ¹åã§ãããã¨ãã、ä»å¾ã®ã³ã³ãã¥ã¼ã¿ ãã¸ã§ã³é¢é£ã®ã¿ã¹ã¯ã«ããã¦、EfficientNet ãæ°ããåºç¤ã¨ãã¦æ´»ç¨ãããå¯è½æ§ãèãããã¾ã。ããã§、ãã¹ã¦ã® EfficientNet ã¢ãã«ããªã¼ãã³ã½ã¼ã¹åãã¾ãã。ããã«ãã£ã¦æ©æ¢°å¦ç¿ã³ãã¥ããã£ãåºãæ©æµãåãããããã¨ãé¡ã£ã¦ãã¾ã。EfficientNet ã®ã½ã¼ã¹ã³ã¼ãã¨
TPU
ç¨ã®å¦ç¿ã³ã¼ãã¯、
ãã¡ã
ãã確èªã§ãã¾ã。
è¬è¾:
Hongkun Yu、Ruoming Pang、Vijay Vasudevan、Alok Aggarwal、Barret Zoph、Xianzhi Du、Xiaodan Song、Samy Bengio、Jeff Dean、㨠Google Brain ãã¼ã ã«æ ¼å¥ã®æè¬ãæ§ãã¾ã。
Reviewed by
Kaz Sato - Staff Developer Advocate, Google Cloud
ã©ãã«
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