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5 months ago

MobileNetV2: Inverted Residuals and Linear Bottlenecks

View Paper Details

Mark Sandler Andrew Howard Menglong Zhu Andrey Zhmoginov Liang-Chieh Chen

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Abstract

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Benchmarks

BenchmarkMethodologyMetrics
image-classification-on-imagenetMobileNetV2
GFLOPs: 0.600
Number of params: 3.4M
Top 1 Accuracy: 72%
image-classification-on-imagenetMobileNetV2 (1.4)
GFLOPs: 1.170
Number of params: 6.9M
Top 1 Accuracy: 74.7%
retinal-oct-disease-classification-onMobileNet-v2
Acc: 97.46
retinal-oct-disease-classification-on-oct2017MobileNet-v2
Acc: 98.5
Sensitivity: 99.4
semantic-segmentation-on-dada-segMobileNetV2
mIoU: 16.05

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