MobileNetV3

Findings

• IC-1469Progress on standard ImageNet generalization benchmarks is 2.5x faster than progress on crowdsourced global data (DollarStreet, GEODE) across 98 vision models
• IC-1470Geographic disparities (Europe-Africa accuracy gap) are large across all 98 models and have more than tripled between least and best performing models on DollarStreet
• IC-673Trained depthwise convolutional kernels in DS-CNN architectures converge to identifiable DoG-like patterns, with over 95% of ConvNeXtV2 and over 90% of ConvNeXt filters classifiable into a small set of clusters