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Howard and colleagues: MobileNet efficient on-device vision models

Contribution grade

Efficient computer vision · Human-designed AI research method or resource

Reference score40,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Introduced mobile and embedded vision networks built with depthwise-separable convolutions and size/latency trade-off controls.

Scientific scope

Introduced mobile and embedded vision networks built with depthwise-separable convolutions and size/latency trade-off controls. Scope limitation: The original MobileNet paper is not the invention of depthwise-separable convolutions and is distinct from later MobileNetV2/V3 architectures.

Evidence

The retained primary-source abstract, bibliographic record and stated review scope support this provisional assessment. No independent experimental replication by WRS is claimed. The original MobileNet paper is not the invention of depthwise-separable convolutions and is distinct from later MobileNetV2/V3 architectures.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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