Computer Vision Systems • Edge Deployment (MobileNet, EfficientNet-Lite)Hard⏱️ ~3 min
Accuracy vs Latency Trade-offs: Choosing Between SSD MobileNet and EfficientDet Lite
The fundamental edge deployment trade off is accuracy versus latency and energy. SSD MobileNet V1 delivers the fastest and most energy efficient inference on commodity edge hardware: 10 to 12ms on Raspberry Pi with Coral TPU at 0.10 mWh per inference. However, its Mean Average Precision (mAP) of approximately 19 on standard detection benchmarks falls below production thresholds for many general purpose applications. EfficientDet Lite2 achieves 33 mAP, a 14 point improvement, but costs 139 to 188ms on the same Pi with TPU setup.
This creates a clear decision tree. If your application can tolerate 5 to 10 frames per second and accuracy is critical, such as industrial quality inspection or medical imaging assistance, EfficientDet Lite is viable. For real time 30 fps interactions like augmented reality filters, mobile camera effects, or robotics navigation, you must use SSD MobileNet or EfficientDet Lite0 on stronger accelerators like Jetson Orin Nano that can keep EfficientDet within 20ms. The alternative is to sacrifice input resolution: running EfficientDet at 256 by 256 instead of 384 by 384 can halve latency but costs 3 to 5 mAP points.
Portability introduces another dimension. Tuning a model specifically for a Coral TPU or Apple Neural Engine yields excellent latency but locks you into that hardware ecosystem. A portable path that runs on CPU and GPU across vendors costs 2 to 3x more latency and substantial engineering effort to optimize for each backend. Companies like Google and Apple choose vertical integration: they design models and accelerators together. Startups often must support heterogeneous fleets, requiring careful model selection and fallback strategies for devices without accelerators.
💡 Key Takeaways
•SSD MobileNet V1 runs 12 to 16x faster than EfficientDet Lite2 on the same hardware but sacrifices 14 mAP points, creating a clear speed versus accuracy trade off
•For 30 fps real time applications like AR or robotics, only SSD MobileNet or EfficientDet Lite0 on strong accelerators like Jetson Orin Nano meet latency budgets under 25ms
•Reducing input resolution from 384 by 384 to 256 by 256 can halve EfficientDet latency but costs 3 to 5 mAP points, a viable trade off for latency constrained deployments
•Hardware specific tuning for Coral TPU or Apple Neural Engine improves latency by 2 to 3x but locks deployment to that ecosystem versus portable models that run slower everywhere
•Battery powered continuous operation at 30 fps with SSD MobileNet draws 180mW for inference alone; EfficientDet would consume 1W+ and drain batteries in hours
📌 Examples
Mobile AR app chooses SSD MobileNet to maintain 30 fps smooth UI, accepting lower mAP for user experience; background photo analysis uses EfficientDet overnight
Industrial inspection system uses EfficientDet Lite2 at 5 fps for quality control where accuracy matters more than speed, achieving 33 mAP with acceptable latency
Robotics startup deploys SSD MobileNet on Jetson Orin Nano for 30 fps navigation, then upgrades to EfficientDet Lite0 when Orin NX provides 3x more GPU compute