MobileNetV2: Efficiency for Edge Computing
For cases where computing power is limited, like mobile apps or embedded devices, MobileNetV2 is usually the best choice. It use something called “depthwise separable convolutions,” which break one big convolution into two smaller steps. This reduce the number of parameters and calculations by alot.[1]
Inference Speed: MobileNetV2 is made to be fast. In test benchmarks, it can run inference in about 15ms per image, which is much faster then most ResNet models.[1]
Accuracy vs Size: Even though it is much smaller (around 3.5 million parameters compared to 25 million in ResNet-50), it still keep good accuracy (about 71–72% on ImageNet). Because of this, it is very suitable for the “Edge Computing” option in a Zwicky Box analysis.[2]
References
- ↑ 1,0 1,1 Joshua, Chidiebere & Kotsis, Konstantinos & Ghosh, Sourangshu. (2025). Comparative Evaluation of ResNet, EfficientNet, and MobileNet for Accurate Classification of Babylonian Sexagesimal Numerals.
- ↑ Ahmed, I. Why Your MobileNetV2 Model Performs Better Than ResNet50. Medium. Available at: https://medium.com/@imtiaz.ahmed2206/why-your-mobilenetv2-model-performs-better-than-resnet50-2a9998fda4c7