Our implementation introduces a new class for building MobileNet feature extractors. Following the Section 6.3 of the MobileNetV3 paper, the backbone returns the output of the expansion layer of the Inverted Bottleneck block which has an output stride of 16 and the output of the layer just before the pooling which has an output stride of 32. Moreover, all extra blocks of the backbone are replaced with lightweight equivalents which use a 1x1 compression, a separable 3x3 convolution with stride 2 and a 1x1 expansion. Finally to ensure that the heads have enough prediction power even when small width multipliers are used, the minimum depth size of all convolutions is controlled by the min_depth hyperparameter.