A set of final optimizations that brought our implementation very close to the official one and helped us bridge the accuracy gap was training the backbone from scratch instead of initializing from ImageNet, adapting our weight initialization scheme, changing our Input Scaling and replacing all standard ReLUs added on the SSDlite heads with ReLU6. Note that since we trained the model from random weights, we additionally applied the speed optimization described on the paper of using a reduced tail on the backbone.