As we can see, the SSDlite320 MobileNetV3-Large model is by far the fastest and smallest model and thus it’s an excellent candidate for real-world mobile applications. Though its accuracy is lower than the pre-trained low-resolution Faster R-CNN equivalent, the SSDlite framework is adaptable and one can boost its accuracy by introducing heavier heads with more convolutions. 
On the other hand, the SSD300 VGG16 model is rather slow and less accurate. This is mainly because of its VGG16 backbone. Though extremely important and influential, the VGG architecture is nowadays quite outdated. Thus though the specific model has historical and research value and hence it’s included in TorchVision, we recommend to users who want high-resolution detectors for real world applications to either combine SSD with alternative backbones (see this example on how to create one) or use one of the Faster R-CNN pre-trained models.