Benchmarks
Here is how to initialize the two pre-trained models:
ssdlite = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True)
ssd = torchvision.models.detection.ssd300_vgg16(pretrained=True)

Below are the benchmarks between the new and selected previous detection models:
{:.table.table-striped.table-bordered}
| Model | mAP | Inference on CPU (sec) | # Params (M) |
| ------------- | ------------- | ------------- | ------------- |
| SSDlite320 MobileNetV3-Large | 21.3 | 0.0911 | 3.44 |
| SSD300 VGG16 | 25.1 | 0.8303 | 35.64 |
| SSD512 VGG16 (not released) | 28.8| 2.2494 | 37.08 |
| SSD512 ResNet50 (not released) | 30.2 | 1.1137 | 42.70 |
| Faster R-CNN MobileNetV3-Large 320 FPN (Low-Res) | 22.8 | 0.1679 | 19.39|
| Faster R-CNN MobileNetV3-Large FPN (High-Res) | 32.8 | 0.8409 | 19.39 |