We have introduced 3 new model variants for RetinaNet, FasterRCNN and MaskRCNN that include several post-paper architectural optimizations and improved training recipes. All models can be used similarly:
import torch
from torchvision.models.detection import *

images = [torch.rand(3, 800, 600)]
model = retinanet_resnet50_fpn_v2(weights="DEFAULT")
# model = fasterrcnn_resnet50_fpn_v2(weights="DEFAULT")
# model = maskrcnn_resnet50_fpn_v2(weights="DEFAULT")
model.eval()
prediction = model(images)

Below we present the metrics of the new variants on COCO val2017. In parenthesis we denote the improvement over the old variants:



Model
Box mAP
Mask mAP




retinanet_resnet50_fpn_v2
41.5 (+5.1)
-


fasterrcnn_resnet50_fpn_v2
46.7 (+9.7)
-


maskrcnn_resnet50_fpn_v2
47.4 (+9.5)
41.8 (+7.2)


