The output will be empty, if your model isn’t able to detect any objects as seen here using an untrained model and random inputs:
model = models.detection.fasterrcnn_resnet50_fpn()
model.eval()
x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
out = model(x)
print(out)
> [{'boxes': tensor([], size=(0, 4), grad_fn=<StackBackward>), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=<IndexBackward>)}, {'boxes': tensor([], size=(0, 4), grad_fn=<StackBackward>), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=<IndexBackward>)}]