How can I determine whether predicted bboxes are true or false positives from faster rcnn in a binary problem

1

Hi,

As the title suggests, how can I determine whether a bbox is considered to be “correct”? I have followed the object detection tutorial where I evaluate my model via:

for epoch in range(num_epochs):
  # train for one epoch, printing every 10 iterations
  train_one_epoch(model, optimizer, train_data_loader, device, epoch, print_freq=10)
  # update the learning rate
  lr_scheduler.step()
  # evaluate on the test dataset
  evaluate(model, valid_data_loader, device=device)

I’m wondering if there is a simple way of retrieving this information without the need to amend the engine script…