Hi,
I’ve followed the object detection tutorial (TorchVision Object Detection Finetuning Tutorial — PyTorch Tutorials 1.10.1+cu102 documentation) and adapted the code for my problem.
I understand that the average precision and recalls are calculated when put through evaluation:
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)
How can I calculate the f1 score from these metrics? Would I need to extract additional information? I’m guessing I would need to also calculate the number of false negatives, false positives, true positives and true negatives based on a specific IoU threshold…
Is there an example that already exists?