Open ksh11023 opened 1 year ago
Hi, In the student model and the teacher model, there are several shared structures like BEV backbone and detection head. We load the pre-trained weights from the teacher model to accerate convergence and potentially improve the performance of the student model.
Thank you for the swift reply!
I have one more question. When training 60 epochs(BEV DA), is it better to train without rpn loss? the code only train with depth loss and BEV loss.
Thank you.
Hi, Yes, this can be considered as a trick in the training process, because when the BEV features are not correct, calculating RPN losses is meaningless. And similar technique has been used in other methods, for example, using only 2D detection losses first and then adding 3D detection losses.
Hello,
When training with V2.yaml file, in train_cmkd.py why do you load pretrained_lidar_model to model.model_img?
Thank you.