Hi , thanks for your work. It really open my mind for the interpretable of DNN with a replacement of linenar transform . But when reading the paper ,i have some point not understanding well as below, so if you can help to answear it ? 1.What kind of metric is the 'localisation metric',what 's the physical meaning? (Fig 6) 2.What's the meaning of baseline 'orcale attribution' and 'uniform attribution' ?(Fig 6) 3.Is there some parameter refer to the area in red ? Or just for better visual ? (Fig 2)

Hi all, I was trying to run the code but I faced multiple issues:

1. I am running the experiments/Imagenet/bcos/model.py​, the save_path in the code is still trying to reach the CIFAR10 dataset.

2. In model.py, get_optimizer():

"the_model" does not have an attribute called "opti"

3. after spending some time I managed to solve above issues (not sure if it is gonna harm the functionality), but when I run the code the GPU runs out of memory! I played with the batch_size, but no luck. I guess that is due to a memory leak somewhere. Can anyone verify that?

Hi, Thanks for your interesting paper. I'm trying to train your model but only I can find is Trainer class in training folder and not the training loop code. Can you please add your code for training loop code OR give some guidelines for reproducing your implementation?

Thanks a lot.

Hello,

thanks for your fascinating work. I am trying to use the B-cos network (the densenet121 named “densenet_121_cossched”) in my research but I struggle with having it transfer effectively to smaller datasets, e.g. CUB2011. In fact, it overfits much more ( much worse final test acc) and improves a lot slower than the conventional densenet (In fact, only retraining the final layer leads to no learning whatsoever across a range of hyperparameters that all work for the conventional one). Since you have experience with training this network, I figure I might just ask you:

• How sensitive was the training to optimized hyperparameters? Do I “just” need to tune regularization etc?
• Have you done some experiments on transfer learning and maybe figured out effective methods?
• Could B-cos networks be less suited for fine-grained-classification?
• How exactly did you train the model? From my point of view your training code does not work, as the trainer class does not get a “loss” argument or am I missing something? I am encountering nan as loss, when using BCE.
• Did you investigate the impact of norming w in Equation 3 in the paper (maybe with increased B) so that the model rescales the outputs himself?

Any answers would be greatly appreciated :)

Greetings

By following the README of Training，an error happens when run the code as below. Could you help to check it ,thanks !

Traceback (most recent call last): File "D:/1.Study/PycharmProjects/Reproduce/B-cos-main/experiments/Imagenet/bcos/model.py", line 93, in start_training(cmd_args, get_model, get_optimizer) File "D:\1.Study\PycharmProjects\Reproduce\B-cos-main\training\training_utils.py", line 17, in start_training training_loop(params, get_model, get_optimizer) File "D:\1.Study\PycharmProjects\Reproduce\B-cos-main\training\training_utils.py", line 83, in training_loop data_handler = Data(params.dataset_name, rank=ddp_rank, File "D:\1.Study\PycharmProjects\Reproduce\B-cos-main\data\data_handler.py", line 82, in init self.train_data_loader = self.init_data_loader(train=True, File "D:\1.Study\PycharmProjects\Reproduce\B-cos-main\data\data_handler.py", line 105, in init_data_loader data = Data.datasets[self.dataset_name](self.data_path, train=train, download=False, File "D:\1.Study\PycharmProjects\Reproduce\B-cos-main\data\datasets.py", line 34, in init path = os.path.join(root, self.base_folder, "train" if train else "val") File "D:\Software\anaconda3\lib\ntpath.py", line 78, in join path = os.fspath(path) TypeError: expected str, bytes or os.PathLike object, not NoneType