ramdrop / stun Goto Github PK

What is the purpose of exponentiating the extracted image features by e?
What is the mathematical basis for doing this?

Thank you for sharing your work!

I have one question about the experimental results in this paper.
The original Netvlad records a recall at 1 of about 86% for pitts30k. However, the recall at 1 of the network proposed in this paper is measured to be about 61%.
I think a lot of this difference can be attributed to the difference in pooling layers. In this paper, it used the gem pooling layer instead of the vlad pooling layer.

Could you share any personal experience or insight into the reason for this choice?

Thank you in advance.

Hi there, do you have any plans to release training scripts?

Stun is a great work.Now I have read the source code you shared and have benefited a lot. Thank you for sharing.
When can you publish your training code?

The function [update_opt_from_json] is to exclude some parameters that need to be manually updated, and then used to update other parameters with the same name as the command line parameters but different from the default value of the command line。right?

What does this line of code do?

Great work! I have been using your code, and I noticed that the best results are achieved around the third or fourth epoch during training. Is this because you have some special settings for training?

Dear authors, I got this error report while training with your code: python main.py --phase=train_tea --loss=cont, did you get the same error while training? How did you solve it?
/root/miniconda3/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:129: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

nontrivial_positives is abbreviated from positives and which other words?

Thank you for your great work! But I have a question about your pitts.py code. Since self.cache = None in line 253, is there no file open in line 270 with h5py.File(self.cache, mode='r') as h5, then the meaning of the code with h5py.File(self.cache, mode='r') as h5 is What?

Dear Author, I am going to use vis_results.py to evaluate STUN(tea and stu) trianed from scratch,but I found that there is no embs.pickle file generated, how can I generate embs.pickle file in training and evaluating STUN from for vis_results.py？