qtacierp / isecret Goto Github PK

Hello, first thank you so much for sharing your code! :)
It helped me a lot!

I have one question,
Did you use all the data in EyeQ dataset?
As you may know, some images don't have whole circle of the fundus like the image below.

I have trying to use the same data as you did (but with different model), but because of the imperfect circle of some fundus images, high quality images are not generated well:( And I think it is because the model tries make the circle perfect like the below images.

Do you have some idea to overcome this problem? You seem to overcome this issue (if you have used the imperfect circle fundus images) since your FIQAs are high!

Thanks a lot in advance:>

Hello:)
hope you are doing well

I have some questions about the experiment settings.

Did you did the cross-validation for the experiment ?

for PSNR and SSIM, the value is like this

so I assumed you did a cross-validation, but for FIQA and VSD is just a single value like this

so I am kind of confused.

Thanks

Hello I came again😁

I was looking at your paper and came up with one question about how you split the lq and hq data. As mentioned in your paper and your github, you split your hq and lq data by score of eyeQ/MCFNet. - LQ for "usable" and HQ for "good"-.
But in the table in the paper, Original FIQA is not zero which means some pictures are marked as "good".
How could this possible?
Did you used "EyeQ/data/Label_EyeQ_train.csv" quality level? I did and found out that some datas mark as "usable" to be "good" or "reject" when I tested with MCF net.

Hi, thank you for releasing such excellent research on image enhancement. May I ask a few questions about your experiments?

1. How you split the EyeQ dataset into training/val/testing sets? In your paper, you mentioned that We utilize EyeQ [3] as our first dataset, which consists of 28792 fundus images with three quality grades (“Good”, “Usable”, “Reject”) and has been divided into a training set, a validation set, and a testing set. I noticed that on the EyeQ dataset website, they split the total dataset into training/testing sets, also on the Kaggle challenge website, I didn't find any places explain how they split the dataset.

2. How long it takes to train your network on your machine?

Thank you!

Hello,
After I'd run the ISECRET code, which includes the multiprocessing, I experienced a semaphore leak issue. This issue is occurring in just one of my servers (other servers are doing fine).
Do you happen to know about this issue? I think it is my pytorch or python version problem, not your code, but if you happen to know about the issue, I will be more than happy to hear it from you.

Because of this issue, the code is killed in the middle of training. 😥

The error code is like this.
/home/guest1/.conda/envs/ellen/lib/python3.7/multiprocessing/semaphore_tracker.py:148: UserWarning: semaphore_tracker: There appear to be 6 leaked semaphores to clean up at shutdown len(cache))

Thank you in advance :)