I’m sorry for the late thank you.
As you said, I applied blur only and checked it, and I got bad results.
However, I did several trials,
It seems that augmentation does not play a decisive role in constant train loss.
I reconsidered your previous answer and accessed the data again from the beginning, and I found it curious in the normalize part.
In my code,
image = Image.fromarray(image)
image = TF.to_tensor(image).float()
image = image/(image.max()+0.000000001)
In order to fit the data in the [0,1] range, each data was divided into .max () values to make each data into the [0,1] range.
The question in this part is that the max values of each data are different. Is it correct to set the range to [0,1] as each max rather than the max value of the entire data set?
Also, I saw that the data range should be normalized to [-1,1] through various posts.
However, when norm = transforms.Normalize([0.5], [0.5]),image = norm(image) is used, mean and std values of the entire image cannot be 0 and 1, respectively. Is it correct to apply this?
I’m always grateful for your help
Thanks!