Comments (3)
dcorti
commented on July 29, 2024
Hi
It looks like the program converged, but indeed the loss function did not decrease significantly. Usually, with the retina DRIVE dataset, the training loss at least halves before flattening.
Probably in your case the program did not learn much, what kind of dataset did you try?
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wenouyang
commented on July 29, 2024
Hi, the data set is remote sensing data for field survey. Currently, I have 125 pairs of raw training data with size 512*512. I heavily sampled from this training set to have 100K training set with 64*64 patches.
Will this kind of sampling process cause any potential issue due to the heavy overlapping among sampled training set?
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dcorti
commented on July 29, 2024
I'm not sure, since I did strong ovelap as well but it did not create problems. It's hard to guess where the problem could be.
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Related Issues (20)
- Produce the segmentation for a whole image.
- only the configuration.txt file is available in the resulting test folder
- Why is there no 1st_manual and 2nd_manual under test file after I download the DIRVE? HOT 4
- Could not get the files after running training code HOT 3
- can't find sample_input_imgs
- how to train on other database?
- how to train on my own database HOT 1
- Keras and tf version HOT 2
- TypeError: float() argument must be a string or a number, not 'TiffImageFile' HOT 2
- ImportError: cannot import name 'jaccard_similarity_score' HOT 1
- how to create overlapping patches from images
- version
- How to python run_training.py ?
- please help "ImportError: No module named visualize_util
- For those who are interested in the theoretical part of this code... the article's title of this code is "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network".
- Why i am getting less performance than yours?
- Process finished with exit code -1073740791 (0xC0000409) HOT 1
- Trouble with model architecture HOT 1
- Any subsequent image repair work
- semi-supervised learning
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