Comments (3)
ikhlestov
commented on August 27, 2024
1
Yes, you are right. Maybe this is not best approach - but at leas it's working.
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ikhlestov
commented on August 27, 2024
Hi @John1231983
I've try with mean/std generated per image and it went to about 2-3 percent worst results. You may try by your own - just change few lines in data provider.
Regarding recomputing - I've calculate mean and std for full dataset and didn't change it if new images are come.
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John1231983
commented on August 27, 2024
Thanks, the full dataset means training+testing set. Is it right?
So, you first merge training and testing set as the full dataset, then compute the mean and std from the full dataset. The normalize for each image in each set (training and testing) based on the mean of std. Am I right?
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Related Issues (20)
- can you provide your trained models? HOT 5
- SVHN normalization issue HOT 8
- How about performance for flower dataset? HOT 1
- The order of image normalization and augmentation seems to be the same as the original implementation? HOT 5
- Problem regarding the number of features generated at the initial convolution layer HOT 1
- requirements issue HOT 1
- How to use 2 gpus? HOT 1
- Problem about the running time. HOT 2
- l2 regularization for bias too.. is it necessary? HOT 1
- Train on custom dataset HOT 1
- Train on images on my desktop HOT 1
- Choose GPU device HOT 9
- ResourceExhaustedError: OOM when allocating tensor HOT 2
- Why did you remove the max pooling layer after the initial convolution? HOT 1
- 'by_chanels' normalization issue between different splits HOT 3
- Pre-trained weights HOT 1
- Is there some way to get the softmax (probability values) from the saved models using the repos code. HOT 1
- batch_norm in dense_net.py HOT 1
- moving mean/variance update HOT 6
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