Comments (5)
I have the same question. I think the question should be whether the order of image normalization and augmentation matter since I notice some other implementations apply normalization after data augmentation during training. If yes, so which order should be better?
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Hi all!
Here I try to implement code the same way as it was in original repo, but there still can be some difference. Really I don't know exactly what order of augmentation should be correct and better. It should be tested. Unfortunately, I have very tight schedule now. So, if somebody wants to make such experiment and send a result/updated code with PR - it will be really great!
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I've checked the original repo. There is only one minor difference between yours and the authors'. For the augmentation part, the authors do flipping before translating, as in yours, the order is reverse. That's the only difference regarding data processing.
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Ok, I will change the order and re-run the experiments, when I'm free.
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Well, for CIFAR100+ dataset I've get an error 25.62 when previous was 25.87. I don't know did this happen because of another augmentation order or because of another initialization. But at least it didn't become worse. So I'm going to change the order. Thank you for reporting the issue!
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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
- 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
- Error while running SVHN Dataset
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