Comments (2)
Hi Feng,
Thanks very much for your interest. The preprocessing pipeline for validation is defined in the function valloader_upscaled_static in datasets/dataloader_imagenet_dct.py. Please ignore transform4 and transform5 because they are used for debugging only. I will delete these code in the next commit. Thanks.
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Hello, it is me again. I use this code to train my own dataset last night, but no matter how I adjust the hyperparameters, the loss will go to NaN in less than 10 steps.
My dataset is a traffic sign dataset, images are pretty small (around 39 to 96px). I have tried setting different batch size (64, 128 ,156), different image size (64, 128, 256, 512), and learning rate ( 1e1 to 1e4), but all of them fail to train.
Any suggestions?
Thank you.
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Related Issues (20)
- look for "block_composition" HOT 10
- Do you transform the images from BGR to YCbCr color space? HOT 2
- pretrained model can't be downloaded HOT 1
- The mobilenetv2 cannot be evaluated with your pretrained models. HOT 1
- the link to Training code is not available now ,could you update it again? HOT 5
- Cifar model not found
- About mean and std in transfrom HOT 2
- pretrained model can't be downloaded HOT 1
- How do you group the components with same frequency into one channel? HOT 1
- The code is so bad HOT 2
- pretrained model can't open HOT 5
- Questions about the dataloader and missing files HOT 1
- Missing transforms (ToYCrCb, ChromaSubsample, UpsampleDCT, CenterCropDCT) in cvtransforms.py HOT 3
- update training code
- how to decompress the pretrained model?
- could you share the env config?
- ModuleNotFoundError: No module named '_dctfromjpg_wrapper'
- RuntimeError: Wrong JPEG library version: library is 62, caller expects 90 solved
- 数据预处理
- 2✖ upsampling
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