Comments (4)
I trained my "VDSR_170000" model by about 300000 training samples (1638 images augumented from 91 images).
You can easily get 1638 images from 91 images by simple transforms, like rotation, crop, flip and translation。
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thanks for your quick reply, i understand now. have you ever try real time data augmentation in Caffe? do you think it is better than preprocessing the traing data? similar with this https://github.com/kevinlin311tw/caffe-augmentation
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I didn't try real time data augmentation in caffe. You can have a try. In my opinion, it wil be more effective.
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ok i will try to do it. one again thanks
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Related Issues (20)
- Multi-Scale Implementation HOT 10
- Caffemodels corresponding to VDSR_official.mat and VDSR_ADAM.mat HOT 3
- This version isn't use the clip-gradients? HOT 2
- about the data sample HOT 3
- loss during training HOT 9
- Test Function at Caffe HOT 2
- Error in VDSR_Matconvnet (line 15) HOT 4
- A display bug in Demo_SR_Conv.m ?
- how to get high resolution output by using my own image data?
- Questions about parameters HOT 2
- sr_psnr less than bicubic psnr HOT 6
- Test code in pyCaffe/C++ HOT 7
- About learning rate HOT 2
- File for training data is offline – alternative location? HOT 2
- Data Preprocessing HOT 2
- About GPU load
- loss nan
- Thanks and some questions HOT 1
- Questions about usage HOT 1
- Some questions about DEM(>255)
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