Comments (3)
I use im2rec in MXNet to generate images based according to #4 , then choosing choose resize=320 will yield default image size as 320*320. Also you could set resize=224 when doing .rec file generation. But still I cannot reproduce the results mentioned in the README and the Top-1 error is usually lower by around 1% and Top-5 errors is lower by 0.2%, I try to boost the accuracy by center croping but I don't know how to reproduce the similar result. @cypw Could you give us more hints?
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@whcacademy
Please set the unchanged
option to 1
while generating the .rec
file using im2rec as I have already indicated in run_val.sh.
Note that: By setting unchanged=1
, im2rec
will directly save the raw image data without even decode it, see: im2rec.cc. So you won't lose any valuable information in this step, which may potentially damage the performance.
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Sorry for ignoring import comments in that file.
And new test does reach the performance in the README.
Thanks!
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Related Issues (20)
- caffe model? HOT 5
- Why "num_filter=(num_1x1_c+2*inc)" instead of "num_filter=(num_1x1_c+inc)" HOT 2
- I want to detect objects by DPNs HOT 6
- "Mean-Max pooling" HOT 1
- Cann't find pretrained model based on Keras
- DPN-98 on Place365-Standard dataset HOT 2
- .rec must use the DPNs's Mxnet im2rec? HOT 12
- How to replace augmenter in new mxnet source by image_aug_torch.cc
- Train Log about DPNs
- ImageNet-5k data HOT 4
- Finetune on new dataset HOT 4
- Need substrated mean RGB = [124,117,104] on DPN-68 of imagenet-5k? HOT 4
- The order of Blocks: BN-Act-Conv2d or Conv2d-BN-Act?
- some question about hyper parameter
- In DPN86-5k and DPN92-5k, What's the relation between label and semantic class ? HOT 2
- OSError HOT 1
- How to train DPNs HOT 2
- Segmentation fault HOT 1
- How to trian my dataset by DPNs? HOT 2
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