Comments (10)
The experiments with ResNet101 are trained with 4 GPUs. Actually our training code contains many memory optimization so it needs less memory.
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@wistone Thank you for the reply.
Could you please let me know how do you optimize memory??
Thank you.
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https://github.com/dmlc/mxnet-memonger
You can refer to the memory optimization in MXNet
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Did you train a model using MXNet and then convert the MXNet model into Caffe model??
Thank you.
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We implement this method in our training platform in caffe.
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Is it publicly available?
If not, could you please let me know how you implement this method in Caffe?
Thank you
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No it is not public. You can read the paper for the theory, and refer the code in MXNet. It is doable.
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@hszhao @wistone Could you explain what's mean of the three accuracy output on training phase? There only one SegAccuracy, so I think there should one accuracy output, why three ones occurs?
example output:
I0605 10:56:09.200160 16167 solver.cpp:245] Train net output #0: accuracy = 0.971667
I0605 10:56:09.200170 16167 solver.cpp:245] Train net output #1: accuracy = 0.873694
I0605 10:56:09.200176 16167 solver.cpp:245] Train net output #2: accuracy = 0.803811
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Wait, @LearnerInGithub , where did you get training files?!
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@DonghyunK Can you public script for training?
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Related Issues (20)
- PASCAL VOC 2012
- Questions about PSPNet. HOT 6
- small size
- Reason to have a fixed inference size (473x473) HOT 1
- 请问下怎么将BVLC/caffe下的bn转换到BN?
- question about pooling layer
- Make Runtest Error HOT 2
- Invalid MEX-file '/media/sgp1053c/DATA/PSPNET-cudnn5/matlab/+caffe/private/caffe_.mexa64
- Make Error
- model performance
- C++ Prediction/Segmentation Code for PSPNet
- Could you provide the prototxt file for training PSPNet?
- [FIXED] Why are scale_factors used to scale pixel values? HOT 1
- Evaluation code
- OSX hdf5 make error HOT 1
- Hi, GPU GTX 1070 8G memory is not enough? HOT 1
- compile using cmake failed! (undefined reference to `pthread_create')
- Problem with evaluation
- math_functions.cu:375 [Check failed: status== CURAND_STATUS_SUCCESS (201 VS. 0) CURAND_STATUS_LAUNCH_FAILURE]
- TBD
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