Open sunshineatnoon opened 9 years ago
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@sunshineatnoon @kaifeichen @rbgirshick I got the same problem. Did you solve this problem ???
@haimansx No, I have not solved the problem, I think the only way to solve the problem is by implementing the two functions above in c++
Hi @haimansx, I think @sunshineatnoon is right. I just used the GPU version and it worked.
Hi @sunshineatnoon I have followed your blog to train Fast-RCNN my own dataset and i have stumbled upon this error. I am using CPU Mode. Were you able to solve this issue?
@kaifeichen Does that mean Fast-RCNN is not implemented for CPU mode? Any updates on this would help me.
@tanjoreg No, I haven't solved this issue. Fast Rcnn can only be trained on GPU as far as I know. But it can be tested on CPU.
@sunshineatnoon Thank you very much,for the swift reply. Your blog helped me a lot. I have tested the imagenet demo on CPU and it works. I was trying to train my own dataset, which resulted in the error. Does that mean I have hit the wall with CPU mode?
As I mentioned above, I think the way to solve this issue is to implement the two functions above in c++.
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@sunshineatnoon: Were you able to train R-CNN in cpu? or is it still in the status of not running training on cpu mode?
I am facing the same problem!
Any help?
If someone implemented those 2 functions in cpp, can he post the code?
@HamdiHamed1992 , Do you solve this problem. There is someone write these 2 functions, but it still not work.
@PeterJiangwy sorry no. I am using a CPU to train CNN and interested to segmentation not object localisation. Therefore i did not focus alot with RCNN, Fast RCNN and Faster RCNN. I can suggest you to use other network architectures and change just the final classification layers to your wanted task. Choose some already existing models that can run on CPU... (Segnet 4 example, based on VGG16 Model arch). If you need anything, feel free to ask.
@ HamdiHamed1992, Thank you. I’m newer to this scope. Now I’m trying to do something about object detection. Do you have any suggestions? Thank you very much for your help.
在 2018年1月16日,18:22,HamdiHamed1992 notifications@github.com 写道:
@PeterJiangwy https://github.com/peterjiangwy sorry no. I am using a CPU to train CNN and interested to segmentation not object localisation. Therefore i did not focus alot with RCNN, Fast RCNN and Faster RCNN. I can suggest you to use other network architectures and change just the final classification layers to your wanted task. Choose some already existing models that can run on CPU... (Segnet 4 example, based on VGG16 Model arch). If you need anything, feel free to ask.
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I modified train_net.py to use cpu for training. I also disabled MATLAB and use the pre-computed selective search bounding box for VOC2007. However, I got this error during training:
I looked into fast-rcnn/caffe-fast-rcnn/src/caffe/layers/smooth_L1_loss_layer.cpp and found following code:
So I guess this layer is only implemented for GPU, not for CPU. Does this mean I can't use CPU to train fast-rcnn?