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Question about machine #18
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Hi, Actually the problem might lie on the CPU memory (not the GPU memory). The RPN need to pre-compute and save all the anchors for all images, and store them in the memory during the training time. The Caltech 10x dataset contains 4w+ images, and it might need a large memory. Thus, it might help when reduce the training data, e.g. reduce to Caltech 3x or Caltech 5x. |
@zhangliliang Thank you! |
Hi, Change the skip in the extract_img_anno.m. Skip = 3 coresponds to caltech10x, skip = 6 corespond a to caltech5x. |
First of all, thank you for your job!
However, when I run script_rpn_pedestrian_VGG16_caltech.m , my computer jamed and can not do anything, staying in the stage "stage one RPN"! Then, I waited for about 1 hour, it's still jamed.
So I just want to ask the minimum requirement about machine.
Here is my machine : One Titan x GPU; 32G memory. Is enough for this experiment?
Thank you very much!
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