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Torch implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).

Lua 100.00%

residual-networks network-architecture imagenet deep-learning cnn-architecture resnet cifar-10 cifar-100 torch7

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pyramidnet's Issues

the basic layer is different from resnet

in resnet basic block, there is no bn in the head (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua#L53)

Is there any reason to add bn in the head of the basic block in PyramidNet (https://github.com/jhkim89/PyramidNet/blob/master/addpyramidnet.lua#L53)?

Training depth-272 net out of memory.

I encounter out-of-memory problem each time I train the net with depth=272, alpha = 200. My training configuration is as follows:
th main.lua -netType addpyramidnet -depth 272 -batchSize 128 -nGPU 4 -shareGradInput true
and the out-of-memory problem occurs when the 2nd epoch begins.
After I changed batchSize into 120 and nGPU into 6, the problems occurs when the 3rd epoch begins.
Are there any solutions?

Why always use bottleneck?

in resnet-34, the block is basicBlock.

but in the code, you always use bottleneck (https://github.com/jhkim89/PyramidNet/blob/master/addpyramidnet.lua#L125), do we also need bottleneck when using resnet-34?

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