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resnetcifar's Introduction

ResNet 20/32/44/56/110 for CIFAR10 with caffe

  1. get cifar10 python version, then create a soft link ln -s cifar-10-batches-py here
  2. use data_utils.py to generate 4 pixel padded training data and testing data. Horizontal flip and random crop are performed on the fly while training.
  3. use net_generator.py to generate solver.prototxt and trainval.prototxt, you can generate resnet or plain net of depth 20 44 56 110, or even deeper if you want. you just need to change n according to depth=6n+2
  4. use train.sh to train it
  5. specify caffe path in cfgs.py and use plot.py to generate beautful loss plots.

results are consistent with original paper

seems there's no much difference between resnet-20 and plain-20. However, from the second plot, you can see that plain-110 have difficulty to converge. a b

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