Network in Network CIFAR10

readme.md

Info

name: Network in Network CIFAR10 Model

caffemodel: cifar10_nin.caffemodel

caffemodel_url: https://www.dropbox.com/s/blrajqirr1p31v0/cifar10_nin.caffemodel?dl=1

license: BSD

sha1: 8e89c8fcd46e02780e16c867a5308e7bb7af0803

caffe_commit: c69b3b49084b503e23b95dc387329975245949c2

gist_id: e56253735ef32c3c296d

Descriptions

This model is a 3 layer Network in Network model trained on CIFAR10 dataset.

The performance of this model on validation set is 89.6% The detailed descriptions are in the paper Network in Network

The preprocessed CIFAR10 data is downloadable in lmdb format here:

• Preprocessed training data
• Preprocessed testing data

License

The data used to train this model comes from http://www.cs.toronto.edu/~kriz/cifar.html Please follow the license there if used.

solver.prototxt

net: "train_test.prototxt"
test_iter: 100
test_interval: 500
base_lr: 0.1
momentum: 0.9
weight_decay: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 100
max_iter: 120000
snapshot: 10000
snapshot_prefix: "cifar10_nin"
solver_mode: GPU

train_val.prototxt

name: "CIFAR10_full"
layers {
  name: "cifar"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "cifar-train-leveldb"
    batch_size: 128
  }
  include: { phase: TRAIN }
}
layers {
  name: "cifar"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "cifar-test-leveldb"
    batch_size: 100
  }
  include: { phase: TEST }
}
layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "cccp1"
  type: CONVOLUTION
  bottom: "conv1"
  top: "cccp1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 160
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp1"
  type: RELU
  bottom: "cccp1"
  top: "cccp1"
}
layers {
  name: "cccp2"
  type: CONVOLUTION
  bottom: "cccp1"
  top: "cccp2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp2"
  type: RELU
  bottom: "cccp2"
  top: "cccp2"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "cccp2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "drop3"
  type: DROPOUT
  bottom: "pool1"
  top: "pool1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "pool1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "cccp3"
  type: CONVOLUTION
  bottom: "conv2"
  top: "cccp3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp3"
  type: RELU
  bottom: "cccp3"
  top: "cccp3"
}
layers {
  name: "cccp4"
  type: CONVOLUTION
  bottom: "cccp3"
  top: "cccp4"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp4"
  type: RELU
  bottom: "cccp4"
  top: "cccp4"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "cccp4"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "drop6"
  type: DROPOUT
  bottom: "pool2"
  top: "pool2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "pool2"
  top: "conv3"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 1.
  weight_decay: 0.
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "cccp5"
  type: CONVOLUTION
  bottom: "conv3"
  top: "cccp5"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp5"
  type: RELU
  bottom: "cccp5"
  top: "cccp5"
}
layers {
  name: "cccp6"
  type: CONVOLUTION
  bottom: "cccp5"
  top: "cccp6"
  blobs_lr: 0.1
  blobs_lr: 0.1
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 10
    group: 1
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cccp6"
  type: RELU
  bottom: "cccp6"
  top: "cccp6"
}
layers {
  name: "pool3"
  type: POOLING
  bottom: "cccp6"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 8
    stride: 1
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "pool3"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}
layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "pool3"
  bottom: "label"
  top: "loss"
}

@kgl-prml, we followed the paper outline and did global contrast normalization and ZCA whitening and we were able to reproduce %10.4 error rate on cifar-10

Has anybody been able to reproduce the paper results on cifar-100? We could get the paper result on cifar-10 (%10 error rate) but the same network doesn't converge to less than %75 error rate on cifar-100! Do you have any suggestion?

Hi, @hengck23. I try to reproduce NIN with caffe and get nearly 88% accuracy follow your advice.Now, I encounter some questions.
I want to use the network architecture to test a new image in python,
1.How should I implement GCN? I guess subtract mean and divide std, is it right?
2.Is the dropout layer needed to test a new image?
By the way, do you know how to visualize the feature map as shown in the paper? The patch size seems smaller than the input.

I also want to implement this paper (NIN). But i am new in caffe and deep learning. I download the pre-trained model and the cifar-test-leveldb file from the website of the author(https://gist.github.com/mavenlin/e56253735ef32c3c296d), but my caffe cannot read the cifar-test-leveldb. What is wrong with this? and how can implement this experiment easily? thanks so much!

@mavenlin Can you upload the deploy.prototxt or show how to convert train_val.prototxt to deploy.prototxt as I don't know the input params. I want to take the model weights and biases without downloading the entire dataset.

@hengck23
Hello,I also meet the problem that I can't read the downloaded data,I see you change your leveldb.Can you tell me how to change it ? I don't understand it.
I have to change my leveldb version to this "https://github.com/maxd/leveldbwin".

@hengck23
How to use the leveldbwin? I can't understand it.I am a fresher in caffe and hope to get the solution from you sincerely.

@Perseus14, did you find the deploy.prototxt ?

i want to finetune the nin model just for 3 classes, but i dont know what layer must be changed? i tried to rename cccp6 but it didnt work. anyone has idea about it? please share

i'm trying to train nin with my data, and i don't want to use pretrained weights, instead i'm going to try with random weights and use the nin network, my data consists of 3 classes and i just tried to to change the output of cccp6 from 10 to 3. when i run it i got wrong constant accuracy 0.3368 in every testing process. so if anyone has idea to help me with this error?
i did'nt change anything except the cccp6' output 10 to 3. thnx very much

Does anyone have a deploy.prototxt for this model?