vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/dnns_easily_fooled/model/image_list.txt

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+/home/anh/workspace/sferes/exp/images/imagenet/hen_256.png 1

modules/dnns_easily_fooled/model/imagenet_deploy_image_memory_data.prototxt

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+name: "CaffeNet"
+layers {
+  name: "data"
+  type: IMAGE_DATA
+  top: "data"
+  top: "label"
+  image_data_param {
+    source: "/home/anh/workspace/sferes/exp/images/imagenet/image_list.txt"
+    mean_file: "/home/anh/src/caffe/data/ilsvrc12/imagenet_mean.binaryproto"
+    batch_size: 10
+    crop_size: 227
+    mirror: false
+    new_height: 256
+    new_width: 256
+    images_in_color: true
+  }
+}
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  inner_product_param {
+    num_output: 1000
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8"
+  top: "prob"
+}

modules/dnns_easily_fooled/model/lenet/lenet_image_memory_data.prototxt

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+name: "LeNet"
+layers {
+  name: "data"
+  type: IMAGE_DATA
+  top: "data"
+  top: "label"
+  image_data_param {
+    source: "/project/EvolvingAI/anguyen8/model/mnist_image_list.txt"
+    mean_file: "/project/EvolvingAI/anguyen8/model/mnist_mean.binaryproto"
+    batch_size: 1
+    mirror: false
+    new_height: 28
+    new_width: 28
+    scale: 0.00390625
+    images_in_color: false
+  }
+}
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "pool1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "ip1"
+  type: INNER_PRODUCT
+  bottom: "pool2"
+  top: "ip1"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "ip1"
+  top: "ip1"
+}
+layers {
+  name: "ip2"
+  type: INNER_PRODUCT
+  bottom: "ip1"
+  top: "ip2"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "ip2"
+  top: "prob"
+}

modules/dnns_easily_fooled/model/lenet/mnist_image_list.txt

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+/project/EvolvingAI/anguyen8/model/mnist_sample_image.png 0