From 8e4fe30db60e8433a55e4018806d8dbdeb8bb696 Mon Sep 17 00:00:00 2001 From: Dmitry Kurtaev Date: Sat, 3 Mar 2018 16:43:21 +0300 Subject: [PATCH] Unite deep learning image classification samples --- samples/data/dnn/.gitignore | 1 - .../data/dnn/MobileNetSSD_300x300.prototxt | 3102 ----------------- ...GG_VOC0712_SSD_300x300_iter_60000.prototxt | 1546 -------- samples/data/dnn/bvlc_googlenet.prototxt | 2156 ------------ samples/data/dnn/enet-classes.txt | 20 - samples/data/dnn/fcn32s-heavy-pascal.prototxt | 502 --- samples/data/dnn/fcn8s-heavy-pascal.prototxt | 612 ---- samples/data/dnn/pascal-classes.txt | 21 - samples/data/dnn/rgb.jpg | Bin 47099 -> 0 bytes samples/data/dnn/space_shuttle.jpg | Bin 27598 -> 0 bytes samples/data/dnn/synset_words.txt | 1000 ------ samples/dnn/README.md | 8 + samples/dnn/caffe_googlenet.cpp | 181 - samples/dnn/classification.cpp | 153 + samples/dnn/classification.py | 98 + .../dnn/classification_classes_ILSVRC2012.txt | 1000 ++++++ samples/dnn/googlenet_python.py | 24 - samples/dnn/object_detection.cpp | 29 +- samples/dnn/object_detection.py | 19 +- samples/dnn/squeezenet_halide.cpp | 110 - samples/dnn/tf_inception.cpp | 154 - 21 files changed, 1292 insertions(+), 9444 deletions(-) delete mode 100644 samples/data/dnn/.gitignore delete mode 100644 samples/data/dnn/MobileNetSSD_300x300.prototxt delete mode 100644 samples/data/dnn/VGG_VOC0712_SSD_300x300_iter_60000.prototxt delete mode 100644 samples/data/dnn/bvlc_googlenet.prototxt delete mode 100644 samples/data/dnn/enet-classes.txt delete mode 100644 samples/data/dnn/fcn32s-heavy-pascal.prototxt delete mode 100644 samples/data/dnn/fcn8s-heavy-pascal.prototxt delete mode 100644 samples/data/dnn/pascal-classes.txt delete mode 100644 samples/data/dnn/rgb.jpg delete mode 100644 samples/data/dnn/space_shuttle.jpg delete mode 100644 samples/data/dnn/synset_words.txt delete mode 100644 samples/dnn/caffe_googlenet.cpp create mode 100644 samples/dnn/classification.cpp create mode 100644 samples/dnn/classification.py create mode 100644 samples/dnn/classification_classes_ILSVRC2012.txt delete mode 100644 samples/dnn/googlenet_python.py delete mode 100644 samples/dnn/squeezenet_halide.cpp delete mode 100644 samples/dnn/tf_inception.cpp diff --git a/samples/data/dnn/.gitignore b/samples/data/dnn/.gitignore deleted file mode 100644 index be7186603d..0000000000 --- a/samples/data/dnn/.gitignore +++ /dev/null @@ -1 +0,0 @@ -*.caffemodel diff --git a/samples/data/dnn/MobileNetSSD_300x300.prototxt b/samples/data/dnn/MobileNetSSD_300x300.prototxt deleted file mode 100644 index def19412b6..0000000000 --- a/samples/data/dnn/MobileNetSSD_300x300.prototxt +++ /dev/null @@ -1,3102 +0,0 @@ -name: "MobileNet-SSD" -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 300 - dim: 300 -} -layer { - name: "conv0" - type: "Convolution" - bottom: "data" - top: "conv0" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 32 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv0/bn" - type: "BatchNorm" - bottom: "conv0" - top: "conv0" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv0/scale" - type: "Scale" - bottom: "conv0" - top: "conv0" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv0/relu" - type: "ReLU" - bottom: "conv0" - top: "conv0" -} -layer { - name: "conv1/dw" - type: "Convolution" - bottom: "conv0" - top: "conv1/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 32 - bias_term: false - pad: 1 - kernel_size: 3 - group: 32 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv1/dw/bn" - type: "BatchNorm" - bottom: "conv1/dw" - top: "conv1/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv1/dw/scale" - type: "Scale" - bottom: "conv1/dw" - top: "conv1/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv1/dw/relu" - type: "ReLU" - bottom: "conv1/dw" - top: "conv1/dw" -} -layer { - name: "conv1" - type: "Convolution" - bottom: "conv1/dw" - top: "conv1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 64 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv1/bn" - type: "BatchNorm" - bottom: "conv1" - top: "conv1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv1/scale" - type: "Scale" - bottom: "conv1" - top: "conv1" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv1/relu" - type: "ReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "conv2/dw" - type: "Convolution" - bottom: "conv1" - top: "conv2/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 64 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - group: 64 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv2/dw/bn" - type: "BatchNorm" - bottom: "conv2/dw" - top: "conv2/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv2/dw/scale" - type: "Scale" - bottom: "conv2/dw" - top: "conv2/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv2/dw/relu" - type: "ReLU" - bottom: "conv2/dw" - top: "conv2/dw" -} -layer { - name: "conv2" - type: "Convolution" - bottom: "conv2/dw" - top: "conv2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv2/bn" - type: "BatchNorm" - bottom: "conv2" - top: "conv2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv2/scale" - type: "Scale" - bottom: "conv2" - top: "conv2" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv2/relu" - type: "ReLU" - bottom: "conv2" - top: "conv2" -} -layer { - name: "conv3/dw" - type: "Convolution" - bottom: "conv2" - top: "conv3/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - pad: 1 - kernel_size: 3 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv3/dw/bn" - type: "BatchNorm" - bottom: "conv3/dw" - top: "conv3/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv3/dw/scale" - type: "Scale" - bottom: "conv3/dw" - top: "conv3/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv3/dw/relu" - type: "ReLU" - bottom: "conv3/dw" - top: "conv3/dw" -} -layer { - name: "conv3" - type: "Convolution" - bottom: "conv3/dw" - top: "conv3" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv3/bn" - type: "BatchNorm" - bottom: "conv3" - top: "conv3" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv3/scale" - type: "Scale" - bottom: "conv3" - top: "conv3" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv3/relu" - type: "ReLU" - bottom: "conv3" - top: "conv3" -} -layer { - name: "conv4/dw" - type: "Convolution" - bottom: "conv3" - top: "conv4/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv4/dw/bn" - type: "BatchNorm" - bottom: "conv4/dw" - top: "conv4/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv4/dw/scale" - type: "Scale" - bottom: "conv4/dw" - top: "conv4/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv4/dw/relu" - type: "ReLU" - bottom: "conv4/dw" - top: "conv4/dw" -} -layer { - name: "conv4" - type: "Convolution" - bottom: "conv4/dw" - top: "conv4" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv4/bn" - type: "BatchNorm" - bottom: "conv4" - top: "conv4" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv4/scale" - type: "Scale" - bottom: "conv4" - top: "conv4" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv4/relu" - type: "ReLU" - bottom: "conv4" - top: "conv4" -} -layer { - name: "conv5/dw" - type: "Convolution" - bottom: "conv4" - top: "conv5/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - pad: 1 - kernel_size: 3 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv5/dw/bn" - type: "BatchNorm" - bottom: "conv5/dw" - top: "conv5/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv5/dw/scale" - type: "Scale" - bottom: "conv5/dw" - top: "conv5/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv5/dw/relu" - type: "ReLU" - bottom: "conv5/dw" - top: "conv5/dw" -} -layer { - name: "conv5" - type: "Convolution" - bottom: "conv5/dw" - top: "conv5" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv5/bn" - type: "BatchNorm" - bottom: "conv5" - top: "conv5" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv5/scale" - type: "Scale" - bottom: "conv5" - top: "conv5" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv5/relu" - type: "ReLU" - bottom: "conv5" - top: "conv5" -} -layer { - name: "conv6/dw" - type: "Convolution" - bottom: "conv5" - top: "conv6/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv6/dw/bn" - type: "BatchNorm" - bottom: "conv6/dw" - top: "conv6/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv6/dw/scale" - type: "Scale" - bottom: "conv6/dw" - top: "conv6/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv6/dw/relu" - type: "ReLU" - bottom: "conv6/dw" - top: "conv6/dw" -} -layer { - name: "conv6" - type: "Convolution" - bottom: "conv6/dw" - top: "conv6" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv6/bn" - type: "BatchNorm" - bottom: "conv6" - top: "conv6" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv6/scale" - type: "Scale" - bottom: "conv6" - top: "conv6" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv6/relu" - type: "ReLU" - bottom: "conv6" - top: "conv6" -} -layer { - name: "conv7/dw" - type: "Convolution" - bottom: "conv6" - top: "conv7/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv7/dw/bn" - type: "BatchNorm" - bottom: "conv7/dw" - top: "conv7/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv7/dw/scale" - type: "Scale" - bottom: "conv7/dw" - top: "conv7/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv7/dw/relu" - type: "ReLU" - bottom: "conv7/dw" - top: "conv7/dw" -} -layer { - name: "conv7" - type: "Convolution" - bottom: "conv7/dw" - top: "conv7" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv7/bn" - type: "BatchNorm" - bottom: "conv7" - top: "conv7" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv7/scale" - type: "Scale" - bottom: "conv7" - top: "conv7" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv7/relu" - type: "ReLU" - bottom: "conv7" - top: "conv7" -} -layer { - name: "conv8/dw" - type: "Convolution" - bottom: "conv7" - top: "conv8/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv8/dw/bn" - type: "BatchNorm" - bottom: "conv8/dw" - top: "conv8/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv8/dw/scale" - type: "Scale" - bottom: "conv8/dw" - top: "conv8/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv8/dw/relu" - type: "ReLU" - bottom: "conv8/dw" - top: "conv8/dw" -} -layer { - name: "conv8" - type: "Convolution" - bottom: "conv8/dw" - top: "conv8" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv8/bn" - type: "BatchNorm" - bottom: "conv8" - top: "conv8" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv8/scale" - type: "Scale" - bottom: "conv8" - top: "conv8" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv8/relu" - type: "ReLU" - bottom: "conv8" - top: "conv8" -} -layer { - name: "conv9/dw" - type: "Convolution" - bottom: "conv8" - top: "conv9/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv9/dw/bn" - type: "BatchNorm" - bottom: "conv9/dw" - top: "conv9/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv9/dw/scale" - type: "Scale" - bottom: "conv9/dw" - top: "conv9/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv9/dw/relu" - type: "ReLU" - bottom: "conv9/dw" - top: "conv9/dw" -} -layer { - name: "conv9" - type: "Convolution" - bottom: "conv9/dw" - top: "conv9" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv9/bn" - type: "BatchNorm" - bottom: "conv9" - top: "conv9" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv9/scale" - type: "Scale" - bottom: "conv9" - top: "conv9" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv9/relu" - type: "ReLU" - bottom: "conv9" - top: "conv9" -} -layer { - name: "conv10/dw" - type: "Convolution" - bottom: "conv9" - top: "conv10/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv10/dw/bn" - type: "BatchNorm" - bottom: "conv10/dw" - top: "conv10/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv10/dw/scale" - type: "Scale" - bottom: "conv10/dw" - top: "conv10/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv10/dw/relu" - type: "ReLU" - bottom: "conv10/dw" - top: "conv10/dw" -} -layer { - name: "conv10" - type: "Convolution" - bottom: "conv10/dw" - top: "conv10" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv10/bn" - type: "BatchNorm" - bottom: "conv10" - top: "conv10" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv10/scale" - type: "Scale" - bottom: "conv10" - top: "conv10" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv10/relu" - type: "ReLU" - bottom: "conv10" - top: "conv10" -} -layer { - name: "conv11/dw" - type: "Convolution" - bottom: "conv10" - top: "conv11/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv11/dw/bn" - type: "BatchNorm" - bottom: "conv11/dw" - top: "conv11/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv11/dw/scale" - type: "Scale" - bottom: "conv11/dw" - top: "conv11/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv11/dw/relu" - type: "ReLU" - bottom: "conv11/dw" - top: "conv11/dw" -} -layer { - name: "conv11" - type: "Convolution" - bottom: "conv11/dw" - top: "conv11" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv11/bn" - type: "BatchNorm" - bottom: "conv11" - top: "conv11" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv11/scale" - type: "Scale" - bottom: "conv11" - top: "conv11" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv11/relu" - type: "ReLU" - bottom: "conv11" - top: "conv11" -} -layer { - name: "conv12/dw" - type: "Convolution" - bottom: "conv11" - top: "conv12/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv12/dw/bn" - type: "BatchNorm" - bottom: "conv12/dw" - top: "conv12/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv12/dw/scale" - type: "Scale" - bottom: "conv12/dw" - top: "conv12/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv12/dw/relu" - type: "ReLU" - bottom: "conv12/dw" - top: "conv12/dw" -} -layer { - name: "conv12" - type: "Convolution" - bottom: "conv12/dw" - top: "conv12" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 1024 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv12/bn" - type: "BatchNorm" - bottom: "conv12" - top: "conv12" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv12/scale" - type: "Scale" - bottom: "conv12" - top: "conv12" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv12/relu" - type: "ReLU" - bottom: "conv12" - top: "conv12" -} -layer { - name: "conv13/dw" - type: "Convolution" - bottom: "conv12" - top: "conv13/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 1024 - bias_term: false - pad: 1 - kernel_size: 3 - group: 1024 - engine: CAFFE - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv13/dw/bn" - type: "BatchNorm" - bottom: "conv13/dw" - top: "conv13/dw" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv13/dw/scale" - type: "Scale" - bottom: "conv13/dw" - top: "conv13/dw" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv13/dw/relu" - type: "ReLU" - bottom: "conv13/dw" - top: "conv13/dw" -} -layer { - name: "conv13" - type: "Convolution" - bottom: "conv13/dw" - top: "conv13" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 1024 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv13/bn" - type: "BatchNorm" - bottom: "conv13" - top: "conv13" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv13/scale" - type: "Scale" - bottom: "conv13" - top: "conv13" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv13/relu" - type: "ReLU" - bottom: "conv13" - top: "conv13" -} -layer { - name: "conv14_1" - type: "Convolution" - bottom: "conv13" - top: "conv14_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv14_1/bn" - type: "BatchNorm" - bottom: "conv14_1" - top: "conv14_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv14_1/scale" - type: "Scale" - bottom: "conv14_1" - top: "conv14_1" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv14_1/relu" - type: "ReLU" - bottom: "conv14_1" - top: "conv14_1" -} -layer { - name: "conv14_2" - type: "Convolution" - bottom: "conv14_1" - top: "conv14_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 512 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv14_2/bn" - type: "BatchNorm" - bottom: "conv14_2" - top: "conv14_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv14_2/scale" - type: "Scale" - bottom: "conv14_2" - top: "conv14_2" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv14_2/relu" - type: "ReLU" - bottom: "conv14_2" - top: "conv14_2" -} -layer { - name: "conv15_1" - type: "Convolution" - bottom: "conv14_2" - top: "conv15_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv15_1/bn" - type: "BatchNorm" - bottom: "conv15_1" - top: "conv15_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv15_1/scale" - type: "Scale" - bottom: "conv15_1" - top: "conv15_1" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv15_1/relu" - type: "ReLU" - bottom: "conv15_1" - top: "conv15_1" -} -layer { - name: "conv15_2" - type: "Convolution" - bottom: "conv15_1" - top: "conv15_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv15_2/bn" - type: "BatchNorm" - bottom: "conv15_2" - top: "conv15_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv15_2/scale" - type: "Scale" - bottom: "conv15_2" - top: "conv15_2" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv15_2/relu" - type: "ReLU" - bottom: "conv15_2" - top: "conv15_2" -} -layer { - name: "conv16_1" - type: "Convolution" - bottom: "conv15_2" - top: "conv16_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv16_1/bn" - type: "BatchNorm" - bottom: "conv16_1" - top: "conv16_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv16_1/scale" - type: "Scale" - bottom: "conv16_1" - top: "conv16_1" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv16_1/relu" - type: "ReLU" - bottom: "conv16_1" - top: "conv16_1" -} -layer { - name: "conv16_2" - type: "Convolution" - bottom: "conv16_1" - top: "conv16_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 256 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv16_2/bn" - type: "BatchNorm" - bottom: "conv16_2" - top: "conv16_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv16_2/scale" - type: "Scale" - bottom: "conv16_2" - top: "conv16_2" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv16_2/relu" - type: "ReLU" - bottom: "conv16_2" - top: "conv16_2" -} -layer { - name: "conv17_1" - type: "Convolution" - bottom: "conv16_2" - top: "conv17_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 64 - bias_term: false - kernel_size: 1 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv17_1/bn" - type: "BatchNorm" - bottom: "conv17_1" - top: "conv17_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv17_1/scale" - type: "Scale" - bottom: "conv17_1" - top: "conv17_1" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv17_1/relu" - type: "ReLU" - bottom: "conv17_1" - top: "conv17_1" -} -layer { - name: "conv17_2" - type: "Convolution" - bottom: "conv17_1" - top: "conv17_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - convolution_param { - num_output: 128 - bias_term: false - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - } -} -layer { - name: "conv17_2/bn" - type: "BatchNorm" - bottom: "conv17_2" - top: "conv17_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } -} -layer { - name: "conv17_2/scale" - type: "Scale" - bottom: "conv17_2" - top: "conv17_2" - param { - lr_mult: 0.1 - decay_mult: 0.0 - } - param { - lr_mult: 0.2 - decay_mult: 0.0 - } - scale_param { - filler { - value: 1 - } - bias_term: true - bias_filler { - value: 0 - } - } -} -layer { - name: "conv17_2/relu" - type: "ReLU" - bottom: "conv17_2" - top: "conv17_2" -} -layer { - name: "conv11_mbox_loc" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 12 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_loc_perm" - type: "Permute" - bottom: "conv11_mbox_loc" - top: "conv11_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_loc_flat" - type: "Flatten" - bottom: "conv11_mbox_loc_perm" - top: "conv11_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_conf" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 63 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_conf_perm" - type: "Permute" - bottom: "conv11_mbox_conf" - top: "conv11_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_conf_flat" - type: "Flatten" - bottom: "conv11_mbox_conf_perm" - top: "conv11_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_priorbox" - type: "PriorBox" - bottom: "conv11" - bottom: "data" - top: "conv11_mbox_priorbox" - prior_box_param { - min_size: 60.0 - aspect_ratio: 2.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv13_mbox_loc" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_loc_perm" - type: "Permute" - bottom: "conv13_mbox_loc" - top: "conv13_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_loc_flat" - type: "Flatten" - bottom: "conv13_mbox_loc_perm" - top: "conv13_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_conf" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_conf_perm" - type: "Permute" - bottom: "conv13_mbox_conf" - top: "conv13_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_conf_flat" - type: "Flatten" - bottom: "conv13_mbox_conf_perm" - top: "conv13_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_priorbox" - type: "PriorBox" - bottom: "conv13" - bottom: "data" - top: "conv13_mbox_priorbox" - prior_box_param { - min_size: 105.0 - max_size: 150.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv14_2_mbox_loc" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_loc_perm" - type: "Permute" - bottom: "conv14_2_mbox_loc" - top: "conv14_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv14_2_mbox_loc_perm" - top: "conv14_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_conf" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_conf_perm" - type: "Permute" - bottom: "conv14_2_mbox_conf" - top: "conv14_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv14_2_mbox_conf_perm" - top: "conv14_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv14_2" - bottom: "data" - top: "conv14_2_mbox_priorbox" - prior_box_param { - min_size: 150.0 - max_size: 195.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv15_2_mbox_loc" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_loc_perm" - type: "Permute" - bottom: "conv15_2_mbox_loc" - top: "conv15_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv15_2_mbox_loc_perm" - top: "conv15_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_conf" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_conf_perm" - type: "Permute" - bottom: "conv15_2_mbox_conf" - top: "conv15_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv15_2_mbox_conf_perm" - top: "conv15_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv15_2" - bottom: "data" - top: "conv15_2_mbox_priorbox" - prior_box_param { - min_size: 195.0 - max_size: 240.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv16_2_mbox_loc" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_loc_perm" - type: "Permute" - bottom: "conv16_2_mbox_loc" - top: "conv16_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv16_2_mbox_loc_perm" - top: "conv16_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_conf" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_conf_perm" - type: "Permute" - bottom: "conv16_2_mbox_conf" - top: "conv16_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv16_2_mbox_conf_perm" - top: "conv16_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv16_2" - bottom: "data" - top: "conv16_2_mbox_priorbox" - prior_box_param { - min_size: 240.0 - max_size: 285.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv17_2_mbox_loc" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_loc_perm" - type: "Permute" - bottom: "conv17_2_mbox_loc" - top: "conv17_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv17_2_mbox_loc_perm" - top: "conv17_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_conf" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_conf_perm" - type: "Permute" - bottom: "conv17_2_mbox_conf" - top: "conv17_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv17_2_mbox_conf_perm" - top: "conv17_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv17_2" - bottom: "data" - top: "conv17_2_mbox_priorbox" - prior_box_param { - min_size: 285.0 - max_size: 300.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "mbox_loc" - type: "Concat" - bottom: "conv11_mbox_loc_flat" - bottom: "conv13_mbox_loc_flat" - bottom: "conv14_2_mbox_loc_flat" - bottom: "conv15_2_mbox_loc_flat" - bottom: "conv16_2_mbox_loc_flat" - bottom: "conv17_2_mbox_loc_flat" - top: "mbox_loc" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_conf" - type: "Concat" - bottom: "conv11_mbox_conf_flat" - bottom: "conv13_mbox_conf_flat" - bottom: "conv14_2_mbox_conf_flat" - bottom: "conv15_2_mbox_conf_flat" - bottom: "conv16_2_mbox_conf_flat" - bottom: "conv17_2_mbox_conf_flat" - top: "mbox_conf" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_priorbox" - type: "Concat" - bottom: "conv11_mbox_priorbox" - bottom: "conv13_mbox_priorbox" - bottom: "conv14_2_mbox_priorbox" - bottom: "conv15_2_mbox_priorbox" - bottom: "conv16_2_mbox_priorbox" - bottom: "conv17_2_mbox_priorbox" - top: "mbox_priorbox" - concat_param { - axis: 2 - } -} -layer { - name: "mbox_conf_reshape" - type: "Reshape" - bottom: "mbox_conf" - top: "mbox_conf_reshape" - reshape_param { - shape { - dim: 0 - dim: -1 - dim: 21 - } - } -} -layer { - name: "mbox_conf_softmax" - type: "Softmax" - bottom: "mbox_conf_reshape" - top: "mbox_conf_softmax" - softmax_param { - axis: 2 - } -} -layer { - name: "mbox_conf_flatten" - type: "Flatten" - bottom: "mbox_conf_softmax" - top: "mbox_conf_flatten" - flatten_param { - axis: 1 - } -} -layer { - name: "detection_out" - type: "DetectionOutput" - bottom: "mbox_loc" - bottom: "mbox_conf_flatten" - bottom: "mbox_priorbox" - top: "detection_out" - include { - phase: TEST - } - detection_output_param { - num_classes: 21 - share_location: true - background_label_id: 0 - nms_param { - nms_threshold: 0.45 - top_k: 100 - } - code_type: CENTER_SIZE - keep_top_k: 100 - confidence_threshold: 0.25 - } -} \ No newline at end of file diff --git a/samples/data/dnn/VGG_VOC0712_SSD_300x300_iter_60000.prototxt b/samples/data/dnn/VGG_VOC0712_SSD_300x300_iter_60000.prototxt deleted file mode 100644 index 77a2365553..0000000000 --- a/samples/data/dnn/VGG_VOC0712_SSD_300x300_iter_60000.prototxt +++ /dev/null @@ -1,1546 +0,0 @@ -name: "VGG_VOC0712_SSD_300x300_deploy" -input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 300 -input_dim: 300 -layer { - name: "conv1_1" - type: "Convolution" - bottom: "data" - top: "conv1_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu1_1" - type: "ReLU" - bottom: "conv1_1" - top: "conv1_1" -} -layer { - name: "conv1_2" - type: "Convolution" - bottom: "conv1_1" - top: "conv1_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu1_2" - type: "ReLU" - bottom: "conv1_2" - top: "conv1_2" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1_2" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv2_1" - type: "Convolution" - bottom: "pool1" - top: "conv2_1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu2_1" - type: "ReLU" - bottom: "conv2_1" - top: "conv2_1" -} -layer { - name: "conv2_2" - type: "Convolution" - bottom: "conv2_1" - top: "conv2_2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu2_2" - type: "ReLU" - bottom: "conv2_2" - top: "conv2_2" -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2_2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv3_1" - type: "Convolution" - bottom: "pool2" - top: "conv3_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu3_1" - type: "ReLU" - bottom: "conv3_1" - top: "conv3_1" -} -layer { - name: "conv3_2" - type: "Convolution" - bottom: "conv3_1" - top: "conv3_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu3_2" - type: "ReLU" - bottom: "conv3_2" - top: "conv3_2" -} -layer { - name: "conv3_3" - type: "Convolution" - bottom: "conv3_2" - top: "conv3_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu3_3" - type: "ReLU" - bottom: "conv3_3" - top: "conv3_3" -} -layer { - name: "pool3" - type: "Pooling" - bottom: "conv3_3" - top: "pool3" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv4_1" - type: "Convolution" - bottom: "pool3" - top: "conv4_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu4_1" - type: "ReLU" - bottom: "conv4_1" - top: "conv4_1" -} -layer { - name: "conv4_2" - type: "Convolution" - bottom: "conv4_1" - top: "conv4_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu4_2" - type: "ReLU" - bottom: "conv4_2" - top: "conv4_2" -} -layer { - name: "conv4_3" - type: "Convolution" - bottom: "conv4_2" - top: "conv4_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu4_3" - type: "ReLU" - bottom: "conv4_3" - top: "conv4_3" -} -layer { - name: "pool4" - type: "Pooling" - bottom: "conv4_3" - top: "pool4" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv5_1" - type: "Convolution" - bottom: "pool4" - top: "conv5_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu5_1" - type: "ReLU" - bottom: "conv5_1" - top: "conv5_1" -} -layer { - name: "conv5_2" - type: "Convolution" - bottom: "conv5_1" - top: "conv5_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu5_2" - type: "ReLU" - bottom: "conv5_2" - top: "conv5_2" -} -layer { - name: "conv5_3" - type: "Convolution" - bottom: "conv5_2" - top: "conv5_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu5_3" - type: "ReLU" - bottom: "conv5_3" - top: "conv5_3" -} -layer { - name: "pool5" - type: "Pooling" - bottom: "conv5_3" - top: "pool5" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "fc6" - type: "Convolution" - bottom: "pool5" - top: "fc6" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 1024 - pad: 6 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - dilation: 6 - } -} -layer { - name: "relu6" - type: "ReLU" - bottom: "fc6" - top: "fc6" -} -layer { - name: "fc7" - type: "Convolution" - bottom: "fc6" - top: "fc7" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 1024 - kernel_size: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "relu7" - type: "ReLU" - bottom: "fc7" - top: "fc7" -} -layer { - name: "conv6_1" - type: "Convolution" - bottom: "fc7" - top: "conv6_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 0 - kernel_size: 1 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6_1_relu" - type: "ReLU" - bottom: "conv6_1" - top: "conv6_1" -} -layer { - name: "conv6_2" - type: "Convolution" - bottom: "conv6_1" - top: "conv6_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6_2_relu" - type: "ReLU" - bottom: "conv6_2" - top: "conv6_2" -} -layer { - name: "conv7_1" - type: "Convolution" - bottom: "conv6_2" - top: "conv7_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 0 - kernel_size: 1 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv7_1_relu" - type: "ReLU" - bottom: "conv7_1" - top: "conv7_1" -} -layer { - name: "conv7_2" - type: "Convolution" - bottom: "conv7_1" - top: "conv7_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv7_2_relu" - type: "ReLU" - bottom: "conv7_2" - top: "conv7_2" -} -layer { - name: "conv8_1" - type: "Convolution" - bottom: "conv7_2" - top: "conv8_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 0 - kernel_size: 1 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv8_1_relu" - type: "ReLU" - bottom: "conv8_1" - top: "conv8_1" -} -layer { - name: "conv8_2" - type: "Convolution" - bottom: "conv8_1" - top: "conv8_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv8_2_relu" - type: "ReLU" - bottom: "conv8_2" - top: "conv8_2" -} -layer { - name: "pool6" - type: "Pooling" - bottom: "conv8_2" - top: "pool6" - pooling_param { - pool: AVE - global_pooling: true - } -} -layer { - name: "conv4_3_norm" - type: "NormalizeBBox" - bottom: "conv4_3" - top: "conv4_3_norm" - normalize_bbox_param { - across_spatial: false - scale_filler { - type: "constant" - value: 20 - } - channel_shared: false - } -} -layer { - name: "conv4_3_norm_mbox_loc" - type: "Convolution" - bottom: "conv4_3_norm" - top: "conv4_3_norm_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 12 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv4_3_norm_mbox_loc_perm" - type: "Permute" - bottom: "conv4_3_norm_mbox_loc" - top: "conv4_3_norm_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv4_3_norm_mbox_loc_flat" - type: "Flatten" - bottom: "conv4_3_norm_mbox_loc_perm" - top: "conv4_3_norm_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv4_3_norm_mbox_conf" - type: "Convolution" - bottom: "conv4_3_norm" - top: "conv4_3_norm_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 63 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv4_3_norm_mbox_conf_perm" - type: "Permute" - bottom: "conv4_3_norm_mbox_conf" - top: "conv4_3_norm_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv4_3_norm_mbox_conf_flat" - type: "Flatten" - bottom: "conv4_3_norm_mbox_conf_perm" - top: "conv4_3_norm_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv4_3_norm_mbox_priorbox" - type: "PriorBox" - bottom: "conv4_3_norm" - bottom: "data" - top: "conv4_3_norm_mbox_priorbox" - prior_box_param { - min_size: 30.0 - aspect_ratio: 2 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "fc7_mbox_loc" - type: "Convolution" - bottom: "fc7" - top: "fc7_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "fc7_mbox_loc_perm" - type: "Permute" - bottom: "fc7_mbox_loc" - top: "fc7_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "fc7_mbox_loc_flat" - type: "Flatten" - bottom: "fc7_mbox_loc_perm" - top: "fc7_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "fc7_mbox_conf" - type: "Convolution" - bottom: "fc7" - top: "fc7_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 126 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "fc7_mbox_conf_perm" - type: "Permute" - bottom: "fc7_mbox_conf" - top: "fc7_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "fc7_mbox_conf_flat" - type: "Flatten" - bottom: "fc7_mbox_conf_perm" - top: "fc7_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "fc7_mbox_priorbox" - type: "PriorBox" - bottom: "fc7" - bottom: "data" - top: "fc7_mbox_priorbox" - prior_box_param { - min_size: 60.0 - max_size: 114.0 - aspect_ratio: 2 - aspect_ratio: 3 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "conv6_2_mbox_loc" - type: "Convolution" - bottom: "conv6_2" - top: "conv6_2_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6_2_mbox_loc_perm" - type: "Permute" - bottom: "conv6_2_mbox_loc" - top: "conv6_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv6_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv6_2_mbox_loc_perm" - top: "conv6_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv6_2_mbox_conf" - type: "Convolution" - bottom: "conv6_2" - top: "conv6_2_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 126 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6_2_mbox_conf_perm" - type: "Permute" - bottom: "conv6_2_mbox_conf" - top: "conv6_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv6_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv6_2_mbox_conf_perm" - top: "conv6_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv6_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv6_2" - bottom: "data" - top: "conv6_2_mbox_priorbox" - prior_box_param { - min_size: 114.0 - max_size: 168.0 - aspect_ratio: 2 - aspect_ratio: 3 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "conv7_2_mbox_loc" - type: "Convolution" - bottom: "conv7_2" - top: "conv7_2_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv7_2_mbox_loc_perm" - type: "Permute" - bottom: "conv7_2_mbox_loc" - top: "conv7_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv7_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv7_2_mbox_loc_perm" - top: "conv7_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv7_2_mbox_conf" - type: "Convolution" - bottom: "conv7_2" - top: "conv7_2_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 126 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv7_2_mbox_conf_perm" - type: "Permute" - bottom: "conv7_2_mbox_conf" - top: "conv7_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv7_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv7_2_mbox_conf_perm" - top: "conv7_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv7_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv7_2" - bottom: "data" - top: "conv7_2_mbox_priorbox" - prior_box_param { - min_size: 168.0 - max_size: 222.0 - aspect_ratio: 2 - aspect_ratio: 3 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "conv8_2_mbox_loc" - type: "Convolution" - bottom: "conv8_2" - top: "conv8_2_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv8_2_mbox_loc_perm" - type: "Permute" - bottom: "conv8_2_mbox_loc" - top: "conv8_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv8_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv8_2_mbox_loc_perm" - top: "conv8_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv8_2_mbox_conf" - type: "Convolution" - bottom: "conv8_2" - top: "conv8_2_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 126 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv8_2_mbox_conf_perm" - type: "Permute" - bottom: "conv8_2_mbox_conf" - top: "conv8_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv8_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv8_2_mbox_conf_perm" - top: "conv8_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv8_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv8_2" - bottom: "data" - top: "conv8_2_mbox_priorbox" - prior_box_param { - min_size: 222.0 - max_size: 276.0 - aspect_ratio: 2 - aspect_ratio: 3 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "pool6_mbox_loc" - type: "Convolution" - bottom: "pool6" - top: "pool6_mbox_loc" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "pool6_mbox_loc_perm" - type: "Permute" - bottom: "pool6_mbox_loc" - top: "pool6_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "pool6_mbox_loc_flat" - type: "Flatten" - bottom: "pool6_mbox_loc_perm" - top: "pool6_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "pool6_mbox_conf" - type: "Convolution" - bottom: "pool6" - top: "pool6_mbox_conf" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 126 - pad: 1 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "pool6_mbox_conf_perm" - type: "Permute" - bottom: "pool6_mbox_conf" - top: "pool6_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "pool6_mbox_conf_flat" - type: "Flatten" - bottom: "pool6_mbox_conf_perm" - top: "pool6_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "pool6_mbox_priorbox" - type: "PriorBox" - bottom: "pool6" - bottom: "data" - top: "pool6_mbox_priorbox" - prior_box_param { - min_size: 276.0 - max_size: 330.0 - aspect_ratio: 2 - aspect_ratio: 3 - flip: true - clip: true - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - } -} -layer { - name: "mbox_loc" - type: "Concat" - bottom: "conv4_3_norm_mbox_loc_flat" - bottom: "fc7_mbox_loc_flat" - bottom: "conv6_2_mbox_loc_flat" - bottom: "conv7_2_mbox_loc_flat" - bottom: "conv8_2_mbox_loc_flat" - bottom: "pool6_mbox_loc_flat" - top: "mbox_loc" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_conf" - type: "Concat" - bottom: "conv4_3_norm_mbox_conf_flat" - bottom: "fc7_mbox_conf_flat" - bottom: "conv6_2_mbox_conf_flat" - bottom: "conv7_2_mbox_conf_flat" - bottom: "conv8_2_mbox_conf_flat" - bottom: "pool6_mbox_conf_flat" - top: "mbox_conf" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_priorbox" - type: "Concat" - bottom: "conv4_3_norm_mbox_priorbox" - bottom: "fc7_mbox_priorbox" - bottom: "conv6_2_mbox_priorbox" - bottom: "conv7_2_mbox_priorbox" - bottom: "conv8_2_mbox_priorbox" - bottom: "pool6_mbox_priorbox" - top: "mbox_priorbox" - concat_param { - axis: 2 - } -} -layer { - name: "mbox_conf_reshape" - type: "Reshape" - bottom: "mbox_conf" - top: "mbox_conf_reshape" - reshape_param { - shape { - dim: 0 - dim: -1 - dim: 21 - } - } -} -layer { - name: "mbox_conf_softmax" - type: "Softmax" - bottom: "mbox_conf_reshape" - top: "mbox_conf_softmax" - softmax_param { - axis: 2 - } -} -layer { - name: "mbox_conf_flatten" - type: "Flatten" - bottom: "mbox_conf_softmax" - top: "mbox_conf_flatten" - flatten_param { - axis: 1 - } -} -layer { - name: "detection_out" - type: "DetectionOutput" - bottom: "mbox_loc" - bottom: "mbox_conf_flatten" - bottom: "mbox_priorbox" - top: "detection_out" - include { - phase: TEST - } - detection_output_param { - num_classes: 21 - share_location: true - background_label_id: 0 - nms_threshold: 0.45 - top_k: 400 - code_type: CENTER_SIZE - keep_top_k: 200 - confidence_threshold: 0.01 - } -} diff --git a/samples/data/dnn/bvlc_googlenet.prototxt b/samples/data/dnn/bvlc_googlenet.prototxt deleted file mode 100644 index 4648bf26ef..0000000000 --- a/samples/data/dnn/bvlc_googlenet.prototxt +++ /dev/null @@ -1,2156 +0,0 @@ -name: "GoogleNet" -input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 224 -input_dim: 224 -layer { - name: "conv1/7x7_s2" - type: "Convolution" - bottom: "data" - top: "conv1/7x7_s2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 3 - kernel_size: 7 - stride: 2 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "conv1/relu_7x7" - type: "ReLU" - bottom: "conv1/7x7_s2" - top: "conv1/7x7_s2" -} -layer { - name: "pool1/3x3_s2" - type: "Pooling" - bottom: "conv1/7x7_s2" - top: "pool1/3x3_s2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "pool1/norm1" - type: "LRN" - bottom: "pool1/3x3_s2" - top: "pool1/norm1" - lrn_param { - local_size: 5 - alpha: 0.0001 - beta: 0.75 - } -} -layer { - name: "conv2/3x3_reduce" - type: "Convolution" - bottom: "pool1/norm1" - top: "conv2/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "conv2/relu_3x3_reduce" - type: "ReLU" - bottom: "conv2/3x3_reduce" - top: "conv2/3x3_reduce" -} -layer { - name: "conv2/3x3" - type: "Convolution" - bottom: "conv2/3x3_reduce" - top: "conv2/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 192 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "conv2/relu_3x3" - type: "ReLU" - bottom: "conv2/3x3" - top: "conv2/3x3" -} -layer { - name: "conv2/norm2" - type: "LRN" - bottom: "conv2/3x3" - top: "conv2/norm2" - lrn_param { - local_size: 5 - alpha: 0.0001 - beta: 0.75 - } -} -layer { - name: "pool2/3x3_s2" - type: "Pooling" - bottom: "conv2/norm2" - top: "pool2/3x3_s2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "inception_3a/1x1" - type: "Convolution" - bottom: "pool2/3x3_s2" - top: "inception_3a/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_1x1" - type: "ReLU" - bottom: "inception_3a/1x1" - top: "inception_3a/1x1" -} -layer { - name: "inception_3a/3x3_reduce" - type: "Convolution" - bottom: "pool2/3x3_s2" - top: "inception_3a/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 96 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_3a/3x3_reduce" - top: "inception_3a/3x3_reduce" -} -layer { - name: "inception_3a/3x3" - type: "Convolution" - bottom: "inception_3a/3x3_reduce" - top: "inception_3a/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_3x3" - type: "ReLU" - bottom: "inception_3a/3x3" - top: "inception_3a/3x3" -} -layer { - name: "inception_3a/5x5_reduce" - type: "Convolution" - bottom: "pool2/3x3_s2" - top: "inception_3a/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 16 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_3a/5x5_reduce" - top: "inception_3a/5x5_reduce" -} -layer { - name: "inception_3a/5x5" - type: "Convolution" - bottom: "inception_3a/5x5_reduce" - top: "inception_3a/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_5x5" - type: "ReLU" - bottom: "inception_3a/5x5" - top: "inception_3a/5x5" -} -layer { - name: "inception_3a/pool" - type: "Pooling" - bottom: "pool2/3x3_s2" - top: "inception_3a/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_3a/pool_proj" - type: "Convolution" - bottom: "inception_3a/pool" - top: "inception_3a/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3a/relu_pool_proj" - type: "ReLU" - bottom: "inception_3a/pool_proj" - top: "inception_3a/pool_proj" -} -layer { - name: "inception_3a/output" - type: "Concat" - bottom: "inception_3a/1x1" - bottom: "inception_3a/3x3" - bottom: "inception_3a/5x5" - bottom: "inception_3a/pool_proj" - top: "inception_3a/output" -} -layer { - name: "inception_3b/1x1" - type: "Convolution" - bottom: "inception_3a/output" - top: "inception_3b/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_1x1" - type: "ReLU" - bottom: "inception_3b/1x1" - top: "inception_3b/1x1" -} -layer { - name: "inception_3b/3x3_reduce" - type: "Convolution" - bottom: "inception_3a/output" - top: "inception_3b/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_3b/3x3_reduce" - top: "inception_3b/3x3_reduce" -} -layer { - name: "inception_3b/3x3" - type: "Convolution" - bottom: "inception_3b/3x3_reduce" - top: "inception_3b/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 192 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_3x3" - type: "ReLU" - bottom: "inception_3b/3x3" - top: "inception_3b/3x3" -} -layer { - name: "inception_3b/5x5_reduce" - type: "Convolution" - bottom: "inception_3a/output" - top: "inception_3b/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_3b/5x5_reduce" - top: "inception_3b/5x5_reduce" -} -layer { - name: "inception_3b/5x5" - type: "Convolution" - bottom: "inception_3b/5x5_reduce" - top: "inception_3b/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 96 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_5x5" - type: "ReLU" - bottom: "inception_3b/5x5" - top: "inception_3b/5x5" -} -layer { - name: "inception_3b/pool" - type: "Pooling" - bottom: "inception_3a/output" - top: "inception_3b/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_3b/pool_proj" - type: "Convolution" - bottom: "inception_3b/pool" - top: "inception_3b/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_3b/relu_pool_proj" - type: "ReLU" - bottom: "inception_3b/pool_proj" - top: "inception_3b/pool_proj" -} -layer { - name: "inception_3b/output" - type: "Concat" - bottom: "inception_3b/1x1" - bottom: "inception_3b/3x3" - bottom: "inception_3b/5x5" - bottom: "inception_3b/pool_proj" - top: "inception_3b/output" -} -layer { - name: "pool3/3x3_s2" - type: "Pooling" - bottom: "inception_3b/output" - top: "pool3/3x3_s2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "inception_4a/1x1" - type: "Convolution" - bottom: "pool3/3x3_s2" - top: "inception_4a/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 192 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_1x1" - type: "ReLU" - bottom: "inception_4a/1x1" - top: "inception_4a/1x1" -} -layer { - name: "inception_4a/3x3_reduce" - type: "Convolution" - bottom: "pool3/3x3_s2" - top: "inception_4a/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 96 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_4a/3x3_reduce" - top: "inception_4a/3x3_reduce" -} -layer { - name: "inception_4a/3x3" - type: "Convolution" - bottom: "inception_4a/3x3_reduce" - top: "inception_4a/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 208 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_3x3" - type: "ReLU" - bottom: "inception_4a/3x3" - top: "inception_4a/3x3" -} -layer { - name: "inception_4a/5x5_reduce" - type: "Convolution" - bottom: "pool3/3x3_s2" - top: "inception_4a/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 16 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_4a/5x5_reduce" - top: "inception_4a/5x5_reduce" -} -layer { - name: "inception_4a/5x5" - type: "Convolution" - bottom: "inception_4a/5x5_reduce" - top: "inception_4a/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 48 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_5x5" - type: "ReLU" - bottom: "inception_4a/5x5" - top: "inception_4a/5x5" -} -layer { - name: "inception_4a/pool" - type: "Pooling" - bottom: "pool3/3x3_s2" - top: "inception_4a/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_4a/pool_proj" - type: "Convolution" - bottom: "inception_4a/pool" - top: "inception_4a/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4a/relu_pool_proj" - type: "ReLU" - bottom: "inception_4a/pool_proj" - top: "inception_4a/pool_proj" -} -layer { - name: "inception_4a/output" - type: "Concat" - bottom: "inception_4a/1x1" - bottom: "inception_4a/3x3" - bottom: "inception_4a/5x5" - bottom: "inception_4a/pool_proj" - top: "inception_4a/output" -} -layer { - name: "inception_4b/1x1" - type: "Convolution" - bottom: "inception_4a/output" - top: "inception_4b/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 160 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_1x1" - type: "ReLU" - bottom: "inception_4b/1x1" - top: "inception_4b/1x1" -} -layer { - name: "inception_4b/3x3_reduce" - type: "Convolution" - bottom: "inception_4a/output" - top: "inception_4b/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 112 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_4b/3x3_reduce" - top: "inception_4b/3x3_reduce" -} -layer { - name: "inception_4b/3x3" - type: "Convolution" - bottom: "inception_4b/3x3_reduce" - top: "inception_4b/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 224 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_3x3" - type: "ReLU" - bottom: "inception_4b/3x3" - top: "inception_4b/3x3" -} -layer { - name: "inception_4b/5x5_reduce" - type: "Convolution" - bottom: "inception_4a/output" - top: "inception_4b/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_4b/5x5_reduce" - top: "inception_4b/5x5_reduce" -} -layer { - name: "inception_4b/5x5" - type: "Convolution" - bottom: "inception_4b/5x5_reduce" - top: "inception_4b/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_5x5" - type: "ReLU" - bottom: "inception_4b/5x5" - top: "inception_4b/5x5" -} -layer { - name: "inception_4b/pool" - type: "Pooling" - bottom: "inception_4a/output" - top: "inception_4b/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_4b/pool_proj" - type: "Convolution" - bottom: "inception_4b/pool" - top: "inception_4b/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4b/relu_pool_proj" - type: "ReLU" - bottom: "inception_4b/pool_proj" - top: "inception_4b/pool_proj" -} -layer { - name: "inception_4b/output" - type: "Concat" - bottom: "inception_4b/1x1" - bottom: "inception_4b/3x3" - bottom: "inception_4b/5x5" - bottom: "inception_4b/pool_proj" - top: "inception_4b/output" -} -layer { - name: "inception_4c/1x1" - type: "Convolution" - bottom: "inception_4b/output" - top: "inception_4c/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_1x1" - type: "ReLU" - bottom: "inception_4c/1x1" - top: "inception_4c/1x1" -} -layer { - name: "inception_4c/3x3_reduce" - type: "Convolution" - bottom: "inception_4b/output" - top: "inception_4c/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_4c/3x3_reduce" - top: "inception_4c/3x3_reduce" -} -layer { - name: "inception_4c/3x3" - type: "Convolution" - bottom: "inception_4c/3x3_reduce" - top: "inception_4c/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_3x3" - type: "ReLU" - bottom: "inception_4c/3x3" - top: "inception_4c/3x3" -} -layer { - name: "inception_4c/5x5_reduce" - type: "Convolution" - bottom: "inception_4b/output" - top: "inception_4c/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_4c/5x5_reduce" - top: "inception_4c/5x5_reduce" -} -layer { - name: "inception_4c/5x5" - type: "Convolution" - bottom: "inception_4c/5x5_reduce" - top: "inception_4c/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_5x5" - type: "ReLU" - bottom: "inception_4c/5x5" - top: "inception_4c/5x5" -} -layer { - name: "inception_4c/pool" - type: "Pooling" - bottom: "inception_4b/output" - top: "inception_4c/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_4c/pool_proj" - type: "Convolution" - bottom: "inception_4c/pool" - top: "inception_4c/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4c/relu_pool_proj" - type: "ReLU" - bottom: "inception_4c/pool_proj" - top: "inception_4c/pool_proj" -} -layer { - name: "inception_4c/output" - type: "Concat" - bottom: "inception_4c/1x1" - bottom: "inception_4c/3x3" - bottom: "inception_4c/5x5" - bottom: "inception_4c/pool_proj" - top: "inception_4c/output" -} -layer { - name: "inception_4d/1x1" - type: "Convolution" - bottom: "inception_4c/output" - top: "inception_4d/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 112 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_1x1" - type: "ReLU" - bottom: "inception_4d/1x1" - top: "inception_4d/1x1" -} -layer { - name: "inception_4d/3x3_reduce" - type: "Convolution" - bottom: "inception_4c/output" - top: "inception_4d/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 144 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_4d/3x3_reduce" - top: "inception_4d/3x3_reduce" -} -layer { - name: "inception_4d/3x3" - type: "Convolution" - bottom: "inception_4d/3x3_reduce" - top: "inception_4d/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 288 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_3x3" - type: "ReLU" - bottom: "inception_4d/3x3" - top: "inception_4d/3x3" -} -layer { - name: "inception_4d/5x5_reduce" - type: "Convolution" - bottom: "inception_4c/output" - top: "inception_4d/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_4d/5x5_reduce" - top: "inception_4d/5x5_reduce" -} -layer { - name: "inception_4d/5x5" - type: "Convolution" - bottom: "inception_4d/5x5_reduce" - top: "inception_4d/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_5x5" - type: "ReLU" - bottom: "inception_4d/5x5" - top: "inception_4d/5x5" -} -layer { - name: "inception_4d/pool" - type: "Pooling" - bottom: "inception_4c/output" - top: "inception_4d/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_4d/pool_proj" - type: "Convolution" - bottom: "inception_4d/pool" - top: "inception_4d/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4d/relu_pool_proj" - type: "ReLU" - bottom: "inception_4d/pool_proj" - top: "inception_4d/pool_proj" -} -layer { - name: "inception_4d/output" - type: "Concat" - bottom: "inception_4d/1x1" - bottom: "inception_4d/3x3" - bottom: "inception_4d/5x5" - bottom: "inception_4d/pool_proj" - top: "inception_4d/output" -} -layer { - name: "inception_4e/1x1" - type: "Convolution" - bottom: "inception_4d/output" - top: "inception_4e/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_1x1" - type: "ReLU" - bottom: "inception_4e/1x1" - top: "inception_4e/1x1" -} -layer { - name: "inception_4e/3x3_reduce" - type: "Convolution" - bottom: "inception_4d/output" - top: "inception_4e/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 160 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_4e/3x3_reduce" - top: "inception_4e/3x3_reduce" -} -layer { - name: "inception_4e/3x3" - type: "Convolution" - bottom: "inception_4e/3x3_reduce" - top: "inception_4e/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 320 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_3x3" - type: "ReLU" - bottom: "inception_4e/3x3" - top: "inception_4e/3x3" -} -layer { - name: "inception_4e/5x5_reduce" - type: "Convolution" - bottom: "inception_4d/output" - top: "inception_4e/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_4e/5x5_reduce" - top: "inception_4e/5x5_reduce" -} -layer { - name: "inception_4e/5x5" - type: "Convolution" - bottom: "inception_4e/5x5_reduce" - top: "inception_4e/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_5x5" - type: "ReLU" - bottom: "inception_4e/5x5" - top: "inception_4e/5x5" -} -layer { - name: "inception_4e/pool" - type: "Pooling" - bottom: "inception_4d/output" - top: "inception_4e/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_4e/pool_proj" - type: "Convolution" - bottom: "inception_4e/pool" - top: "inception_4e/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_4e/relu_pool_proj" - type: "ReLU" - bottom: "inception_4e/pool_proj" - top: "inception_4e/pool_proj" -} -layer { - name: "inception_4e/output" - type: "Concat" - bottom: "inception_4e/1x1" - bottom: "inception_4e/3x3" - bottom: "inception_4e/5x5" - bottom: "inception_4e/pool_proj" - top: "inception_4e/output" -} -layer { - name: "pool4/3x3_s2" - type: "Pooling" - bottom: "inception_4e/output" - top: "pool4/3x3_s2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "inception_5a/1x1" - type: "Convolution" - bottom: "pool4/3x3_s2" - top: "inception_5a/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_1x1" - type: "ReLU" - bottom: "inception_5a/1x1" - top: "inception_5a/1x1" -} -layer { - name: "inception_5a/3x3_reduce" - type: "Convolution" - bottom: "pool4/3x3_s2" - top: "inception_5a/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 160 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_5a/3x3_reduce" - top: "inception_5a/3x3_reduce" -} -layer { - name: "inception_5a/3x3" - type: "Convolution" - bottom: "inception_5a/3x3_reduce" - top: "inception_5a/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 320 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_3x3" - type: "ReLU" - bottom: "inception_5a/3x3" - top: "inception_5a/3x3" -} -layer { - name: "inception_5a/5x5_reduce" - type: "Convolution" - bottom: "pool4/3x3_s2" - top: "inception_5a/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_5a/5x5_reduce" - top: "inception_5a/5x5_reduce" -} -layer { - name: "inception_5a/5x5" - type: "Convolution" - bottom: "inception_5a/5x5_reduce" - top: "inception_5a/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_5x5" - type: "ReLU" - bottom: "inception_5a/5x5" - top: "inception_5a/5x5" -} -layer { - name: "inception_5a/pool" - type: "Pooling" - bottom: "pool4/3x3_s2" - top: "inception_5a/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_5a/pool_proj" - type: "Convolution" - bottom: "inception_5a/pool" - top: "inception_5a/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5a/relu_pool_proj" - type: "ReLU" - bottom: "inception_5a/pool_proj" - top: "inception_5a/pool_proj" -} -layer { - name: "inception_5a/output" - type: "Concat" - bottom: "inception_5a/1x1" - bottom: "inception_5a/3x3" - bottom: "inception_5a/5x5" - bottom: "inception_5a/pool_proj" - top: "inception_5a/output" -} -layer { - name: "inception_5b/1x1" - type: "Convolution" - bottom: "inception_5a/output" - top: "inception_5b/1x1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 384 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_1x1" - type: "ReLU" - bottom: "inception_5b/1x1" - top: "inception_5b/1x1" -} -layer { - name: "inception_5b/3x3_reduce" - type: "Convolution" - bottom: "inception_5a/output" - top: "inception_5b/3x3_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 192 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.09 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_3x3_reduce" - type: "ReLU" - bottom: "inception_5b/3x3_reduce" - top: "inception_5b/3x3_reduce" -} -layer { - name: "inception_5b/3x3" - type: "Convolution" - bottom: "inception_5b/3x3_reduce" - top: "inception_5b/3x3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 384 - pad: 1 - kernel_size: 3 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_3x3" - type: "ReLU" - bottom: "inception_5b/3x3" - top: "inception_5b/3x3" -} -layer { - name: "inception_5b/5x5_reduce" - type: "Convolution" - bottom: "inception_5a/output" - top: "inception_5b/5x5_reduce" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 48 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.2 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_5x5_reduce" - type: "ReLU" - bottom: "inception_5b/5x5_reduce" - top: "inception_5b/5x5_reduce" -} -layer { - name: "inception_5b/5x5" - type: "Convolution" - bottom: "inception_5b/5x5_reduce" - top: "inception_5b/5x5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 2 - kernel_size: 5 - weight_filler { - type: "xavier" - std: 0.03 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_5x5" - type: "ReLU" - bottom: "inception_5b/5x5" - top: "inception_5b/5x5" -} -layer { - name: "inception_5b/pool" - type: "Pooling" - bottom: "inception_5a/output" - top: "inception_5b/pool" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 1 - pad: 1 - } -} -layer { - name: "inception_5b/pool_proj" - type: "Convolution" - bottom: "inception_5b/pool" - top: "inception_5b/pool_proj" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "xavier" - std: 0.1 - } - bias_filler { - type: "constant" - value: 0.2 - } - } -} -layer { - name: "inception_5b/relu_pool_proj" - type: "ReLU" - bottom: "inception_5b/pool_proj" - top: "inception_5b/pool_proj" -} -layer { - name: "inception_5b/output" - type: "Concat" - bottom: "inception_5b/1x1" - bottom: "inception_5b/3x3" - bottom: "inception_5b/5x5" - bottom: "inception_5b/pool_proj" - top: "inception_5b/output" -} -layer { - name: "pool5/7x7_s1" - type: "Pooling" - bottom: "inception_5b/output" - top: "pool5/7x7_s1" - pooling_param { - pool: AVE - kernel_size: 7 - stride: 1 - } -} -layer { - name: "pool5/drop_7x7_s1" - type: "Dropout" - bottom: "pool5/7x7_s1" - top: "pool5/7x7_s1" - dropout_param { - dropout_ratio: 0.4 - } -} -layer { - name: "loss3/classifier" - type: "InnerProduct" - bottom: "pool5/7x7_s1" - top: "loss3/classifier" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - inner_product_param { - num_output: 1000 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob" - type: "Softmax" - bottom: "loss3/classifier" - top: "prob" -} diff --git a/samples/data/dnn/enet-classes.txt b/samples/data/dnn/enet-classes.txt deleted file mode 100644 index 303bf6f4c5..0000000000 --- a/samples/data/dnn/enet-classes.txt +++ /dev/null @@ -1,20 +0,0 @@ -Unlabeled 0 0 0 -Road 128 64 128 -Sidewalk 244 35 232 -Building 70 70 70 -Wall 102 102 156 -Fence 190 153 153 -Pole 153 153 153 -TrafficLight 250 170 30 -TrafficSign 220 220 0 -Vegetation 107 142 35 -Terrain 152 251 152 -Sky 70 130 180 -Person 220 20 60 -Rider 255 0 0 -Car 0 0 142 -Truck 0 0 70 -Bus 0 60 100 -Train 0 80 100 -Motorcycle 0 0 230 -Bicycle 119 11 32 \ No newline at end of file diff --git a/samples/data/dnn/fcn32s-heavy-pascal.prototxt b/samples/data/dnn/fcn32s-heavy-pascal.prototxt deleted file mode 100644 index 7b5a0c55ba..0000000000 --- a/samples/data/dnn/fcn32s-heavy-pascal.prototxt +++ /dev/null @@ -1,502 +0,0 @@ -# -# This prototxt is based on voc-fcn32s/val.prototxt file from -# https://github.com/shelhamer/fcn.berkeleyvision.org, which is distributed under -# Caffe (BSD) license: -# http://caffe.berkeleyvision.org/model_zoo.html#bvlc-model-license -# -name: "voc-fcn32s" -input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 500 -input_dim: 500 -layer { - name: "conv1_1" - type: "Convolution" - bottom: "data" - top: "conv1_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 100 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu1_1" - type: "ReLU" - bottom: "conv1_1" - top: "conv1_1" -} -layer { - name: "conv1_2" - type: "Convolution" - bottom: "conv1_1" - top: "conv1_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu1_2" - type: "ReLU" - bottom: "conv1_2" - top: "conv1_2" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1_2" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv2_1" - type: "Convolution" - bottom: "pool1" - top: "conv2_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu2_1" - type: "ReLU" - bottom: "conv2_1" - top: "conv2_1" -} -layer { - name: "conv2_2" - type: "Convolution" - bottom: "conv2_1" - top: "conv2_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu2_2" - type: "ReLU" - bottom: "conv2_2" - top: "conv2_2" -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2_2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv3_1" - type: "Convolution" - bottom: "pool2" - top: "conv3_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_1" - type: "ReLU" - bottom: "conv3_1" - top: "conv3_1" -} -layer { - name: "conv3_2" - type: "Convolution" - bottom: "conv3_1" - top: "conv3_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_2" - type: "ReLU" - bottom: "conv3_2" - top: "conv3_2" -} -layer { - name: "conv3_3" - type: "Convolution" - bottom: "conv3_2" - top: "conv3_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_3" - type: "ReLU" - bottom: "conv3_3" - top: "conv3_3" -} -layer { - name: "pool3" - type: "Pooling" - bottom: "conv3_3" - top: "pool3" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv4_1" - type: "Convolution" - bottom: "pool3" - top: "conv4_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_1" - type: "ReLU" - bottom: "conv4_1" - top: "conv4_1" -} -layer { - name: "conv4_2" - type: "Convolution" - bottom: "conv4_1" - top: "conv4_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_2" - type: "ReLU" - bottom: "conv4_2" - top: "conv4_2" -} -layer { - name: "conv4_3" - type: "Convolution" - bottom: "conv4_2" - top: "conv4_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_3" - type: "ReLU" - bottom: "conv4_3" - top: "conv4_3" -} -layer { - name: "pool4" - type: "Pooling" - bottom: "conv4_3" - top: "pool4" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv5_1" - type: "Convolution" - bottom: "pool4" - top: "conv5_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_1" - type: "ReLU" - bottom: "conv5_1" - top: "conv5_1" -} -layer { - name: "conv5_2" - type: "Convolution" - bottom: "conv5_1" - top: "conv5_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_2" - type: "ReLU" - bottom: "conv5_2" - top: "conv5_2" -} -layer { - name: "conv5_3" - type: "Convolution" - bottom: "conv5_2" - top: "conv5_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_3" - type: "ReLU" - bottom: "conv5_3" - top: "conv5_3" -} -layer { - name: "pool5" - type: "Pooling" - bottom: "conv5_3" - top: "pool5" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "fc6" - type: "Convolution" - bottom: "pool5" - top: "fc6" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 4096 - pad: 0 - kernel_size: 7 - stride: 1 - } -} -layer { - name: "relu6" - type: "ReLU" - bottom: "fc6" - top: "fc6" -} -layer { - name: "fc7" - type: "Convolution" - bottom: "fc6" - top: "fc7" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 4096 - pad: 0 - kernel_size: 1 - stride: 1 - } -} -layer { - name: "relu7" - type: "ReLU" - bottom: "fc7" - top: "fc7" -} -layer { - name: "score_fr" - type: "Convolution" - bottom: "fc7" - top: "score_fr" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 21 - pad: 0 - kernel_size: 1 - } -} -layer { - name: "upscore" - type: "Deconvolution" - bottom: "score_fr" - top: "upscore" - param { - lr_mult: 0 - } - convolution_param { - num_output: 21 - bias_term: false - kernel_size: 64 - stride: 32 - } -} -layer { - name: "score" - type: "Crop" - bottom: "upscore" - bottom: "data" - top: "score" - crop_param { - axis: 2 - offset: 19 - } -} diff --git a/samples/data/dnn/fcn8s-heavy-pascal.prototxt b/samples/data/dnn/fcn8s-heavy-pascal.prototxt deleted file mode 100644 index 426b40f815..0000000000 --- a/samples/data/dnn/fcn8s-heavy-pascal.prototxt +++ /dev/null @@ -1,612 +0,0 @@ -# -# This prototxt is based on voc-fcn8s/val.prototxt file from -# https://github.com/shelhamer/fcn.berkeleyvision.org, which is distributed under -# Caffe (BSD) license: -# http://caffe.berkeleyvision.org/model_zoo.html#bvlc-model-license -# -name: "voc-fcn8s" -input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 500 -input_dim: 500 -layer { - name: "conv1_1" - type: "Convolution" - bottom: "data" - top: "conv1_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 100 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu1_1" - type: "ReLU" - bottom: "conv1_1" - top: "conv1_1" -} -layer { - name: "conv1_2" - type: "Convolution" - bottom: "conv1_1" - top: "conv1_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu1_2" - type: "ReLU" - bottom: "conv1_2" - top: "conv1_2" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1_2" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv2_1" - type: "Convolution" - bottom: "pool1" - top: "conv2_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu2_1" - type: "ReLU" - bottom: "conv2_1" - top: "conv2_1" -} -layer { - name: "conv2_2" - type: "Convolution" - bottom: "conv2_1" - top: "conv2_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu2_2" - type: "ReLU" - bottom: "conv2_2" - top: "conv2_2" -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2_2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv3_1" - type: "Convolution" - bottom: "pool2" - top: "conv3_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_1" - type: "ReLU" - bottom: "conv3_1" - top: "conv3_1" -} -layer { - name: "conv3_2" - type: "Convolution" - bottom: "conv3_1" - top: "conv3_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_2" - type: "ReLU" - bottom: "conv3_2" - top: "conv3_2" -} -layer { - name: "conv3_3" - type: "Convolution" - bottom: "conv3_2" - top: "conv3_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu3_3" - type: "ReLU" - bottom: "conv3_3" - top: "conv3_3" -} -layer { - name: "pool3" - type: "Pooling" - bottom: "conv3_3" - top: "pool3" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv4_1" - type: "Convolution" - bottom: "pool3" - top: "conv4_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_1" - type: "ReLU" - bottom: "conv4_1" - top: "conv4_1" -} -layer { - name: "conv4_2" - type: "Convolution" - bottom: "conv4_1" - top: "conv4_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_2" - type: "ReLU" - bottom: "conv4_2" - top: "conv4_2" -} -layer { - name: "conv4_3" - type: "Convolution" - bottom: "conv4_2" - top: "conv4_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu4_3" - type: "ReLU" - bottom: "conv4_3" - top: "conv4_3" -} -layer { - name: "pool4" - type: "Pooling" - bottom: "conv4_3" - top: "pool4" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv5_1" - type: "Convolution" - bottom: "pool4" - top: "conv5_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_1" - type: "ReLU" - bottom: "conv5_1" - top: "conv5_1" -} -layer { - name: "conv5_2" - type: "Convolution" - bottom: "conv5_1" - top: "conv5_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_2" - type: "ReLU" - bottom: "conv5_2" - top: "conv5_2" -} -layer { - name: "conv5_3" - type: "Convolution" - bottom: "conv5_2" - top: "conv5_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 1 - } -} -layer { - name: "relu5_3" - type: "ReLU" - bottom: "conv5_3" - top: "conv5_3" -} -layer { - name: "pool5" - type: "Pooling" - bottom: "conv5_3" - top: "pool5" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "fc6" - type: "Convolution" - bottom: "pool5" - top: "fc6" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 4096 - pad: 0 - kernel_size: 7 - stride: 1 - } -} -layer { - name: "relu6" - type: "ReLU" - bottom: "fc6" - top: "fc6" -} -layer { - name: "fc7" - type: "Convolution" - bottom: "fc6" - top: "fc7" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 4096 - pad: 0 - kernel_size: 1 - stride: 1 - } -} -layer { - name: "relu7" - type: "ReLU" - bottom: "fc7" - top: "fc7" -} -layer { - name: "score_fr" - type: "Convolution" - bottom: "fc7" - top: "score_fr" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 21 - pad: 0 - kernel_size: 1 - } -} -layer { - name: "upscore2" - type: "Deconvolution" - bottom: "score_fr" - top: "upscore2" - param { - lr_mult: 0 - } - convolution_param { - num_output: 21 - bias_term: false - kernel_size: 4 - stride: 2 - } -} -layer { - name: "score_pool4" - type: "Convolution" - bottom: "pool4" - top: "score_pool4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 21 - pad: 0 - kernel_size: 1 - } -} -layer { - name: "score_pool4c" - type: "Crop" - bottom: "score_pool4" - bottom: "upscore2" - top: "score_pool4c" - crop_param { - axis: 2 - offset: 5 - } -} -layer { - name: "fuse_pool4" - type: "Eltwise" - bottom: "upscore2" - bottom: "score_pool4c" - top: "fuse_pool4" - eltwise_param { - operation: SUM - } -} -layer { - name: "upscore_pool4" - type: "Deconvolution" - bottom: "fuse_pool4" - top: "upscore_pool4" - param { - lr_mult: 0 - } - convolution_param { - num_output: 21 - bias_term: false - kernel_size: 4 - stride: 2 - } -} -layer { - name: "score_pool3" - type: "Convolution" - bottom: "pool3" - top: "score_pool3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 21 - pad: 0 - kernel_size: 1 - } -} -layer { - name: "score_pool3c" - type: "Crop" - bottom: "score_pool3" - bottom: "upscore_pool4" - top: "score_pool3c" - crop_param { - axis: 2 - offset: 9 - } -} -layer { - name: "fuse_pool3" - type: "Eltwise" - bottom: "upscore_pool4" - bottom: "score_pool3c" - top: "fuse_pool3" - eltwise_param { - operation: SUM - } -} -layer { - name: "upscore8" - type: "Deconvolution" - bottom: "fuse_pool3" - top: "upscore8" - param { - lr_mult: 0 - } - convolution_param { - num_output: 21 - bias_term: false - kernel_size: 16 - stride: 8 - } -} -layer { - name: "score" - type: "Crop" - bottom: "upscore8" - bottom: "data" - top: "score" - crop_param { - axis: 2 - offset: 31 - } -} diff --git a/samples/data/dnn/pascal-classes.txt b/samples/data/dnn/pascal-classes.txt deleted file mode 100644 index a3a62c1911..0000000000 --- a/samples/data/dnn/pascal-classes.txt +++ /dev/null @@ -1,21 +0,0 @@ -background 0 0 0 -aeroplane 128 0 0 -bicycle 0 128 0 -bird 128 128 0 -boat 0 0 128 -bottle 128 0 128 -bus 0 128 128 -car 128 128 128 -cat 64 0 0 -chair 192 0 0 -cow 64 128 0 -diningtable 192 128 0 -dog 64 0 128 -horse 192 0 128 -motorbike 64 128 128 -person 192 128 128 -pottedplant 0 64 0 -sheep 128 64 0 -sofa 0 192 0 -train 128 192 0 -tvmonitor 0 64 128 diff --git a/samples/data/dnn/rgb.jpg b/samples/data/dnn/rgb.jpg deleted file mode 100644 index f78e6e44e1f434031714d8c149475f2347a73256..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 47099 zcmbT6Wl$VZ*XIWvoIoHz7#ISBySux)6N0$bQNO5sVx!!@^as5A!mmUBC29h|^B?=NX0GR*@g#hWL4*>o*Pc)?e z8i4;fkdRSO(a?bym{{2V44Pg6kdaVOkWo?4&`?qTSqJ}n4?rbABYejxiT+yC97yd> z#1)?M9fL-yu9sMA`V_)#;Sqs}^@fC$>@6)FJp&^X4=*3TfS{1HjI5lzf})bPj;@}* zfuWJ5m9>qnoxOvnm$#3vpMOB)$EfI-*iUh(X`j%a3P9}rg$hH@g^B23Us-H_@#i>oxonG#{P4#^F4tJ0qZo!}O?*it*SRKj0fS2) zh!4;DV3N}L_1R$T($4$A#cwT1;8GrW_{OWgo{p6f?ipq47r?T+PNCxdZX!Po1#?9F z_WLQ)*6B^lgEXO#kGzKJpZE|@E-74j*Yv=GsnTxf1>KP_q}Bt>cH_a4+PjN-_3BMfVMSA>$MdBH6lo@o0mHI7SSb zBG3%QphapL&(}KHDEFSg>1>yy>C^yiGU`*t<#9PYG&ToA#Z)aqG`av!RZ)99c#F30 zTPgdF@>JTP5YOM)YK;6?5^@3Bwbg8Y9qa|S^ZJp{kg1vFI1ja*26-0xg=1Kwv4mz7 zU5j{HwsaknmCvCi*}7Jj<#&?{P`h0`i{8(+NU{tePY12=vI6&lbo{J5|AH};CJC$J z6V?@|rso@+qiX5PIPfB5l83tot9-L@S*IL^o6d9yPz7>9>{-gQt~6 zYIa4CiR6|3#?$5JQ}LOCH?bc*mMA>Lf0klCB!q70oOwy0xRvc@jaFerc=)mjdFFF=u`yK%_t0=2$YOcp{hO-GXyiLR_o;C-IBZjjM81H4^Gi3 z`;vCmA9U+_lL83l6^F!Qd&_Sk`aeJ{arFPJ=`>dbn)BycyYt|$ZzU%@xi94Me4>8r z<~MnK0X#WWo;7o|t*&^*z87+=Nm8fHWm z_nd%#QN!2W+1Ai#PVhzfF|0V`Lr5NiH<7X`s8X^c3$~SX#an33$eh9m37pS_UNXbj zXtSs5W(>}|vk_NShmOZWSHNZ0V;jpMOs=#$Ti%NRhfIMzYs5FIe}A>*hHRK&?M737 z_+6kvo_wbo9*6vvDu?n)r?LEPWVJn0U1h$bmmq->KYocw_$BYsa?pq7AnbKI8S%c; z5sb&!=f=R%Z?o|SzRE;l7kUI_athS}8{6zr)vs)I49JU@;Z}q22yvs?q`qv(uN$KQ z*sl^2#V1vD-a#u9%Wf9-}b z`r4FeRevE{!(-$MW-{%v!)oO@QH@4Yn_yI8r?A`9bYGH|6h{!*Bt%|)lcGfT)XWep zQ<`o>kuonMF;$Z#PjX1Z!UclY|G5rZfnlOu1kdKL@89E~cCCX96n};u%V~td7rqCE zucXGC?WI_CL(()G<0Hp)@n&pE`vs^8F0g(m6HK~sU4&<=q-jwPaL2i0$pOmSr5dQU zgt9#akh9Iw%3-)n%WVfgle~CIO|%$o_j7c*t*B=ERC6?T+Oaca-YE+3inwoD!59x; z!ReyqCee#Vb{Uo?7MdKLUn{|$JwGO50&p?}HUZ+3Ra&P%+JDfT&S#XC5*Mo|v^}Ka zk%su!2;SC}Jr(FAsm2}fILgu~{Ho_*Dakm}DI-NG0}5=ng~o`n1|y$f>?u&sg2ryR zn1YTJw7%8Rr$7ORFi%ZJk4Ex5X>=#AC%bAAsoO8b%Efo>{XbXV4>Pwc7qn#Sh4WLC z(aryCHL(grbP<~j391Vlu+C7YjHTFyS-1i2;n|5t8D=^vR%+kCU&W0!1w61G;3>B{ z;YA<5U;=;O-|D1KhQt`sg*#IXrlrbxV2Xq)u{NeRuwL6Tcj9pf+mcSsE%LOqj1*#j zLQV-oqA5~oPk+Y0S7~#(8|Jx-{NlsA*D!|-roIye_=SlV>3CMI$kS)N^-Ri8n2-t~ z9q0A_y16r?C+oEPUS4qjPPcGj5Lww;V8W+c@Obs{x?lyu?wtkKn8UWN!AkbWX%)|4 ze0zPF^W{r@=nwf_qlN>YOa-k#Nva!B@hTgYjC!uDlzb|mG`AnYkA&WQVc1y;!N z75?m5S;&SLkc5kY@~(6!36)=Z&h~NpOFh9f{uD2}GqTe#Y|lQHP-(^zV%vnxGQv?` zU-kz{d`AcMXJ^)G4GLZ64sciP-yXEPCShiIs)oCW-1KfH!0bi#mBOsnM45OH$pw@@ zB4Eg8DT_Y@N{+X2Ic1`hA|r?_DL?1y9yY#}Bn4O&@r2=Fmq+#tmmF{2$ERn!vDSp8 zb9_;l@Ca#=cVk(i3Bwg|I*`WkonU;h^Ce+s|D?u+N&Q(N@g^Cp;jSaiREsv*R)$*M zgG8r~dO1${o1$-X)J!_JZ^Jg4orOG|G{AX!R=5*ro!O^~e1TM5*yQ=h&-n`Yi0O;<$Jtxi zu}{CLE!x{W*&>-Ql;(p7c9gIp^Q2P1IH^thGU1lq`N!YHzXc zZe;!5VNYS~!4U&s!x+GzFJdmfWK@=(Z%Lb)`J`}Y0ug`Bvyb<7;DGah;%yauA1m>? zo?N>`XzZ_)`?pmnSqCL;$i~(u=P85r!l6oEC$TqpN7&=MgvXVpwXQ`=8#vj>#4Ly< zb^^FVbPL-Tvee!jpg_AtX>CAbYb02*>{-k>4lDk>B%C3vNeHc?OZ-SsUF{3Ny7#&9 zP8-ZEH<-n}ki<`MSDEJ5{rkh123zk5{!AW~V1tQC>EBz;>n7e*wy+cHv6162D3&- z?&mGL@^wEafVtK3B-N`cuPYwS0zSJjrsX?Ps& zm(?`ez=WnryrSA#Me_^YxV1`0sx|SCW!g{<4<=e!T8{$V7R!mw&~03K!^L9dTk6E) zLO+E&E~T`D^uu11NGO-kBAC3v!!R08+7@)Cxjw!(zK1kQ@dBVPd)67s$q-0RmE#Cp z#93b-Pq`fY*}>a+%0uGrko{RK=)B{5D!uSc@}QWhKNU<-eH0!PGGQ zOJqM8RWklrttf{dJkMlWcIghPsKq6w(MH);f`^afce!0AEVJVhB6J$C7tY!`;sSw} z5ep?7vri~{ZUCDET95Nenskr}ec875_toV-ii-vWK4>){dz-28{Sdl;V!(NPr6#n4 zGbcG6zH-FbVPa?_9|@d<5)lJtc1K&%n_a}d%H{rnA*cS0I{>Q)^7b&P3aZp_fi zQlXgpg%=R^Gq=ekvNLkRZ61FmA4h+p7ke-$glI6E^Oa3H<^(p{fxW@kH`LA(qpt)| zaS%xppEC#7hg?3!Rk-q4P}RW(S=5+3InPNxTTs*jrzkH0a`lCd5v?c?@1grSnAr*n z?lKJxI9>(c%Tb6Xr81t>#!^g{HR6 znFcF{YOrB)_1%4q4gg;~d#auPYL*vyF- z1vP2Y?Lw?}%)B4lXO_d@f&h^7)#|-==P5wqI8|U+#J60%OrAU)F9v)B&(0?Q=L~%s z%p#QG4hey2heoDSb-qgG-{X;|=jwDG->fQfdE{)L!)h2)3vbIYmD1kVjGm)zEz&Izk^# z|60twxVlmSleBV4^znA{q@szCro?ZlGLG-(*E~iy#5}xU5U=tiP~|hUX7afQhrR%G z1=pe`N5dC=Z>J}z>V*%*W3zMXNwfD>WS{ZH);RBNX}atr&qy@!yS8R+lEA)0%lDDl z7jJCdSNgbI70D&zFq&8j;|b3*jj;q&1gR_hFlsNU)FyB^IBl zlHCr)8*_ikHy7Mg#byhobnIXFOGciBw0cz%zfW|6o3gZBW?=;?z44=)t=9JvD$0p$ zQ~V6SDvRo5xbBgYlRS?tRH;O$iEH97|jwq$>M z0SIwh73|GbeZD7~eRS;gWy)*)MzCnd19RJN6Z?t=HZmVc@)nAYA$U6JvJ#E@3Eoi^ zaG(UIo!mwY!oF6&3gtF^!$yY+*bMFA&Rc-7(XXZ_8u;#nKb8gh^M9Muw_NXA;af{I zntsmYd;uhK7S2Yzd7GQ1IrsJB;6)9tqTwkHRbURm-CPAK^!B{@1<)t~uep14NViWv zs;L)EX~Ly7NIH(kxhHSEeR3ewgix9?2EBkB zgjHhixKq`CzVA-FwL=Qf8u3xir`g5t~K(ni|L5cj%)`fDatUZR%Iuk-Lcrzy!q7QjHxPsp>*Z>_*KF0IU$NwEY3FF;`a2(S_3 zy6>A3nx|a%j?_rXs^6dOTX6c23nQUTg{_+n0ke^|RF;A)?sqSMD0yKLZiXkQ1z$4& z`yAe)wvotTq*j;10g8}(0f@Z-s_b~aE0ECuFDGxY&*1Q0!p*AGkKZf1D=Xt<# zY+JNKb9pOI@FdKkftOWsG=5ezdjNvJ9Vl0HpQyP?RlE5iuP+O7qsF$ZJrV%kOutg` z)vH6X+6wJ!c+bCa;pTsS(Lzj0WSzd?8-{kyw;2 z0Nn(+X}kb9AW*+->IR%lX|dvxo5o!Q?%a8F)GW7Ctshm3bd?jXa(%m zTg@ED6SS)qq%*B?9Iw<{waRXD3}^)`j7Lp=wSAcCO{x1=Wnfy>feH;9C^W8`h4>jN zkg}vwx?h#;G`4UYClYO|E@!4nf-Wa-LemwntUVClAAVISltXZcB8NbffzM5NDtjGF zvS{tK$pa-Q`4JM6YMt5%_U#7SDP;Krr*JZ{HVOwt8rF1%4mW)Y4#8rs*K9OYuh)=P z?`GHZ2`r*8{8FhT$#~hnyq1$mE*W3M5OXbibm**nkSZUHkk=3-BmKA9b4lElVNrx2 zw_tVTcx)|MW4Q#m*A7=b;_#~{M%<<}Vx}?TkoV({-MLKfA8$P;1mV>}bID`mEXe1# z`vV^T+)R?cH)TF=QQ;RK3^ndnLJaV8Ic~hH3(ZI80rlUYo5TG&9xrjHeIJlA#0|q+`ln<2&~6O9>u7QAdy z#9i$uV5!jSSb%dCo9Qg+OTFm*nOaLz#9CfMG!9qcUkEJb{;I5whLILej$;!lAV><{ z(SH1r=Ws`+p(alS6ThGHSY>Cb}>KqEKxjDCE18R>vlm z2e@00j^`}t7vtz?!?3Uk3q(6!5B}hkN<-IwU@aMK2N3a}rme>Lra0&B@vaGHi0G7K zZLQ0ZsUg8dLnq;q;nHj&=LxuNzX2J8ov4lFARH{)_fe9Y7e5|+uBU=EZL@h#;X3>X>5)=khLYUbeLB{6BE zZc2iOQctQHeD(fcTvI+rC@cl}nX%l;wy7Y7I4@o|Y5Im^smVyLrU2i7iDMC)M!e&T zHOGRuci{Irss`X=zdk&)Qn(}i*FB~Q*4y&xH zr72#}gcedpH^#T0c>RnsKG2UjS|~3k3FITPEfWOCcvZep;Jkn)$_0r0~DD z>{2ze3FTatGprZb9}q9{zo_D6RWly$QfY);tE=_-l@ZrV40%xGxTS{KGx`!0=@?4FXaR~@nfCY14%3&v9tr4Ag{)VlrXi`^A& z5f{*mG~6@X^Zb|ikLxMom-~u?DsTGO=?V#aI8> z%JzWd$>?)35`+i(QJ4_?oOodEQ&jmRZXZ|0E9dns$Wbrb*z%g%$@5^53e$#OqGgP$ z4u791u@HZ(YB8(^qU@4MYCB4&L!!6ofp38%2=a_;C^6_Yi;aJCcXmPOAsRYdwTfBr z{>|L)LC6foX#>fc!i4_b@FZ$TljP@5lNmzS^xrBgbbgHVDdD}l{@J(d4|4z47WkSR zhRzfN_Ik^laTE_~xU-gW{#E<4?mqNtAMT_W;Z_cq^qdM6V`^_18yhJHg6@5{lS#Zt z@AAODWRxZM`i5VuV@V+i5t2QKv}d|6e7z{nqJAg`5~Yhi*TNo3iOUqC4l>$oy;I2K z;3{O=6-xNt;Q$8ZUV2=mx#Fdk_`4@YpPV(@P~b=V*xC=>7l`e91PTpXstjER3-2@6RBB*``@H0gT_{4XQ|(N z?%UEg(k`DCEkXFz9;))L)gng5#!nf75EFp%vB2@vzXp(2VC-XC zj(}zk8jWjn;2f`2pw~*y>o&#ZN z8VeDfikAN42~BspgK~vM2)W;RUl(a{6+gidMVL}*TzYoJwF=3dfupWW=p1;W?nxJM zpPcKW=@Be$?k|8vI2kzigOv^bC*pd))tYqSUt9`&K@M5GY$JWPBd=0h^)e@o|-hXBE_OC7L zbYe({1|?TwgoU|^$c2fjcA!KZI1@U?Nk90RDfXcOmxH?+^`Fw$>I?>PbfN?{Q$^4A z^OTk{NU_c8qq2EolYwS<@-F4YZBK@5RT-~#zu@t;=cR`qL)Z7K3_Kb?VC4&}@NO$S z_HCXG--x)`qmOrYu(SaQQ4(&pYTd8)wVS_i#*I~@ru6xi3arQ(m%^*%glG5IQHMdQ zH`9JV0n6KGgAz>vVXhJ&x-1b1KR>~8!(HGopl&s{Tyf`W(=1@tWwVW>fmuodOt+ij zlMMKSCvfw3p;DgleZpif?(qAnC9|X;#6>wFXC(JXT_McQth{SgVEB(8;9|gj(BQaq zE_giN5nCQ>I1KJ``VCq_wh3OS__6htn6teFNAee*e~)+!FYTlIve1Cxo>YfSJ)S{A z0$xbWS-G;n1%X8ir!cCNp+{vf#?|oQBJ6V!37JQ|HKn0P+Rqlw!s($>Vuevo<^%-U z&32WQ!hpfrgaj3Wv}b=1)#tCFzP=q4`Fk?`m?zC<&W+$CvCoR3sZNW6fi&}}QZC|q z&t!=DmeyR0%yMS#%(9kz7E>vpbcd6&fD&<5GBk@j2C$1A&`PJWqW$tdk@#G-(OV zy4fq3Qpx16Yk<&!xGY2UEVz`QTZ+AZLfM2uhyC5gY-PttCZuFST>;=CMWSy8 zGMeox=kqUX0|vzK&uebt|NLbVS+%`aVP_LAi{sa%cW@}@g|naq_UpkB%$xh!L?FfJ zHO}6q^zIiU8QtUalL^1-j$g;*eHsIPT559B3AR_LpgB!HH)F@wJPxf;1%ynxqOMlo zvA|&ujo|T0GYgDaB6*ZZ$^$v5K%6ZA zV4~Y<)za7+r#K^6sE<|Nlb$l_-WuQ$nl3B4{gY5a?3TP4<_1)E=sm;BH>;zQvF97A z)-^u48c=vB`IpBFxqPIdFfH-MdbQV?KC`yk(caW5L^IAhHp2lsKxe-pGdN)r_66W& zA%C&0r2B5y>>+Z)>iHv(`<-})Q+VU+X-qf}>X_0Adq&wEw-rB0|Gt(8Bi*faUrRvL z^1|5ZX8(RmHTX?99R5jwTpfTk<$iDToiBI#k6wj(%Ak7d;etI)=Ia+grJ+qKh-RmoAvMDm0a}mya|8ucJpHHCcVpgO>YuHT#kodSB8_A`)H$Yft?5hX>*N zs<|9C7s=%$@*7EAzi{yTp$>f-jVecblBq<<-#jGfX+*q+$~@ZD_V~NCrS^t>2gIb1 zlO;iCvyoTDtCFdtWvt2Et((up4vezn9i_q+1iKX7o$8r01ysXDGe1}B*bfIsF!pYNLEJTZi2Gft#nD_XlI2nnlR*tl0zAJcv)HV zDdU2!iuks*?PFC-CP*nfPOdK*NCQRsJ9Y2bQM5mMkU4G8L&MIDJO&qj^~$hrKOw0P zYvBw(YQDPqh@}dV;nSEJ+BBrxmga_u<1efqrX~avJ>*v_C@-Omsx~%~kItfe_DLn9 z3ycWS`%G+rLWy@irlxq9uvlzgZ}7W1^P9cK`$1o;6r)j35hOyBQ*eynj^L5crcHe( zyF+`-r(=tccR%SatUjc-Dh9@>Etgu$WS@oU#JL<(A$Qb=Zzt73^@Yv z9F3Idbe|21%jFtDP z?WvhYLY%{J*SY)#HH2_*QUe!=*-0!nN{-59g>C6H;Fts&sjRep}yC+I#AVQ8md zeuJ2Qmzo_{ZDOrxfY%d~I%jjKY9Ufu(hER;roc~+4=XHu7IHROBA#fl`qt8uDenX( z{-j%wDu>D}W+L&D9QIkxoB&pS}z9DL3!53+{HgsZ}gHANQEP?n58RDGxyA+K8(9T)p5YF?j$WH z=A>6fTtjsx!|u$dIB_Jdo9N8%ykqyIJ3of1&Fc+Q2{mH#L@>Oywn+(pr!N8nDy`)5 zv-`+*C6!4{!ze1mqNY?*{h>=@5e>e;c`$u6#QnW%;^l**BGr83z9nqn@OEpLf78k; z;5og||JSjUjzl@wO~TV?AUF5R?nwEbs+X<3dlhSLc`u;vs|5Y*6n?zBr<-kENN^{e%&;q8vC%8Rt^-GKozHz;{ufQ63$683tvl1Ly55I9*pp%0C7$ zf6;#bRDFQoh>Vih0T=jXTcbWxZ12i)lGr#~n9zmt10^uwUx`AH}h(++yF91>i1rC$;0eFMaCH;i%FKF1Ll^ryn3eh^J0JgRMpHW(Bz_e919>t zA`9CBX}Xk&_pHo8Men7*AR9S+8v8i;C{%{0lViGcOIChVy3lbT$sI*uM!lcx@^!~D!6)nzXQMetJ}oz0+?A!u;x?H07WCU9nR!g z)?<$@?<877WidX!0I-p{hsjJga_QiLM#d>(hDu|fmy9HFzsp)D5Pez?Cm#?L5XA@5 ztQKi6I7R53~6H)tA6a`E31dZ5)?yiCBQd z-sK0s0JyyeT@+scG&9*bEHf1Fp(E`dPRmlmh+z(oF`P(JdYD)%v7^%Y39rHNE>w{Z zg+T(Hiv)!#@EBes=4gPha@#{9lky2u0D>o)AHkc)%7$}Aj;s|f9Vykudr%RxO<3V+ zH^UQuk}-lDVOJ7hEbOlDv%Nd-UL z*pGYj0xX&4_FFf$e^qXE)R% z?Yy)3y@rfV91DPvQ$%@o>hjIL_qhYsyXcE7YR`60r|yV!3nPEn#rg*2hCRd>O*hQa zywUjim_~4coY=;KArg!vqPN$*`S6u4V`0SxChYgPphBN*u|B+n$YCz|b9G<)XQAL%hZt*Fq&ctAoX7)gubU#M z!S^^V6lMnDSkY$-`R?Mtz0!46W~rT_KA9H)pFfym^WKZ*qGWQY5lD-gsLh2rR&?5i z@u?-1dk+5eccvuOZnN!zpMji1$IOxNWX~(w2A$e+$j&p83Xzs9$2ogzl>&J^G!5jZwTsfnRPa`3JgUvA#*rH=@MH#+t`=B> znRjwH1mSgvk|t-aey6&QWAoZ{`rIeHlaTyc5k`nht#U9`OCyQ9;jq_WtVlntVZ9%i zk!-)Ws^V~*@4g3{e2KQLxfR9-W#o5}H7nn$8?qLpgoD*a%DI1?|ML7bFyGgyi=cz! z5+8L#Vjp|msp2F*{^Aaw1R-4_=ueABJt+Ob<&a1hbYCma`|MFgh_6=lNR-cD+G0-1 zC0(UF1_$yix~ZWQ^5DM!^eMQaHB*(l0mMJSzp8o4r_q zcYNr?q(4Ku6{j3d(F@Kbu5fhIJkbe5S|+7>>=J*HKP&RF$K3FAfvhZ}HG=?{6Tg{$ zLV>_p^!iGd7Bc~pq|a#LWmO8J3Pa^>Hh2Cta-W~3c6~)(_uk9s1xU^r(LSh7jr^Ey zvv?++EcVz)G;Hovl^(a7kQ(aYFC)`_Chj-!i`Q#}sfv-^7*+~nxToSvHePR13RzUy z-SD^jBt8{5Qr@oE;yaOtHzIWg5PF2>dQE2sVpW1nOJrRORt*n)J&ld~($wj{xJ{SP zwkN=z6bHMETjr{cY9P=9oy4H7Lm1p0g!j0}_nRxlg0r`962VW;RYoQ{OyN-IGpIdV z?edp6zeARvv-!k>i!c0Xy{NUy>S;lS(8FXEMBn#-t(-(`a-hf0Oj~ti>NFj-@>4;Z zrHc!N;ltO%MdBextz`#J`M{DtsXdWSgstAMfU98z4oIz0>p73Vb0;Ow-cCF^gr2c? zz!(xUTxf7)0{Lxo+>ofe;Z!!APk4na^|Pz;6rr+G~9dQtwIb~shPNvcg!d%F?%ir2PFa$MF!ZP`wU!@JkrVF z_7tJ-4EgS@vw4et6l|?)T7FT_41DN^*Nns!dIYuzy11cv-|%aa2l}xzE=CCadNqN( zm1IJw%=hn1sq)l|sIQw=rx?YGQ{L92>THOr<|0tQDk!4>P@oZ`ww%-lUuv&;%6bM$ zkL`Wxp>8~n;0Fl0e!WY&m`RU?y^nrhKqDZyg99%z3KOHXFMz(Wez)i@Dk>Ln2K|vv z8o?T&@!glf!XNeeKwUh_er~spoNJi+_=_|w2ikT&Hz47>BAM|kaRiC92jyPdBC61- z)2ga zFLv*$K}oBsBquiVAQ^%vU__Oo3)O=TXJt2M$_Eq(%tP`%Ny$NI$sXfi6t^>QF{n}i za+da3uKXv#{w-^tKbHFNjl96&-(L7a)%sU6K-@YF!|Hr*?_h~qPIhU=?0UPbQz8uq zR%s;dr+=~zQAeZNL~&ow;v!z`qOy{$Cp=Y)XN*y%!8erl4IQrFb`zTvO9x)}QKADnmRr$OAMqMAx zaLeqn>qg}~zC)MrHNnsO*V(g?Dlq2P{ycvSzKJQIHjky;Qsx9MTvcZ$o3dn@O4B57 zVnmun|M3tQDwI`rY^0-2dH3@eh!%DQuc4_i`s0zBSm-`cX{o&;#V6>`N@whk+tka^ zJ+|M0a{gCyWqCltHkmur4KKZmB<6&@$v<^oZrEd5rfS%wNXJ)f&AT#JY2{FU4z_SY zR#l|(CeAL7|LXCeijkG9(a~I|?Jh+2YoPM6(zY-;gQ+286JnWhBer35oPVp%qE|tC zT?g_c7;}@c!h8WBLr`w9#%?YD#e`%g=U7b*&e!A_U5+}RV==TR7wU55)uv@fGYRPV zecDaVa2D}SL4bTGUUgx^H!fOD?+!nMWV16X$`Mw0| zk{I=z%bvBl6`7S&v3Z3Q4)W=@#%}Y*(A$=3?=M7)4=w^J0$kMgv@ppKW&7{FtVzg+ zLAsXYE7vv@TX8;#@b-jSiAt+~ZR+jDPLYqMm}7OsQDi;%3AKN?guD;s>Ttv)VMKLJ z=fs-dTl6W-MW1~v^V}DwurlSdhv#$ODV%UN{ctIuaG$NI`p|N%8B|KIr2X~taqyGs zq2lAi)C>Wn0Lxe47l3PB;sro8_mwtcc-J+Zc4F=9M!jYZTxDDM2^ans>xV@HQnDcY z+cC^jOVO97mN$QQeoZc1je|QU@Lv*luTKOr+H=*ShTAM#Y?0Za9e^@Bv^*XU8WB#6O@W5nVAW(ph@6vrb*P6JRQA5liws!%*EDJyA3xN| ze&u}SgE6H2QqR4B1k5BO_ivxuIU`W2+$;{+oywGLI(!{o0O8a4ACvtMGVQ04ehUaW zKd~_*Z2eZ9i$eMcnvf7J56o*0Nij?u%xa$FGh(RaIRvZe#tT2UtV3P9NVaY7A+s zwTbhjqN8%EBy3(zs{KgcgT2#*Z{8Ek3JVO=ItuP&D;u-qN z3QvlPuhpTLbO93YWTzjsG_0yqGhFbWpzaz(L(-t?C0pc~ zcmYqp(`C&stvbdS+fczGNfD77=OrxhtTU^PVFTpmBB?}_>lbR%7O7@i5M9+?avC22 z`WBUx$@4PVZ;kV|(Ryj*CL}?~n3FsH_lVQ{ePOHx`inQ)=N@qr6-?!qgQAM0@VIQ7 z6jzxDNf2kVZwH&NH$BLOv|ruYs!CXCVI~+#?kPO9V^&{EQ;X%76HC%Uql30eJkzX!c>Uo$amM}3eRSZ{3R z{wER9?kT+r*0_z^doTAPb$eSc_hLrmX=n-uP+l&0pllVlN2izVT#haH2MBrAZLlY# z%jM>lA>`2?ZAC?dw=AaQ{Zr!kY+DZZPb17w;Ivm#${@|7R>lDgPtCG zS3N>`%s26N(etK>pQo(z&|t^I*bF=OSJKx~vTH$bqN_Bu;%g8U>Unt=i7Qf|*(S26 z!XsxVGuP@Ub567C1%nbN+{%^-oz>6bMk8fpUR1tY{Ii=B>*QU;7roSd+CFhbdI1%u;*H+ zq%Pa26PL^v58q6hjoAb}AC)F@ZeLY2Yb2fY6_4esog3iUUOTJc%{X$|no4|RhmSv^ z$nE+f`7BE&3-=~w-ExCQB@DN;;XssmUGqHYpAIdmvfET%TBNOMMEB#jSi z^z)rC!`yRMgVj8fnTOfEtMq0yTBAkvHMEI-YjCV9VISQ|_IgeLYbemXr#p3jHWe|$ zdMVrRzB^Zb_5b^L%*W2BfR=xk&~>+bp697kRn0pt_ggr9f%S;xW4?I(&xvvFP{Ph{ z5eTGcjMYW#0r^Y^sq4|cma)_##In?yGV3&bPXJ#2!56#Mg~$IcPyJnPF9HNVw;x5>M-%Fq zk_`_qPkN7%!CT%YWYp)Ib_}3qZ6$A>3#C1A8)p+wQGzlI=EDr}wpvri)(|DdOc4&I z-d9ZxWwP-%eL1^R&F~(Ppu!s0&vQT3U83d^oJ!`i16QHA~{z7+Y@C5EXYr{JRZ_~%TK z6lWz_GGFDyoY+G9p1my%qVhkdTaTukmohg%F(=ioB&%HBN|Gn`htW19ehc%2!eOn| z*00IvauZ3%TKH0r6lnub(gvwKm*(I1XVsrj)lH7hCInE<7umcSRklxnvxTU< zqe>S5E_q*XmzzgM#D(Xc6H=@;TvD_@>lPG|7LHr1$}#Elu&TklF2!w-Y9GQ~Jkyc_ z#=`h>L8qzpQbGNkWr)22bY>&Qs^d**h6`I7wvRc`0JF;j#wnwI2P{FTMvTvGJb>Tp zdjDC~X>&zuR@&8eEPv#Dl68$%N0?9%5K2;ua!S0m|JKWtY$juSOWNTzo0aR`UdgRo zXs-6D8Wg55cVbCS%~Bf;_B-VdA^Jf(C(~Db#9n&82-@4ZT01Nv|KU$|bKlo62!Sq{ z#uc7BqEn}dq>geBI&{eoI z*x5u$nWR~a)X8Hj);(Nae$BG8+)s{nK9yXlb1vB1b2U_61#$J;-H{}J^}`&u5f2G+ zed6$jn!3llg;T_Re^L~9tE0>y6fFfLzp+XPJn5D$? z<67m9NGgIs`c|4Et@Ix=vnJ}Gw;v~C^jd8N2E;UU!aRh*6HQhvseXlYa&}o}giD)H zw*b77q!5SF=9|b@cY=i73qZ3RuJVA zt~?_-{siDW4@2Rhb;k=X2u#ZXhX{PTlK z`zypn;Z;U{M)fY4mEFX`yB16G*KQzC2xYp(Zmu~haK&u|{$m#hAl6EgaC2G4c1-i2 z+Av7KHl9JX`>FTm*(#OKy;cEBwrWjYpZYtUPio}VNc0b_r2CH$tYyQ(sXH0Iqm0zT z9Yu&odXUHKWI@r39MZ8V#yXv)P!8z~mjOZId0A95GHLM6`y|G}@JiG1yAH;A0{D2kd%Pgv}&b*hRHKU25=j?Zr>(PIig*S>k zUeXo*+iZi>#Hus(+gX>ozltrb_erTu*$XE!@IOneHYN-#n72ILxHJbKCy_)m=<}9vZfv zZ#IW}Yxd7AgXzp#Ih2w{`16Hhv>pM-2f403-R@_W7~W=8uQyIA|}+&%d3SgI`; zNf@&}VlZhP+j{}V6m|pw_q_r2uRTepc4dpgPYYGkpY)9Tt54r0ub#3 zK+ke>gT+l_Zm~~p$s)Qd#Tb3~0hs5o>5@1;{*^5Dw%3xi!?`Zfg?Z5KVC^s&U=9ZX z$RmzLbZ|mG(G|Q_k;e>5uFES2e4V6~Byo+a__3!cB@>=8DM18(WHCbJv?S)tgU_Nv4)okixMdrXoW97jrRhQ;g#rn^z)7n+*Q| zyKY!F@wf1?$6S2h_ti;%sM~434|Pc1Br7vXg=Rd+R4Ona@W70L(13j_N>y((Z5nC4 zP7(|I?+WU33k^d4&L+5ajjd%>X(7kTO9dgb^7{@>Ijs#ZU-9pW?%K}ZQTueu3pq*t z&Q8t)V931n91mKpd8NY=>1%lJG>vJw_PGH$04_UV6ZOq$=>8qEk<#|dUK&;GGJfrz z;D#J^1TIGej=r_ltHHfanC10qQueX6j$>9DhMB5MYpiIRdt9Ie;d^w9sUsu(OD|$7 zTWf6xShT)rwCB>~wuCZIXCumt*~iTsg_wdf$Zij{Ue!D?Bsc88+K}DLCS{Uk{oo{? zOEz2@mv`E2FOJF9TVzCR8J)83sUrpH)t8zX>ofz*tF z@6Xbub(KOivO5J`%+e4@KI9HhYOZ1q7Z^QD4@30&3iBr0lR4h2L1F>HP~R@>WFDvd z@ls=SYCNVnUO+h`w@Q)=XWC3pyE1KM zV7oyr#~kO1(}9VQk+&TWI63;CTACXvVTk#yBS=WbNI4$HtgIPxfI&QPKgq7TF?YBz z5sDH|%k}6hjn(`;rHHi)x3yy+X(ciV&mYB{AFppwU9Xk9qV(u<&0%WEAKEVrjEG={ zIbv330!J(d&9vlTDLj9(T@5#D6$ce}BZ=`9nnkQ!o1&4tk?v)O1#$r#3~`3f9-mr^ zN+wH-b%==f1YDlo2l&>Wqi=Jm=*y~I$0V2c7xLI;-Om0I!B8-M^J51*u=J?BJZ(;; zYv-_zPcvgAj1-m`9Qt$GwZ+WK;u#R7{&JkwW!HqWnalp>u zUZBybB~A-u^0mikeiDaKwYI;yyt*n=VYZKG+EtFn1hJhgO8Vi{OG8+Ap6mypHc-CRvw3wYa-_1;V_coY4DIA&xHS#BA-@l|%VuRU_Y0ilx2_L8di^U-(bU@@3pxo%d=LlF zwg+72^A*9(?%9(IG}9~2n28mKm;pQ8@(3l1eBX)9RgN}UBvAhVGG&erI0N6ePs+4p zkX%9Ksrg)C*e>F5PCAc4$K_XiG2zK|-Bs1hkXgqb=aCpO9E<_ZGDdl)qia)HLz>Y> z1Z)XcW=CFd#Ws%JJ%9SurJ-pz8rHHc<97QxkKW123Zny$!+~8Mui#BTPt&85OVnjR z&9ujK+x*Uc_W*7Bfz4?XY2kepqlR^wclo55Pv1V?f};Ga(wzyZB`t|{ol@4q*jvI? zBaC1Uz*n8#-M#CX2xW&jQ;g@{qP@MFQ=URrMp)N>mIqZFpU525e=Xd^9HCc>cu)b{ z0o%u~)4fVsYE^7Sz1R}kJ1H^zq_b^O(eCfY-gxGw#FlsMY|(GjQ zpOBhpnYQ_*lK`(It7oqq;g99slIcvkh`3?_+KKYH%O5NOkDwX+>Rae=>SJF@KI#y} zmK%ovlYx$UU;$UOohIhuSe`i-4i_kqmw&!j0ArF0l5lzoo-H@+w@^bADm>=b-2Gt&@t0wF(^W?B(XOhRZKJQZe1R zQc_qU$5WrYPrn}3hpB2Z+{yE{alim{uESH4cJ!2PL)04OV9{i~@dP%Isc2a5W<22m z=BD>&)?qOeX~UK=vpeq!S{*;baPJ#rkZ*q6oc{ny&epHWi5qqi22MMlO6YH7TWc_b zg##a@d7p?ib3N&?g|H*TIVbOXQcCXUof!Li)_qQGt^DbpS6sWXVo$w1T7Ps3zix{r ze>$_|fzW=G+h@|kjAFD#dRshrZKJ1E&mXN)xFLzp-X7Jha7bblk;P+7%x(bATd@PJ zI~ZbQqZVkaNgK%b5wK8qgmuAS2s?6pL2iDXYFQD@jIwXSLng#hbB18Y867dS6OUfp z(*Ot>U<*MBY$;*`I|Q06nUEb&@N4sRWEy_mR0tI;w)i1B`RiARpF}7b%gkUhN!6Sz~WGjA{Xo zZaNYCT!1siPd#eRtqR)Rv3OO?OsZpqWJVMMLHA!b89jEM#;;AMvd(HYH|J zlgnoZ+a0~C-+*SYH}?kq#U)u|D;4iKw>{1?ji;gS>0F)mq}OvSV=p6NRaZGDu<2Wx z9-VijTLimRS7OWpxM9fejx+qL(&9XKB?nHM+>ECVl$`1>L#x*`OWSKDxrQi~2b;?~ zOEJOd!#%TDQFvzR@>gRYn5!E*;YrR11M|lf+?%;Cw4-Ua%z{|*hq^aW_+)hUs!{6l zMPcMvE-t1Zw8U``9f0oH&poT+@cEQ^Ipn?N(DtOID71N|q2b$|9UyzT?F8z->0z$%l1 zN{$A4?w+9j1XFF|`$fo-^_iV* z(lr}4um^7CWAu@^{6B`9=7{AEHk@RW=sW%=pslIGHPCBpMp!G5r#Mznah!UA{{ZT) zE+E22m2YVUquA<=hu$Py;tYHi5?g9Q4Vl9z;G+i~$vrxW;%Ng&|zAM9Q%v1UIhJUpVC1 zrHa=p2a@sr?8Yt(oBs1{4BZEE4+Q71^r*hjA(%+&vkl?Ou|GK{l5l#EN#ovw1YuEB zrXMT`A9pNU=~YDJsXz|V$oYo{jGy!Q)V9gJhskvqxqp;^4&?-Aj==t(l_s$r)&7fj zV`hjNbW{?!7+tNq0H3-}+#jdbq5CnM!scHvhF#l-)ExaXqaO8o+jLg`P-0oK%%?a# zdBGXR3H<1UoL$jTZJf3J&CSiVrPbAxw&o~Jv-zX$vXFD^4u3FfIy>XQ5~drV3@f-? zms}jH=XTZ3cpZNLD>m*(kiYy&;E@$F&opSO(||rwN$7tbwY*xJwU1W|noAE&Q?`dc z_PI7RXx}4!?02k5q91B`K@nR^I04-7K@ zCyL}miWds#7D7lTq068HIXSJnKQZ+5D8*HX&!#(P+mTi8bh}j6ZzqBkTS=76u_EnQ zrAI0@0ga<+>GjV_==7^d(@KCzbLE*Akf3B7=RA&kaZz2TqBx-`Mm))sLded+;X}^` zPUnpO0H1SKlS|$bLRuur#^d)-e!Wd;F`6mkF*H*g<8!v$e=pOY!mg#Ha|Bmuh`}ux z!vJ|4wmJ5$cvD7NUqd?92;)Wx71I{Z8Dj1`a4SwLNJ}iQ7+`Wplwjxc{{TGGpz`gY z%q!(%1Rhs8J;6L2)1;N8EJ7YR1n_-vT`;C1J;BKUU;)=SC%<}CkAsdB^#e44f{=OJ z)Gm3&4JVbfxBzD)9-pOdv=a;x*aVMq4a~h_?Wz!5bg}wP;3Om@Xo?v9(w8E#uW~q)3i7fg+Z2GwKk2K-NRW zYi+IEne8qwO3GNW4kZWI0OP6Xde-Kl5Vo5{NM11Jdq)M-?Ii;)NC!Cj9)qoGX_8!} z!In9qi3E-$L{`J+oyu5~jH+Wi0H>)nwN>uN(8@i1O#c8C>bD@-TgHWAky2?vLP{~@ zDLBB#x2W~fen8c~HB5#vC@(1+$|?DWY<>Xs{A(=>#R?-_9fmxrfN_zY=ke*9SneY- z$Yf%yakZC_yz`axu91)JBam9Ip$RdV#OUBaqr+JH%Be4W=&#~Gdj=#gJvqt$0A{j| zr=@sry)QcDy3 z+(8|3Gmvr4IId<`zRw&pTdraeGP5zYU;r0@du_uzq_VcxU#i|A}-4kF&cHjRz9X~4$p^N&uPVz;d)@VK(E zzdj+BOMq2{g^inri~)_M-Gbx{E^*tVRhhg?3r6cup4qPEOItG>vs||Lq5HuO z0=J<(&$W4M-x0Jq=b6REuWa(6jjnCgXSvvO@`+_{-H&L|N4cg+;#+vM_!~^rZ#4)d z2V%^EIc?cU#{3;YyxRdxQMz&UJ4S-)i<#T3j2JdBFSUATajsI29*{n^LfzOPJu>=VgmC za7gRZp0$HfOIB{`+3I!vIoH!hw!u5DA1e|8(BtnK@r!i1xzxXMl1($@uu+nGlU?=4 zhfcAmJ@&DF^0q?kg*G4!KdiNF5NSjK6Yad6l@^xwB!P^_MjGn;p`c}2`n9UCmCodR z^CB20NRT{{Y8%>%s3*!ET9b zBP6k%=ov>OgOKMwjnnh17NgF&5vfAJY&jkH=RfDYJy_8UHv5V?0UK^ANj(lh9eqx~ zd*>A^Y0_NJ94cMd%#k2)a?Az}Jvj&Pt1+M6NpC8g2hS&hPXO`TI6sATULCYpW0zHw zDBcRf0Q;bDPqsi5)~2wJGbXjSw(zx+NEIJZl~pB~M^K{ySJ6NNr(9;RJWDiMo|_%5 z(g~%RvY?!7{{U;$VxiZyrJnG|9$16Wcd8I0{h&0zG_nObJOI70eLupsRXv`ob6Som za+z9JhpjY;Vv}Or&L4D_3^^bWc7f0gW9#0n%CTHGkbsF(CnS24{s;Vuo6Zrec?vd- ztZ~lVjGTQKWY%@X;aT2PO~qHJ0Gw?kW4SyL(z)u?omY9g+~jYSu2WaSQtEQ+&}qMC zM76ly!gq1yfCg6wu1M{H%~X~GQR+ zPLnK5P_)~DU^vJlk(^hX*?5uKkZYNkH2e zEzd#6dilHiM$+EqM-GGuSfNA4H)LQQarG6|QxRUYZ8xMl<+*ezk6Cm2Mj-0}jNV-v0pC+OrCuu}15ck>XiA#006w0|%%W_3KR+ zcMj-@97PD(l5l$S*QZXk=GxjGRHGE#nJw%JN^W-r5l{s<&H?_U)k$rVakRy`@tlFt zhX>P+znw`Pt--n7kgkdj0O~P=licy2&a4!j#ycMeaNoPBWEuSwH%W8sg7?`vtO-$!{QrGfJ{ z`HuKrA^DdW0Q1SgITglu$_JJkSkU3rM;fslWRh}!@0|Vyrye3myc=z(0`ELf%p-|@ zYy$C&%6lMXA5zLIzA5siDxVXv4dte%s7(YqT=wub*Lh623;Z}y@|@$5k;gT2!Lw=? z7uvm@)zZ!ONn=t_0*u&Y%8Yc{m_E48WL-6;rkZiqbx{FJ2<{R!3cH$DIppU!2&WkK z>CJ3-F2>s4=Tf_}dzox50tlv^6-3eoV#P6xft+U^xUPO(ENS*p)(cdPQFaZAq$n8} z?~Z7iQi?u9yF97S$}`r3ILkCjz&8bf-JE|TRn!ymp>8-kve?HTk6+fRF|jNpb$1T? zc8|HoIQIVl3VJMIi9`zd=P$@T_{SY-6;?d$+HuA~!9U~E^rV(Ya^Elk2i?cbpFxhF zkFRR7M>}JUzs1KN{eN1EcvURG@5dzcKY^gkc?X6kJDI}+&rUg}B&`%+@5VF8$m^5U zi9hFxU@2}lZu*i(;nV3{R<(NY_?AYR(mBL-r5Y(2lrrO%`4|#;$RB~Ou4$blNq2yQ zEWoL3^Zc{@dsiWIY+J@UbaTwoM{&4-M$(J(9jB5H_DM{unmoyiZQR=kLC$>g;;Pak`x4l%r zIr6i$LkBED$EG{UvVfhFOAtDW@(JCA!BKM}8m_xJw*8k&1)XNd%0 zkeOU={sGsF^{Y|Dt%!>a*xUv=IOFj8)H&IkSzhd#(m5k&h}solZOYv3z|MO5^&XvS zw~{Vyr&+Ra>&W1Y_0P6J{HpQT@`w3$XFQA#^TjrKQ_cBKOC7n-Jr5jxdV307+KrN1 z#8qX1fl{(7F)REdka3=epRVs}W$bawX&TI`@=Boo@i!<@$!veRIHj51IYjUrKJBcm ze(>}j{-0W_HOyuht#StMM1$+%-AdeU9TbBWe8dF%{#_UwMoC(HFx*Nfgbz5GFiNoQt?Fx|vexdjx3{{q)1kgFAu>lSd)GT~4&3rEb5-=)*3{y- z)Fjibt!JL#nLL;_DL%uuQ~B3hsd_r@E=ezWtp(Ijc}mbkk(E|0w;c16k@(j(Zwp99 z6?y0712xvmHpx+flD=M4x%<6(4_*kadt1`HnGMuHnB3*D{3rY~T=eIynRMz|*q#Xk zDszTjK^?1iPq;<4-Q}yx8*U-jAdY&D;<$)z-Vy_I+cmLedfsw{Bj>`J(ong*OHLzj~|6qEpslR z3Vp2-Fraw?HQYe!mM0^hYUfJ&Gm;l_NM@U)-de`j6UDihmSU~Dlk}|ZQe3!?pd6lS zxs3TrI&oaiu+eG~7?}ZM(buT_Yjk}Sa~Bx$$!K#rZMeSGE#W8SXB&^89c#I_nm;B$ zPR}#s+uySR`Gw7Mx-EgWmUAG?z?DV^_(n7N=DG-pxRFtcl~M^)#uo?w0Iyk8ca@JD z8(CGUb+RVAk|b#(87u<7-QyhO{{Sz_m9Z>xw8%(@8x$OE&PmU)$L2*uTmr%}+av@M ze(~fH&!%XO=0pt00!a#e=TO9xjt8bmJfH6IjMQjyX>t^}g41j>PO2nena|$I~oB^I*>DvJBq~C^ySoHDyR3+0@2ByGxhX1{b~Lk*CaM#IW0q8 zK;Z*OHvmVd?}~0Kmzf=yOe4h0l{+)E)3iA*rxzNH)E1WE^C#LQ2=WYM?FXJo1CTq^ zo+Z}|$sLB7ByOaCtc-$+qk?ie*EQl_7TFVL1d@xneBqmT?L-Rfc-mYZI`dM)HO2O- z_V$`g3mOmIN2jLoA;)Y1oM)4abJnVyDkxq{U%b{-;Yuw*-d~XuT0v^1US{(c{{XB5 zo(cZ|4wWJEB9TieU8=Giu*T9iXRo7Y@u%NgTv-N=>IvaxB>8e7Qdh4+K9y2*+RYSY zRTaFTpTtNRZhsHTrdPhF3)S3;avlMh0b(<>5(y;agoLg?Phbhi>mGqza9#eWVxI#*fa8^?+`tpEyUK4ajM@~Oh( z+d2HJkB%8GCs?B!osIynOAQM`^6Cvc*mw4}qH+G0E(fWvpuP{;%Ljsz^5L1Jyp`t5 z9G#=(ANmQeHSp)c@9kTeA63`%-8#}HRo%aPI}w}!cH@rby}I6AFGAAb(k*V{b%xbM zMNUH?;~@7u8daPXqZ6+U3AcStZ^t(aHk&n}c2jg3JdUAyH(zS;%fAfS#gPln&71|u zBa z2+8bv^&`!KLr!9!ZwANP1r`!~j+d?7_?~G$T`NuUP!z|LPa#ca#NVxUJ54k5b zctc@3G_aPIMg+M#w(jSmjPa35ZH7GPZQ)yjLjvshAdW`T*e9kq6~`s-Hje@KAuDLX z(Qc!KSp1vu!N*QLbB?swmIwmJykKPSDV{rjoYE|Mn~x~8PsR$U0|x{Tp!!rY>35q9 zr5kYhNJ2Lp`g70a$E{76u3no^4AF@oEHm?%fFuq^dW`4mRldzMtR6;`5x9VHz&JlP zI-h#GXQBT9q$}K}#TVCC@v$m)~DZD(;G z+JZ_D<_*APZ;`TqdxQ~X&!mEk+(Q2;^|0=$xUsNnP! zTEf!u!&2S$H`FeQJn{J#pJyo%3K((-$l8Esspv79>ZeUcIumbr$nj*LGa)#BToJ}F zjs`#AM!CBdx3`M+#!vN5Jf7kO(ng;$Zk^70NZf*RRvg# ze9OIh41C|6l+fhx{{V`#M2hV$E-p;3``8DTj*IEL=3E^1u63}pidT&1bH&Qw3X!EuxRaMx>7|#du=xPR?j#XEA z9WtFbCy&#g@u=dsmw9WIiV_t5?kTr1{{X&#?oSkkOwym5EruoEhvY!fk%HTLj2z?t0IlmtrxF0FrIdnK1RNeS^!if)(zgedW4i$x zujy6X?O^*uHaaYO&$#yJIHwr|m{RjQD&bK^PCq^~^yjr3QzW`^oMR*m`+i-yrXss2 zEHZ^xdB`IyG0^8frYo1%*lGHPld4EO$nEZcm>|Ybka75c!>>86ugDUsAD4hm;0AH+ z)KoL+wzt;OM{}1)`CS#h`1R?>>qfvqd3gqK{SnEK-s!D$MR zo?qd&^)+J0NrKiThSoeNCkhWE>yeXJ*3>9K%)qZ8A>EI}e_EFAG*ph-G;OS)k-%NK z9G|URh5|x~m61mPkbC=frwAqme6UYo`%fmPg62h!5=_i?o;Lnm=BDE(hTTMEg04r+ zfu2rjvxUdqU954I8Nd|bbqV=FDsjq{>BnzMgU(jKl4cA~Bq#p>Ua1I`Z4BxfH5lp7 zAImgVcQHuCy7vD7RsR6k(N4qA;4NYzBI=wnlo;ndGCj_5Qd=k?_hhaB!d0sK!gGy8*ktdBzP+v+{5;j-XWCGLycnpK~fQzDY)0sm~oTQQpi_By6&n z+#DZIr{_r}%B$i=DhcQ~&T+x2canLQ?7O+(;~g>o0M?|XsGQ9+b%>A1033n&oaU>{ zVV=$@ofLw&>&WenIs7WSmUty%ND4tEdjr$@(vns(ioYpbWR7x4feJn;Ko*P^_|A+&nLUrKP~ZUFJJ=l&76ELg@O za0}ru(gDZ}f4V&e1yR%5=KlasmPA#NEs%+n9#Kv({{WcB;%lMLm*G9ykgY=t8BQtN z@+sRJ@EIYpm<1p(6Jr_1I-dCy=;hQ{Hgv->Fu@W_oR7hUU5;m1CT#6=MO=b8$31?d z=}PiDAZYM}eKB%&JT5uehTSduy*c^GpI1B(k%H9+>KLS3kG(q~$cr zLFj(Yzu{9UNfHM*!pVl++o0rqbNN>-ug7txT!^8FZGV>_WQOhl^`TK!rLX2Ej-0h= zeaA?;+0`0rYg-s3WKGg4#-N4o&IUgUlS#L_x73zNZB{j4Kx~F%>(5%k@cxx9qrXRM zImFTKns*}wSd1y^4{`Z)uA4+zZ0`QUcKhxk1m;C5T}DEleT{THFSA_HGmbBsYMZsq z9ZpGJHW9Hvcpm1mkRA^AXQAse2~*gR)@sP!Atk&()>-9iz({tc<%$Be77! zJc2<7k;eodN{E`&dJx=e7M8I2d-t&ebZR(0yNzU@6#Uy`j1|ss2{ijl4La@G>ths= z+{bIYl!$@%7@wKB$3C5TtEm%6$rfBQoCJ)81GoS#1#HzQXw}*0S;>{Hd`E9Isw8hL zu0YO8{{Z#5t!KK8AoEp&GZhRE0FPXKD}ISfugh2Rw`Vq-ZGSR zi?cB8ZFP%VyP2)0(`AtJ{*i6w6>YnQz}dm~C-J8ziFK>lCbPNIt>9(~uW|MulcItS z71sfBe;<0;9$b6XRDeS(CeF7@Pz6mHj}HdEH+$7WCZpBWAgLUjzFv8@4*7%D>Rz^Uofwi_N%jPyzs*x zARgUDee0An_;oZW?8)7&8=_>EMsUiQ;Q<&?-=!Lkj+5w;v{x5CL}3>a;zaqefzR~x zt!FBCSBZ3B=RQ@ddlT!B_+sD15RFq!NbOcR0>Zk7o3m#DU*;#N$sBa8oif`*Z8p;9 zLbrwp1PwLBz<^|KfiQU=PkQm2=o?PFc+zJshvs!+Hy*v~q415~n}4V@H}YLuT)sTN zv6YTDz&neMes4?wJ!+**I(A2GEKKNPZzZbJ`u_leYR!1Q8}R_2PkCasx?kSpat?Vb zjzG^Ny>`AIn#yS|FXEN&r8ow8kl_zr0b`So$P?>agt`@#)x@^4Y7%XbhAp#@M^9ck zt^GRE-$>I&odxW&BQbL+19nb21CR%`V;IFNy;;?W#Y%ULJrn3*-oT&mmETB@92S<- zEYiDlKyYvdIX!r;r^4|aV%_iFCx}k@iPb{2z{$&CFAAdpdSe_4&yPs5)9%czVGg3x zKIWSGMSq*I0E`d7AJVoa`%4#go;!KY^gAaC6-EF69tS>{Ju3aDNTZ7tUR7+R(?n@K zpPW_pHuX5$2xZCiJBP62+upKUOP=hJGhCvNy3Hao_4}j+`tzESIdw<3R(120PUUvE zIc{;+7$c9?ugK3KX(U!dfX+Al-a2OkH9V>GId5_cYg=6|O_uPNX%6Do4aodK89gd# zUTNW*0IX;WGf2`5ax?QcBz5oUQLKu}H_a&duna^Tb)_35Wlt`As06X(WO4U_JpLZM z)y5&QVdaJh)U!5r?IoCC6WDGP84M8!&h~cUOCVg2PTv0j;Zev~NwauhPu!9|H+rrB zs0@-8Yyd{=zBxYRp8R*M5=59DFS#w@St4eS257R%Ht-zw&u%KeiEcFLl_Ou>fPJ4URcA7mFFshVNJh*d$2~;@x+Ect3|7v&C-QIr}^l$N-nf zR#Mo=ECI;L&qCdM5yf9YUdASpmiHFgDVyxKKe)NSxGTsq$teSPJxBM{6+R{ zKTy!`VkUsARxbN-L`ctEol0w@+ zIT>Ua^}y$xdvjEg9J;E7+&Ttsd-cy>{}pNE7FAftF0*0y)P7@Op}lM)MWpwF4v! zt3Lyg-A5niG|(2AlLs;=85!z8`ug_Br;^VR460Y|F@)-Q%BX3Wqr(D##7&68Yl(s;9-9O+wXZ5Q4 zu)&f=Wf&xu&U^F4T(?39V}UM1V75bgXEiKj(OLyU(kVYL%OHGl>e&a5f}QqO6O24) z5BFGc?npTU{Nk*@%JMLh;v>nz?;DS=8NnFGze=&>ugWqBVT`a;Q^!CCIp@}p%CSQ% zQn6VKfq?)P+;P*6dgr}ZF*!S$DI;e2h+c;Sxc;=)NFZW&#~$DnfITtL`_fJpRc1y; z&l`a1Px=1|3^-?JYjiBQr;|tNg`4#~|b!{vAI`>+bv;2Zs%un)irptznuql*bG&Ey+E2To3^# zl6~=5*2eEi@U&O3yW6GB%!=|xRf8x|fN~Cd)ei*f2VB>$*HzT-VY7H-$BBc{?w}-|-iOZ8f29KZWn_^+jO4>jyN3&?Ol(D zyfmw870#t@v)R4A{{S>d%jGui_`v>sg?WVb(`i?Gl$MM)51vQXuixq#h4qYoWx9|o zWRn~+Hhpvc_tT|wPY*i2?KvfWrjvyvl~^r18~2k%2D5i*wURVH^3`xiBe=&=kZT(9 zNF`_%aKcCkc+7JwZ=O#^7$cg(k5Id7{{S*+Bo09gWOC;Rf=M~auAwyryzs2i+1W{P zcW;E1SpkhzvIL&k_wVgqv&)?r#&LI6*VOZ!RNGq}KZUe+O*yqoo4I6bsDN0Qf#t4G zwh8ZC)!Mb5ts^YK*|E1BewEl+S!x=0hOO=A)#H}pIQFuL)W!!m0N@@4aP#@?smUeB zmKj@Wq92vG&VIG7VHm>>qO?4$wxsG~Wj52?=;4A(myNPcM>y;~sh2iNRzWB4cMZE+ zpr`1!B7ES26p#_}Timqi_Z$6UsOkRD_Z4C=NoD|&FjSA?&wTgoS>3m0V>mC0CCH`$ z8dCVr7(Cyw3lb#RjThmS~!L0}^uT9RGZ!nkV^6)?%#(Mro^sDeGmNy~y@8keM-Gjg%T!Hx4 zKjFB1>v?1ISTqxT+>8;|pTu^qg49VAj~|e!8ZJTEjP&Q9UORTIYR1!P4my=XPH@W` zmO07o(4WgVt4SZ5YLXDkESLk#7~BpxIp-NGQ=>;18(KzULpJuo^yAq3dQ=j?F(g@k zFcJ#_GPrDp>Cfrfqm!FpYUX~k2ibJ;;deZ8DP=t5VDpd6)2wanC6eOW+`A_IiWK8& zb|W1|;g8C+E-hBt*fWDPr{s+#0D{>)et_iBVc}J{meWqRxC`gv7fXwOFccWc3>i)d z{t$T2QCm|=_7l*t;y;J_PlxByZfx)M2c3X+%OuL&asdnYcdZ>OMYFwW7GUcN{Oue? zqXo~@XOC}c%<&J0Z$Gpe*7sHWk)4rPTdkxh4tgU005i=qLf0qJqL#}1OXU~DS2CZz zFfq{duDbNwX|K32)8(3XC)Yd&;knB`%h;uJlIwC#e*y@vIM#e6b)`zT8g%VEVSoqj zx)N|d{dMUVnxVRrY-e`wLs8gUyXq@4upUP|fZsXk!Ri3XsDzq2YQ;OZxru+I-&k11 zaVcp~#->@K&Jm9oKH2DMD=Y1&sF(x*4o*#XG3pXsU(0C**SNB^d#Ce3Didoe%IvpE;lkmw?7pMJGHiK*%^a@A8@)A1^VKYU|2$5Z~M+QkB7Ag_k_x zfH>=1l+)Z_NY2R4$C3cfex%n&rcAOC=3{8;J!_=W^w=z7REpWrC2&!*f=4;ewsH7X zQcWh&HLTh^>rK@z^oy029IBogH*L|$>Z~)xdWM~I9p$7qFvk*G$bpfh9LJ6rW4Q-E z{d(lQVWYzxzxI?nH+jBQ$XxAX&^JBu4>ZpN-%1wd$dbWVdag5;1P-0@KU%H#GuZEE zdtK;+va|3EM2@43_Fm$tOeBIbHVGmMrBISfV~%n$LQJtUtF)L?fP`Ul&;I~l)23;~ z1W4F<0zXhCTxSEEw@DM{+$6oag$l13DhTYl!09FopKYJtc{AuVTJH$yP{(*mM z6mPlUB?jP`Rynw}uVnqAM=_n@Z;# z_B`{)(APT$hNO<+)ns3@&gH}^q-2tQRc^zP$6rHCOt%V093U}AcN^Sek)GeJSTTaZ z7A1ZcgBV8{RgX{kQ>Zc@X(Jz~{C~SkbCmy_a&!;rR zQ&_fwHVvfp&O>^RM+U5Ynq@;Nh>Z1Yp{h#-s;BV(@6a8#UddwS=ZkBFaS zLjYp{$&x`CJy)N{tpG!@6AnIL0VfTd;B)fhr}$KPjX2ngAvq(=ZU;E))P7%DvnQ6j zg2;&|7#RQwK8u~+{X0~CbZF;(TYzvg-`h9?>+AUdMxW}0V;hD*KYJsu&6CGWXZg~y zp@9k#Hz?Y`<8uE13CYjWtb%;vNnjOG1+cjz&OO;Z&TK=N+-ooMZLktfX>Efsm9?p?+bO+uVbU zXWyE7TF)vmWZjdrlH8sF>_1*TYFpH00fx9Rl-{Ag<&>-l=eX`a`u!_r-7T=@Mr^l8 z*^eJw4Eujf)pioi5LGcN70GyHSli_%_=w+- zw}d`!B$@?pni&ePC#HG9<0sS}IpEcwv)kT=j@wX{K(cIPDhUU%C$J|N`qgFoWwzhk z`SCjuBzsOsILTJe2ac!n6%o{}681=q^q*~c2bTeZafx%B9H|Te&pADD#XEs&<3_%= zWmubYlsVm!bJT;!%0D{Kni(0P`$3W^SDmXXuI200ADDFN4|=gB(-SPyvCak)Y4Vg~ z8NoOl_v6!=ZOzfS1!a;XU~#e}!31D!EDn7-`c(*ySDXi3@*vO1?UT6s4&PEKA^Sv` zZREBv8)56mQ`7LM%<@MQqcemdLnD!bM?ymk{{Z@j6zC*-mX(oNW5>$Usr}&Xf_cEl zOn0gf#`B5&-`QD5c_Ycs)SS^n!KjwZc9Ams$@jy+~W!mR&ml*yOWBfq+<3F7-dsA>lraJi{YFwHir_7pAknzh56VssYR0gU;2rOY~rN>rX@AwR3 z{Pn1uOMm{AcPmE&V_+Y8PxnV*?^Z;q8*POaIEw!Ot3ko*)StX~>s#`|lJ3T5-CpWQ z!DqPHw}7ob-Twf9gT-oFYPWVi4Y?3XzJ2|`LnxEy5v~pgJPOOY(h)Sayz=%xo3jJA zbI0RW7B>(>2cHt{`O2(+?2b4mHK(;r8+$gnTiJ}(t_-kRE4e%nNj0wx$atc3bO^Xr zWp2W=Z{XfyN4FnyJ5}@l0P3vxne1l~vE36KsXynfb4ni48g7oP${e+H>~wbm88EEd zw|vB(;v9eV>ZDM`5t1yDukx&&$F(ugtZ{~X{JTbd*pZXlu4n+nvb2i8DI3&|oqsCB zm8}jsRb@_IMityw7BiUVCt)P12am$E68=GITG~R1BPpI3&ebfr+HgLn>6*6D!FzAy z%E5r)9GsOkj9|Fa1lxsN;ei96N~v*m1R7<~d4k+Y9@Jgj zwtD;1$I4UXjrTn4x|Cy5qqUm-O-~8jlVZG$*=+uGqVh9cg~tT-u4lqm7zRA_V~Xha zV(UFXt|_ZZ$G1)1CQh%crGaSIYs-K_^u~MV`cpJ5RT+vpg^M^1yY4ac&uYNdrjPq# zP{`8q7ifF~=mr5N{{XLDC8dqTpfuVvI&!>3GdbC~k_f^Q2_SU$IX;x?LOhpynZ{D* z(_^EOcQHl-1&nobk^s+Mdj9}ZRVR`ww2^$Rtr!6txa4)`^zBYZQXwQ9G5Lax`*{8$ zI43o3B(jl&akNH7W!@AIknx?^>C&;G8jUV-R#8%XyBzn0)C=423G+PNs;appbDVR= zE31hSBi`9DvK3wb04Z^k$Q+V*>FZp#hHaAJZmx_5MrqF01CVF!B*2tq3 zUgOV>SClJd&?7~itH&*m;v>IcIqGYt@UMsbQFnbE_Mbc!s=3*7acX2zK*1O%lbrPF zis+wCgHF=pmi-YVSX$=pLKHD#cC1^7>%q@oyTx(V`j3ikR9%g7duDHvD5Z!$mYoN% zJ$qL0prF*`%v-*QsjN@04-j43Yky@+lB7c<0IEs1z6m(%S>8$=HT3j|#UgU>%k#2= za!A~J_UtRV@g1hEulPZ3^&2~MzO)GOUBuXHfszT?)p+brHOi@jP|`%(Lfs+2mme@5 zp5nTtJ6casLOj|W^_Hpqmn@K6DQLMnoA1Z<13VtzpL*;x=wyloPnD*Sd9C}#Tx5EV z1_vL7b2l)@sVhXqVY(d-H)Lm?INUwI!mQ~xy0(oQ+rlE8OOGk0Y>RHa@$+`$7|5k6 zQF@{KS*6O&tybe4{!F;!=dmIW1 zLj-N|f(Az1;~jo(Ptvq+d_Q~rmgxkKeE06iZs#Fa%^(wwNzbY7JAg4>b8W0@dTrA} z7?nfiye-P{#yz@Kd#NP48QJ$bh@@!e0Wh(gH_MZe{VQYpX*Atd+WIL~zR;dk#4P^+ za~vzEA5sl)%=3v`=aC}E3L7AM4tsw}nj2)5)QgNrCQR-02=MA z)<~=^;8(_1V-R^b$UmiVHthEPA+niE#z|adc^@zs^sZEHdQ|2pRI0kj*+08+(;ndT z{eLQ>Zqq|^8$6#X6TVX;BM^B2E>B_CBZ?nvo7;PD3>Ob7`t^ipv zlgB?y{{TKsApPWt7?8^+-C#!2ap{hqhqW;SG;yNs4J($(4IyB^?t$B|>DGf1Im$Az zVouYN-Eq_&JDRg8k76=IB7gyQBoQYaFhB=AcVuIluMBd?OlVnDoNqC(0Q1M8&;J0b zfDvJqA+eP019M>QR?ZJ6rU3>v`N>aVQOgm@Q-ieQAYicrxjlIYrB5nEu*|YZL}NS9 zWq}^xXB|6^{(3vD<5mb#j#@~`bF*N6bDrG>4nGe; zPKs4%{LC`rC(T?p0~jM1$6sFcHvuB@mkaVLj(%A&yB)g^U;eSFWPqaWQ?VE3$sq2} zZ|JPXgrOL%5 zR%x_#CK^z$jl?j&WvJOae=j=qCAsplUpg;}MQw#gIuyK%BI z2Y*J#uUvg;*Vguz5k{n$Bq$ImVytn;AaTd1etE4KH24j=Tt^y7nF~c21&V-3#yS&& z)bysNmjKH>n6_v63`&40ApPyY@(0(xF;tohGZ{|IFV2_){ay$E0IIB(m7`GCR}um` zKYz}olS?uOYkRc~w{r(#NEyNU5PFPOUEFujh}>!r9DMGjWZa)#$ERHW2A<+BkrYvp zgm6a;-A}DmmP*7CG(Iv z_x7f`O%snYHjHF|xZBsiBl-JPArZaf?j;pe9&n_Q`gEe3Gj5cZAz0%fRFU=I(NrVH zmSM1-gblzRgws?sjfENY1L;jFrca+3=%kJXPj2b}P?*OAsP*mqtCfRA%MGR`mO{Q# z^V9EABAcmR_U%~PqkOTC`Dosr!`7KFC8Dy3h3DoYx2ZI*EONS{D|rZnMhG7+I-jXO z&a{5ybPdL})RW2P5J}Eh9x>PqX8?8-=;L`5mn>Xh>=?(?XFruug{9sK?oi(;8R|Rz zYV1hTuE@S-Vym=rd8}pR2^yM;mD4o)W;l2K;}g`9d(-rdCGY1}Q-CB`xCM)P;C(s@ ze~IK1Yt0>^DM(O8p>P!Asji2^5czt9aK^!fmAg+XgbXB+v5pBC>Us7R5!GsXIFyv( z4#>{&BuLtZ=;A2K2zDcG6n>wTa~HPz)<6)c&OsxB-rwiFbr<)E9o(@wEiOuya2S$v zk(!3;*qZ|&2zcQU`ObM5`g7N{TvAfd<*6=tHel%!$Y5oSWb-*zRbV&dU<_lYT>e$9 zaAvlONthUs(OiIoIV1wD$7~)?w@S>>q+4xP>M3v$*LDaUxyMTAA)n7HBpQaM@y6Cxf|Dvk<&f7_N}|C z$c~yq7|{89cB#Q^o=L&ST%YnQjeE9^Y>Q&Y5_xAi`H9AV3{zCp_T17HIUkX3O(N25~vuYDf<=ZM}YDb)wVorK}Dbt+WYHMR!u%#7vJ6xsVG?C^mBVwK3A1~82 z)LrgIJ9s(mT)vy)h`!$xmyx<#C?j%>#2(ysA4=4?@kW;|#A4n@kh6d@9?|&c98oCF zPVHDC;TYLFlf`#yrrmjKZ5Ep$QtK-(?OZp?$tX7=$8uo!FhDp68R3*Yd1J z9$-dOi9+pUz+y?pI(zo3{v(U~I>SxTS1UUpn&mT%u6hIS-|?%Xc}nl0lWeP_rO%NZ zab2u209mp#_0Kiin~S?EIAfk?2v8yv4669_=bGiT{{XVwLPQWYRl!n#Vey>&-1=6` z1>uVlOjb0@vjV_@*8|W2j1PXMh^}VJS}T{{mFVan5n*2iKay)I31ZurbKd zs&cMQ8$EN!@)c84)t*+%yKG)pIOE^z?^*U*J;uEtoU0|CLZTCoF}cU;KDE^fQG&bN z&Qo&H6)$!Bi^<+y^4v+@Nm$5Fzdb8Q!KYBxwdA#3UQIsOA(nfF849@r11B4pkO#S| zR$46bYd3Z>TKRVH#DKsUDl?wFc9t2)tEl>ShDG6y-Wc@|JjfP2;J?hVs^nuJw(qKr zD!JQs5>`D=SJbU8bf}}Uo6dqu!2&QCU9q3KpmrDoJ@KCP=eLn2nXb(Os1Q$?W5DAl z?=SVId_tMLEv5K#M7CvFZ6S@FlyGFhB$4$s7O{I4rJ%)s1232g$fN==-^4kmQdW}e zT_w#IZO&I&oz>BywlM8G+T^}SKi#dXc`q8y-sbwu!RIdMnKo3?@}|2X107!ag8tSk=XgL+wt~rc9u@#J-Z*my6V-X%Arm+v6ZD!^GTVK z>T^M-PR}%d7tNCih@k}H7I^jpdF`SoRuMU}M66Uyz@M*FGyaSgruT=bkc6akjvp4MH4+6JI$}dVHt{^{#oai4=zn%&ZG^Ag=@Q z$2D_BoSkCXWqArK?raS7^dIM?WzHi)NpK4Aq#m`cqG>vf#B$wRLeDy&Xd^+FaCqT; z4@}k^sl7{0t&aLRS-^CcFz{5xkTc27s2n{N&H_FNwK4f82X;SF77?XMO(0rft_TH zUuu?BB%QpDxhIp3y?E_P#Wn!Ry;BW?{5?4O_37w6YPv{_Lpp{~O14-Hy!7eq{(Cs= zc!BwHI=R}+6nE*IcJ>&@tuVQ1AIp&yz?Ai9%XRC3J^i`-X;6}_<@s!k0=^KrUVVVU z>(lyTpo`5OZouye46l?J0Cyzy=ePd=UZZKjm(CtqkTH^@Ja-RLZB6}tJP?_8h2ei_H5Jgx{T@+LVdaz=d*Jo@7l<#WFw ziV~rf-S9Fw^*;S50%;~HxLD*1lFPBT2gj-JjynGUI;37vDw7F~zUYi71EzAM^dp+O zs~nJ-5@^BN2?f|WC5}K`{vYR<*4}C2xMsL_QhDDHY>|z>B7wAiqLsja6%o5MhsYsR z%8j`YpYLtRJx`}Jw;>_AWNWFj6NQmo6=Tjj4!Az!9P!?-{{Uv&t;#~s$!g(Z*rg1`Z4u;BrH?LucbsF4kTLbEQ##yA(XoaW z##MLVw)3i!sTE@~-t{B52uXrm5Cp*P$n?S6(;RzK=A6ebme*>)g6ORn+6Dm!BL#mR zMKPmGdp5Tgutg9%gmOx*G25pH{{XG+O@jGzwc>Kfeq29KO`vCi%`_%xM%R(4Z?{7n zY&ZlDn03!9{&SinxVx4%j@-jEV4dV)Nk07Hy>snV(_?^=PK@vJ?2+=ob>(sg{{XI` zMv5ufjjjlA6A#XMZ~)}hi&8flfRTmY+-wXaEz{k%w{H34)0&Xm#&S=YAs9b%f%NAl zv<<|9Nm(YDz)%%H#!fPQI{yGl&Xg#SJF(ov?!#^J*!qBZ{$H&CFiP@9%Z;c$T$siQ z_28UyQBQKw#KJZNj``r@r&GbJGMJZgswiw80K+%0PD#hMDw>Gob#;z5-TXKp^Nex_ z6wrq{WEWXoi91Hqj-c>Pc>L<6#AZ)2RC(D`A1Gb{`t|9awPD%f4b4n z)n$16;pHoCBaGvL+yF=U?NlM+-JUqhNf>Rss{-47hD8+{EAIK+G4*+vAIlt3MS$@9 znIuqIbK54bSwv$VYyHI;#(J8gJn>I;=UdwgWN-+H7_x@wa7oTP;GXqmT#GVT^6i?+ zG1P77O%tCcNsd*eI~9QE-iTTyF~{a2@P1-WH+Cn`Qzd0cqD+7kX9wPxfl1)t^#q(` z)J^j+>7g~qG03D9E%#V%F9gru4wUz^%#!K>}}+#_Fv^t!)|u2aEAlsDc?MC zT^^61-|Ckp+fGPsF3e@wM1gjJ!7R;yIpdF0P?h#HqL(^y*v;}Jn(3R9EYFq;_qKN) z*{+wvV8vspUI#^Krd$#WI8Zb4^MQ}=ay^Y@+}L0EZdOf3(pYb0LXm@xS-8r9)N$L| zmR&;SG{5YNK{0tG+)6hm9ddyB3<{4UwMJNqld6`4QVArQH!m6U7kElB3Ks+&yj*i?dmzLG}EWHD>w**1Cx`;0|OZAftsZ9 zy}yeLk|rd8Gcu0E1De~8{{U+|X$%U=`$LWxw$Mq>bIvpMqN_qom{=L@p5M(8`LU2Z z)8*mF`^Rw0b6hq3wcejNhEpOKNfG1bQJ$mR6W+UnAq3@PP^ErHLJAJ2Xan4J&r?v% zt0-05Ceg}(7!9ZJ9QOLusqzi#a=IRobv^7;n?&5NkS;d`VtR0+u@%|tR~K+;+C&rF z!3(9dmlGC^ie=fc^6`U$KNC>tqFcitwT0hd3eDw{k8^{9eLoIrPY@7wX>RW{_#OpR zjw@!lLnKI`V=K^}GJ4kWeCp&glV1!y#+DihWO}2^7RO#U|Vh+1w5fPCHhk=~3%idAji0nC{30p;;Ck*g3)6R1@sH)nlgX(p{|D zpgLvvQi}I(sgOo6u!EkN=O2wXQad-ODR(8*ZP9HW8-zd38NJ%?kNMPce_VJxKK={xyAP(e&mocX+yp2*d35 zfQ8(8e(-^T*OdpL=DC##NsY8v(uSR-z`ES1zD>LNk*Vu&_~BPR{G4_*nWwGRuc=*X z*E8l>T%GO7YVEI`5Q`A1?CR(- zw;c~6x8zY=s7dQ~Z(CeT8{~ulFYq@5p12%i2lc8N)}RS6Avr_y5PSVQcR!6IPGGlr zgE=G+7!SQ%_1(iTDh^%G0Y_4Q`t_wcX=*9S>`7|}_O15D2kg-hWF-Fptp5OK@%~lm zo)@&9T|&%BYaP5u`-1G`g&lh2aa^XGZx)xQLNKp3-R@5SIq#f$S8d^0opk+IQ;;eo zHqGUtKH#i>i$1+-qwh?Y?!?Qzxt@Jbk75?@Mcan~Lqro_lfA z(wbMD*gYX*&pstw%d7a7@v_D;phQoo?mv}t_Bx|BfY39_%nnc5o%Z zH0yBlz0?ag)F4dS`<%Vi?O%uuvVjmn^eRrDMIpT`vUVtN!UyenmCIMg-U+k1God6C&$h1~e; zdjajwTyb4gx|F~{Z>NN_w|7|lyK)1SIpDWmqaTfP-Y16GXm>8nl7VDWPt47bI)mSl z&-hiFTc?vxgon$jvYnwxQUE!}@QmjMo$au#+||-8?k*>_w}o6zhy7ofw=53eqyy0L z$*Okk7NO#6``Fo5+&dMJoL~$f<2mJ2pU0Zhw0LyQF6&cflr(ngEXM96m4?g=4shEW z9e5|!vh*d6&rBg$A)EJ1+5H6zx*<}r{J_m1Lf&I=0LTNs$~ngzPz2j!jU2BVKsz=cQ|Z$ulTC{A zGs$Zj${9{dMy#Q^B!EcACm)UvG|_VQ_{k~r2Qd{Yc;|wnAPoDH?@v(I49pz_V0?|7 z^v-_we!rCfI@=!Mlj3)!OJno z2N+&^XV$T|Ti_?}7G)cJ!U+sdbvVK6`t$; zz}Q_%($$9RDWP?A+>t2QcI2rbXK)1PJ${^u*|xZr*K6J@q;L?D%+WHG$&4@hGe}9v!BNTX zPXiqHsdp(IMed@9%#voem3IV$`E| z01Wfb7_8R4(=DJA+C1V$0a;5NoD;M%H+`oW>&_{u;ypdWh*s&MNe(UW7miOtGJc@* z`P8)9D(JPUOlQ0ML~~C)yZnnixLJPB&UYxz;0{}y9>0Yp=-6S3E9Em7!z{8R1J|bR zfR4k{IjhOyJ6o8Hv6O|kZ`&atkT}Odf_}A$d8*H-d2WdtqVno9Cezqsj()im`CYXY z$tRWkptmxH!5D8lA%}d8qo1crYBt1XfhKszWJ0(-xj6N#^t_H)RV}X!GZ0yX3aZ^Q zMsRtlpn_sEEV48^w{Dhl?e*ucPu87C~3=YhvR=h7>Hv!O;X z`DFaRF$9s{jz)dXI@1Ef#|R^FF|h+7oryl=gUuCPO2jt3#k!H%n`y`RQA`gTSjaeL zT&r>KR%aVTQI6dzNLCy(AGjCg1fRfr)N?TNP_8k7(y*rP*x5O^S*FUqPXlU^oc=YX z4{B^Eu|$}u=TnLW?pR{qH&%@x?Vc-k|;ftTgu?(@LM=kHW^K_hulBgg}R z5-)S|kHqKnlV(sY)={Tfq9ue>60gb**OD&p5znL6x6#8#SL3f)TY)WxRUi` z2!TJ+51GK^1CLzv_cZmrgxoat))3n=Cp!k>IOKp7V?6r|(#s0NJhtP@GOJ(^2Pbg^ zXCF4ua(;%a!EbF9tH$y+Pu)4&@}3wIjP&h8mvQq+D<`1kzLn!bOq`%?(qMs+o-x;) z``~)j-wvPc4Q4Ai2PES+QsK?T>#7Y2_!m;3Nk0*D2IP1XA*0nUi`geo% zZxQ+H3byMjSlTa|Lcney_W%~9Pu`IdX(OD9C)Ks>QtlFV2^@lW$SiO_9xC_PuDsDK zDakozAnoI+$@j%j(QK|>-GJPY&y0`=!0b<`AJ)1Ow~~m~W|h)Db`r(Cv&hNEPQs#b z=9Z(1b{T5~qBluMO~at|_dRpXK3jPA6(s}Y9JgGLryqdzsl2ifoxdv%GIhN-LQ_J&l{;CN%nFv%a@Ba93Vykq&+KCP{$ zxfbe@vt>MJ9cOI<=n zFCmUHN#;+NuYaX%TIeq#ESIv#@so@y><5mXr@d)GYciIZkC@1WfbpqcLD#=EYRyd3 zk1pMr6F=)QyVzr(>DTqDQhh?iHi>C(5#4VSEJv;i01t0-(yYS^T}Xsmv~Y#w`I|!z z%xCC*D^57=F4Vey^JW2FKt7=3r$6VtO7<4TO352Ys@r_NaNeUN{{SkN76&`4%!{%p z-GT-Cx#!xmbjV)oR8kRHU>o+gLUYD=^``1Fskv~96%g$oLC1gdiqP=jF~cO_lJhYb z&Itpz{{X7D`!+LLvq7O}Ss-_SA#a@{4ttz7c+Yz4!EHCdu-fd{_mbo&JnZ~=1E=R) zv%-SlDOnV=;Fn#v=O5(OpNTcwE4@zI4LaaS4U{6?9eD+Z-`mePJ?Ya*DX~j*iksvz zDtSI(Lyg@)&rA{c_O80(VWs>r-WO|L-4-LE+B5wJ>s*hSf>otvMpXpz31h$m^cA)7 zKT(fNi%HSZN&7q@%CGl`$0XB^iLjpO98Hz=?f#=IS8|gNmd>a$LLA@%bIHYWdZ+I0 zX2By8_V%u;!Ral=t#r=7qCx>g{v5YcT6wc4`_lD2J*tzuN~A_rW5(UPz}y#%bU1HJRRNnNznyO>i%aruN|@3^ zI)V>S0_68U(zwks?t5)vCx7+IC z#_d1VTrrw9+m3URJv#b&(-GQzjOrGQF95hkRgoizY>Nbr2`owA6Zm6_p4udm;z*Dc zW`%YgxWM%z9Wng9YXeEv3|1yGk0eBX?U&{;WPQ@T#!2bYw*LU(A!zO7YY{YZo?h~G z^1?0$2_8>52L$x}Xae7eZwq~&L4$LPIa_mQC8llw9-&t`KBwBM+Fr#T(HvhQr+f1i<)+oRXJflNOBUH3jUGd~kSyNd-agZOZMr1TX##4gN;uNfs(SLPYxjP1bA zI%lqWP#cmPfg3cE%Nr_!4#y)PoSYIervtBC=ZR@^~Vl^Ivf4S%!5< zB+Ddn2T)E4BaT%5JYfD5546e*#o|?F3PVT(9AgBj=jwgxJ1r(S-Up3U_yw9g9FTbW zcH`66v*x!j&gzl6<+x&bZ?8R$eGlbTbz$mV*&^+s&?%i@tl8;txnQv608m9 zTSio!;!p#P$0sB(?tSW{am@D3G^(;j2w4_3&IuXl-;e(QUana|1=2FjfI_OK={AKw z?&k;p0N383H+=wn7ngA{Qw_WdR|U~pE$P=CPVZj*c^8bKiB@yFi4=eQKHK?BHQHsWlI4ahrkPaS%Uan`4C1dz48toT98KqTMueq8!z z9s1M>ZyH*wOFMkPCfNX1T=IGCk5T#37+J&R-1%<|Ky6>SZL?>gBm+4rZJ_qJ{Xk?ovSKeR;i7={Y(z%F+TV;DInJpTZNL3<9?k)907$9Kw0Wcr_e z-Gx(*8@-JfkN0K4@=BF0x6_;z^&iTZjag!fD>70?LOy3;pt1De4mqk!(m7yemO~*s zgM-hp9fxC&T9fSoB-^!7K*%j4H$MLWjW$b*g(f*#KfTbyyo`Dtp#K1kI{`JZjHofW zg;{s1?fJd&+;i*c+N;fe_bNb=G%RrDAXg*Qf=@ZkM>Wg9B*`)o$0Vucaq15Tw*sq3 z&mj3?!Y~;5GC%LGoiRX(nn-4iR5J~S8I||obw4h6{{TGHaG2vKbdFhg<}rbuIyVRW z@llpxD>1B6lnIPSj(K*vB8JtMVy^ z-J)p`W9cGBI(qTz?tO9f#nR?NpEB;+G6CoG?^~#1w1C_^0g4~plb#6vEFPeLKs{+V z+T$j*g{-b7xsDe5K!|NvmjXS<1ClZrfLpc{b<1$uYj%xN>4beUHy($oj2^%Y&fHmRmk45Ei3#*Z5v0`wTsJp z1PZS__yh)AXOen!q5Q5p_>+# zZQubTL{Y{Vk&mgy38A#U+l$VgP4B0(8S7D+>B3N4h2m&r}dXf(V(43m# zrumqt;BLXMLqwiC3t*sp@hT|J;r{^Z>58cl5?KspBCR8=hBg@^a^!*r?{7~|xU9bn zK9OU6b7iaOlie&N1-rMG%5bBw2k!z&^e3sU18@G2g}-{r;mLQ-)#o8V=zC=Q(Ej$4)mvb;OIT_kmr|{>eb5QFxI)=Te z&2%*wrG=O)Oj+EP1CRh7{{TwVa~Z#bR4_*O8?ZO80QBGwaaI}eF}acn6qP54$oDPB zU(crp` zI2g}hO?t++DbhX}Tq%$VFUkNEah7bHA5EZF0in(0SvnHC=RY7-$^1`l{{YIRCIbD!r_1;HfdWH$v2XXYXZFn0sjhB@{8d)8g) zi&3zDKHZ$SELB$p52+oG)7GZ6mRnUjCXO=X7V<`U#(4vg{S8kv4Rwc-C)k{Z*vvWU zlh0}a9DmsEtfvCezDYU<87ctoK&$rlw^xpW^^YZfMN+uu_>XVp&$Vu~sEvyq!*M%+ z9C3m%oO|Y)h!J3h|TYmX1mHY{7RS znPUhB76m0*643Zlgl% z6z&CE3jAXVeLZnjtXZt@A%|9$MuJpkl6PaiQOk|NM&7QYu1FttlXfXYp$3z2b#V&1 z!{;*)Rx%kgf=MfYdv)ihZj~h3Y&td!QL|qc1p-5d``bawae@gR{`J_~=}_3}a?J=^ zNMho5W#UEv@G`qW2l#DQ!pL|ke2S6Y3Xw-C*>WGgUMc)=x}JoF?SbmS9?mI)kA zpL=O_tU;!>=x15Bq)i^ou;IRJ5JAUm^IMlX97sa_npe9)xUvDT0s%Q6#4vrwr3*5N zXIZSALISUuEWss*BcVA1ra)dX^sACzrKOB8SWK%UCd7_h66Ysq-^j?vBN)N=%}vVX zGhjj^xVLtN5+ja>&u$HDLk+>W65Ph|q`_E- za>%R(2JCblI&{hHQ>+(ryW-tUcpys7ce3`{GBDkGSCKn#h&ThJ2BqC zoUTa&=D_6l^z@_qPAF8u(MYU7A=VAU9S`wxd*_UFs}~wWNpt5aZ+AP?4E&>oX5eIC zH`Ck-fGho=M<@#0HF%69G zXWTtFTzmBWdsG(J&`NZ*~=4m`*sjE*p}ws^*I&$mBXn*rT+I1IpJzfcD^!cdH%GF6&RAe8N9JU*-Fa|-V zaS<52;~2O{%!dG|!k@=JnaAN)Bei9WDlpDIL?yY$IQ0C#Ds{EHD}3roxdk2^@A@$4 z+~DS+nn_a;$+l((3Ja={k&bXUL#v;HFvZ(oqa@ivo2h?M+{OW{+N4fCa^y3Gw zPeM5N6xk(x!A1;dd!57{;GPaM*!olCx%1@RMnc7~ZO;QF~oJ9{D3btzMJG zaEoIJ)0CD`^E0tH;Pu_@PuVcKM^9%Qw>L>^Wdln%R@)qoFscY8k6=Ev4eyAod^2$@ zw(kY@uhKbFCKv%9H(YLP_vksUOHlD%sjNx%UCN5hg|@d*8fVu713u>_oonHzkxbXl za)&-#ZI%2<E$cIivwc&U=O8A@jSzP zzr*)bb#JHR-kv1$kxNGspw0@f-XQz_6)MKiFp<>@>^nhR?dPvt56Y|5HX?$_oS;j1 zz#SxWjmMmW+wrRivAL0)79byzHRtm5KGc!Sa0Jd(Iu%9Zu;b8r)SKZ&E4kHEmLO!~ zJQ0)EG_7ONwysuqx&dHc z3)46w_4lN5jOS#t6kw`YoxJ`BsmcEU8dEHIVF+hoCKtBu+>_I>Hh%MsHBY|2%9jZ<+9(za6;!Eok!NJ0Ep9hnJ})vpEt}p z?d|HhsP2qIEDl(LpE3o`bCJikMt%C!v5=NVe=QTr3{;`r$OPmZ_BrSI(rNbc>bH)^Dyrn{ zR>XvQ65xJh()1b5d`{Hc?U89v?+{y$JVs7-mmDv!1Gj9}9i4)RL~xlo1OoW^c**DM znvT|awciyz?ZmC-?>A3@_l`gd&q4DKz*j&aA~t3~ISQ@_=LfhL^!BH)w<|TQDCHeV zh~sN0;g10DdhzObrkg=BossQ{dCtNJ8O9i%r25mY3hdrll{)949N_dnm+MO@Q7avc zs(PkJ7*W#$*kt$305rgn$ATC(H;{pLfCe&f277*f^pV6vDhAk8GEL~FbK&Yk@Y{%noyA}d08YvU%xwvP)>R2$ozX%$64*I13IaKK;hNMAOVs= z1Jj;Ir9PlijivJ+&UL|6MlON!*v znf5}bd1#{83G7O-CAi}pgWPnjMvNtrK+WbB4A$$FK_Uzz6~|I=I0Gzmn$MbTI#(!y zSe;8k!53*Rrx?gRGk|%qK*Bitlvh_1kV;N(6@UY!QxTFdsh^!Z}@98y@wtbWaHA&=(3!!8b3@z`_4 zRf-)(=H51q#k!+_9!U!koD78r1a}$cwl?2s?q?Fp#D!CmyIA^;(a)GCuH9c~SxNscE9r zlQSpPh1R0?228wbvffq%F(mW6V}Xo<2VSSruUh~laN^c%f2w44LDz%3Jdb5Py5owX zw>CF7Qd?TVEzQJxeBa-d$R`=YD9=ur{A$#2{i{_rmkyB;hPk(caNKg+Hy#6WgdRsq zmYG*Hk{h!fBh==t zD=U+GmR?xyU1x`MpWWMTPJ1xOJy#t1V2m2byMt>ndl@a&u_8MkpLg*5rT72=xQrbB zReF2qVrgz}Wb(5cV1;eO=aoBKp4{(yAmf64#Of zqYTg0gYN)#2R%o)=B0&Wg+}*PSeGcJH*P0@HsoY|Po+*ZYguHHDG`*ZAPu9a_8fN< zTCV0EXF1MTF$b?49OsO3e=2vltRaZ`C<1TXgC$?4Gmr*2ALBw=MkHklvAAro1pMFN zefg(P5Hbmp*fOj@BecX|XSE(nhIMhh%a9kIR{#$DaBw~7KwVF^ zL3VQCcaqVg6+4F~*O8iM+9r}g<*KBJ!M}6>Y~zlZ132nxAKlurx=%VYHqgZG=NQLd zr%HUXTBLCWC?kyRXYz3Svz+HV^gU_rBI`60z-74eyyKjYG&1z$cG`bB&%01=BJ%vj z^8ngIvjR9dI3#-VYH8;Y#xfikMmD+6ncSR?JC35M$thlo8i@|*@Ci6R-<<$4?dEk7 z6l@Mx10$%;58+CSEIS!PN4V`97XzmtV}N<|@A%VYySbNSj6wmNY#8crIPchhoYTas z=DSP^rZ$qG93I1-X^3{shs<9%l0NZ3PC9drF`rNshqfaFe= z$RlUvflPy(bjj`M>CH@SE`%#Gsz{s^c@A6k?e*vRQLw}-1<~`0-cDPR@%+O*`Nurb XS@P~_TXaeYIM14S_xq-bV1NJFSKO9# diff --git a/samples/data/dnn/space_shuttle.jpg b/samples/data/dnn/space_shuttle.jpg deleted file mode 100644 index 412a919694c0b0141ba0fa7003fbfc800dd1040d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 27598 zcmeFYReT&hvIjb5c09(InVFfHnVFfHW2TsynPO&UW@g5iV~ic+b-uH^=XW0N!+pLh zP0whgQngf4cUS$Z`eXHD7l0%sCMgC00RaIRd}hGM9>7A>!_o`@kd&kXKmz~(FaRH;o{=JMNe<%OlN3fZ)8emY;Q~NVdy~5NXI}A;NkahFf_I{bs;b^ zHMg|mCHmFTLquR{!b_yiD$5}2AZ%)3DdFX0s_Z4FV(evY%xOZz&qu)H!R2A=U~B4P zNZ?^>W9Q7}!AtZHbFRj7-&By?#4v)$-~LS zj7v#G>|Y!{|M3$2YpCw-?sV?VboNf>^o*RGob(J#^h`{&pA@vto^~#V9<+AO#Q)|X zV(M({Wa;2yX>UjHm!qMPy{ii^(dYU98JOv(nE%7~|A^pk%Re&sH>r!I+5Zda-e_3!z+8Uahs#}`4Sp2)o z|5i(wTAEvY4xEYkKjiXGmZqQ6{W~>YB1R(mPnpsGOK5+K|5*b*g8!!fO5nc|_^$;1 zD}nz?;J*_1|DOc@SBo>X`&8ZBKXtT^4S+BJ0t_4+91P;~3jzWH5(*v~>N9355s^jfjT| zM8*66oId&iKxhyF5CJd{A^<251PloBF$loJQxT76bbB8JqGD-)4M!I(t>pnuQBdH&2CD{hRKFMg&`qxVJ?QxR+1DZ z7bdroT$Lmw$xp0JpMyqcoxri4+L18(-eNn~vy&icO-Vu$mmnkwLyk#~`LE&^b?j*| zgb8w6k%ZtxC3A63y-R)eQZbZj{rQQd%b{#q)5QG5e3IYDj~h3 zwYB%EH#z@|pYpg@P;b5ylW7jZhRt9$U?6C<+)mGLjMtbduZ?e9LoU;lDj6oTuV4_x zcghwOQJhG-S9U)bh2|akH>hHW4w}0k@8E%bU@bZNaAJ@ z1#8;&Mnj#=S{!jZN*Qq6>m=&jLua?`T1mrbt{vB_mAULJo1vjz+G$x?U@RIQu7%4Q zy;)3n{K%J{>t;RKQOztj`Of3fmJL%I-FfQRv8vWcFJ|F(F7?w1Ue^dcT0B=mo?w$p z3;L9~0gGh%`$K7_536>;z%24ff4j0=J_&=uLyG3mWmNVrm|qLpJio+$vbo?Y{MdY= zGqkvFr`7Wixp-NR5|jjL35o`ln@C|eb)*&!H10G0RHM|9RAE2pRHE%`P=P4ih+dhP z-TWR*(nS@YWQN+3EA$Y{cVBrmlH@h4dGsg#INeLE7-K@}3cz7)w+0w0_ZH^c;U!dQ z9b60GMiR5R#C^$bh0|+u|MHc-PW%hyh=oTBw}V}*?lr!&8!EitKSO@{B1l{p zdKaf1@Oo!AcJR3`&BY=Xo_AZO`0~QGa3Pn|NT@R{R>BC^&t9xdS4BVGWJ$HJw$ITD zS8NTDdl#>ye*Gd|F;ddK?0a!0V>u3Cik35hws}U~R-{Y_BOvZk9aDx=YJmEu>g@oH zhv!`GC#;&FskbY(tkz<5=8DB4e#5la&8c(V!*Y$o1vg2iQPr$5G>Cc6-To#WL^I(r1jqug3sln|l zpQ_Z&)2GCV3xQi8Ui%kvs|Tugv&8PI0g@dHRq3&8h4tq&@GB3>nd^4QA!Z17rewq584)S zdd|_|>6b-%hPUcibUW*rU5?q3f8YQL)#p(>me$3=FUHUh<}gxa8z z6vSURolManf_xmW* zJJ9i&OYsngkqNH#=rC&zHc&+Aeup}N(JA&@U3QG!ko9oCTHQBaPv9uMM9YfsO3cbQ z+joXI$r{XQ(9CKyG;?B=Jz@}E$SzmLYw%IsQJ6$0rz(v4m1(34vp~o0cv9z*EJ;bD zLbSyvty#GwtW{WaRJOz#V>N2or)hdtZcl|0#d&8dsl@A-1}~{yLm?*ZGaG>g6p^&% z7?pDLw3EuZ>4?W3w*)wC3J6ZZNJ$Dn%!ax`uZd_lOA>-sZOpV}=J{YAXscLK)#>%J zX7t5zJAL2XI6eb5eh?BVgb_WC#>gLLx{sMkGN3 zViE=>W&t4u7WB_21oS5n2n1-Ie-TOM>7gw_POkiu|K1B>94$v_bS^y{{+9leYtlZZ zhr89?{Lt5@Fc<&5rRp`s{iHH8wd6_scl)QGt;@bGe7dpm!v3ace_F}W*Jy4=|&x$w)hJ;mUA`u*`su-!ER8Ei+5v#Q404Y%1}F&qkeif=7)gf43o zCVj$6ntoL7ock3_7^)GrsWpxghH9)N5g&HH79t9NZPBZ{rCTy~<5Ui>dUKVnXjgns zed)+dSx*)}<9PAQgUyOFPiPp|JfeBl#*c6BQ{|T>tp5O%(OkNuPCXvWGt4l|P?jd# zjn{js$y2SCZJvwy<&LDt>R+AmzkUFQCsC;(wVb?DUUH6mbz?B9(J<~!bWm-MtOJd^U~WIq^gW6gRL<1gw5H!buB&{wSIIj`!s#bi{E{?>gVI9^o#Sg@zGMGpN1RQ2a6M7}L^~?G&~3-NlA(7|0jZu%AQFBFoRz z+-mVR>bS9X`Js0T*?W}RZm}%n2N{uaqf?MMeIUQrnIoA4CN?No1ay;YQo5SV=P%^E zRsIN9J@UpXuA=d0JWibI*uBeYHfksG6;@y173?S<%XD{S`q~_$=cG&gu9P{V{mp4b zmO&O6&ndK_3xE!v7|3n0Me~pHkHE*Wo~QLu`T$6YW|-JfH&7t7++QLM6F~+kpD?#q z{Aiy%<52mj_`nCrF5mWTA2En+91LUD-`Ujc0bD3v7v#s39JZ+*^BtWnDj67NbH%%i zuhVA5o!L>xk)i=DRuwF!))$4k z4*-tg?^HkW#~)QSRN|#Fq!MJ90Zi! z;UFk1501OOk=6b@;SdqKoXEw4{q-C)jRXrMws1TD{cy;k zv_Sai2k(6rn#n=BxWweCb7N5B z^`w-GHQlr;tx=w8Y>LYat(`ut*AyB@-P}0V<$?Fc`{S?L?=!cwYW)zMXm8|1AK94B4Jub90GB9$h|*C7{aWj9C}C)R!xn4}6cbk; z5nkdeBm`l|mBlr#&c}j=e`H}BgrZ?zV2C*@Ra940qcwv7yFbHK%hKk;lN=jE@;965 zO~aIyjb3VGWF8h1VXi9Ei=%CdU=X~N-}6TgvGm`MwyYvuwbe=iE7~2mT2zVBfi2*T zjEG-!I8EoZCt4&oKb8O8_m(*w@M>zr$41J_S?_p#t&aC=p@LX*{^G|v-F88)^%J?U z-#X(O7E*9E66`Tw^{M6>$4tO7gN^$67BAs*WZUCK45#Osrtoi+$7huffcm0Wh=y3H zG|JNiMLIem?(<%I`JpmTq?D=(<5nM1lzmrv0OcpU}=Av>D_nq40}g{1JES z?22aUGiEtDci^9yJQLC6c9K&bgLHs(dG)2mM|TRBYaw5hI2Rj0 zuYBS9WazXtEjD_8-{HbIyh26(HwSLth)l%L2>veBI(C?m-_iF&b7I7YGfSCyS&|-L zLYGL%aHVk%97@o1s?<(;?!xtX4O(wYURJyx?IX#V%OhpaoBt!$-jdfaZ$-p11gq~u zxL=nizt7m}0KxWdGgXDsQc-*I!A1&#;IOS2mw$3@X7Aw#plThtPFoaO`w!LW#?|4m zKaso5<>Rk|;o(}-Ov5a0&|Huhz zrr(HgaTgp;!5e=F-%v2oJr9C!CIIc!L&&!WF0OX~meEm5Gx~d-WhFg0!kPbC-mBjU zX(0a073;J2=WVzt5RQh{zTNx5ZcjJEMuI9j*rVq?3P z6cx#W3Bt*%gvR@XP%6f=yzFd*YY5xk=Io{{V&SW6lV5 z(h(fTW;Ust=8kg13ixe^vdCjdfw=6XYroz|--^FU=BFv%y#3p?Mi%Py3=9M*zA9XX zjP7A#dlG?n0OSp-%z(&mx7Y0Fm1Hi3WRMxc5K3&%-~)(mdadE|Z*Sl1(TjPL$i@=a?c?fzy^Oz=z(S1tmn?_ zj_#B-PYqM|>}SZ=!^khM91ynnfPDn{q;U*~ZSymNjRvo@RN$=CG_N6PZ%V6Kfgi(h zXHd)#=Ds5{Z1WXAiA2`UaZ$LeC{6gJE6W~ogp6_%mq2^zAoLFjzqpl*g29*NwDQ-9g7=u?OW zUmOKDyt(d?X4WERsmM_*7Rv2*fTmK}OQAEFJKCsYzV7e3`vwqUkV&}Hdt&I*)_6mn z7mzSty{>A5y!ZJ9%j@_^UJU~BBeg&!-%U0GUHKi#=2S)WWv{oB^NU2}tb1wnCZxHg zIp-JNoTaO0D1ghL0#Ab7G?eph8E$dn1?5%ZNMS4ER`f@qKZ|oetpZ};r_U4w>@R}! zZwLtlhzyEE2!=wyz$mCFfclRm@M$pcg9NeqegMd7x`!5Qyv%oa^?fOkv@H1%OX3TV zGNr6u4%j4gSq#1_mdXIj$qZuF-+}(nF>K*OU*Sp&IT%W9vrCMo7v9rk*BMF8h(s4H zdkW;ejMA?w%xJZ{%JSVL22YDUSuOR0PD|*b@xF`8G37PCF^pGci@cZyh? z?OUH!`G*h@jCErA@LfD723yX-oadm<-Ftb4FO=B(KFji;7C-be*~-Ft&L?${w=94G zi;~xgR{BzjetOSDturSk9nX4cGccH261Nm-Av20m3~7lT_+o7M2LWjp7!bX1o{(W+CWQn`-7vn?BId_V0n$OuNjAgD-W2rhu?81Ro+LH~+%9@k$k z{Nak9MBOOC*Xv-V!HZp@Cvk{u`c3g+tOD^JEIUP9O5jOQSPsYH@H$Go!A)(`5ch7=xH!gk;}sT1>D3*NbXc`&`<( z&hqd?6PPHJ3u#$T;eaaRV%3;5n*W>B6gkQlL0z0av3RVGtkRFzH14`Jl4UY29eH)@ z^nJ9a?&;k|jV^d88^%F=Mwa!w%m>-PpA)B*wasnj@7LQO7zo5!+5-t8`h~ zPN5_wCgJCaKK(LAW=_nBIZ;TaeXka~X1`u#QAMx4eBX27q|9i_l&)XjV@lr;+Yofa zLFvrq~!=Xw$oa3J))(Wn@H66x>u0 z`dvnfq99UBQCJL;2-T(P@1lt!eO?5hppa1DP>>Mdf7k639R-3BA~PZpF$pThgQGAT zItC;V2sz~wGq42K&F!HID;X8^2`D69-$0NQ)*A=)|I1{CQ>*VvN}1>egH@Yh>k^HQpH;zfh#uX@qAXT zajrAFnY;X7Zl#7djNP41rgtiy^1zs_i`BoQ*tt~|y zZQ4|e~>mo%;Em6Zm)y7WzJE(T5+e^$P~6&$iTOQ zE+cDbcZBiUfJ8aV-VJECtNY$-a^Ui5`i)a>ad%dFt_x90v3&>xa1y0QI}t!!u2{^gV38ZE*@81p4u-{GE)C z1Y*z@q56yoJ@eC>NHQU3wsx@nZ$}q*yx#$?+)u|1O!e-hNalUktOH|4XIpqM-w_fH zOvY&+h0cau;XSEjqgsvltg`9%na9K+w_Ht|_j6vKJc!tA$(_grI_$<~EN3^wZ>BFPg?o^cwm({kR+lRLOpVN;(wY}?9^#C-Um2cFZn)E? zdV-2jjLCjkMtXMBH71fG6DLhOZ0?pge@$5v$r@AIz@2DxXXW@F?C3MzKk-=V($YM+ zb~8&kOtw&mANh;|ju5@4R6-B07|K1q=!XxQwV*VF{!kUZjgI3qH$aLRm;w0^Fhc=b zyKB>+7IN>PyKQLfDnVlB}adpbu9o)IXUu zFmo5ejW!&QniO{0BE+4gs@>xXJ`wcDT=-rq(=Qh9IN-Qb(XEcUR}aHYYG%_Z7R!qieOch5 zDPCu4ni7?S#lI?JVvTEVVJ>EOVxKNk!ekMm`}$S6lyi<$+b^#2Xdo~Gc7oe(&N*EP z#jeStOo0d8shFd%O9GQpy}ntB6alNO>KQ@Yrg5SZl!Y>tvsY%Qk-d1n%ylcGp0fh| zS+P{9qhhDtS%r*DAQ~47T!gbDH!?DPX=_CdA{MjmgevYdPyC+>BB{7wEtR9f zE|1eI9S=hG`c1{%VC+09jwFNg(3!}#ZyvN-30orq%o!x#lhqzA{l!l%!&vs{bWJUt zOPK=614sK~V2wONj7?EIHOHq6t-fJ61wJHIII3?(hHL&71*O#8^M)OO(@X`zDhWtJFC6pJH?<*8a_)gRd<>%4a7E1QIO49z&$QI17 z(GBMoTQOE$!!Z}0Qq;W@`T|{vCkeLWwrn?X(4ynICBkj+4wt7T){I=7)R^-mcQPh5 zs;>Q{+ia=LR4Qq9x;UpjsWG%wd&Y4SN);c@4&3e(s+abg*v+FnG{Of|o6EML&c8tE zZ;)2fmrKsC&U9%PxWwL%_y5Ln#ZDtax2&8|!G&rQT4Qd5OcgnfYC$e8M26UcEk!D6 zA|km#E`>R4$+MCWY{doZa--!V6etec{oFInWh0F=-JxmV$Cp3Pu@5dclkX=zQ#gO4 zq*xVlr9RwmD1(Jhfcsh2rn&EeXgeqlKa(jSH}yFlbQv1-M}dvyaHm=-!Z>fsDXEbo zVEndyP+A};c@w*b8JYSLaTtt{+k6 zx32)JcJBlkb7@Hj^dXQLpN$N%s8!x*PCIh8&pE#(D=&%xiBa|yiZIc$Uc!Es7Poz4 z?T;r2q4SP@Ovd6~esJ9m*lZw@6pX*!Uo~6Gf@jW&EqHqK=zv z>nw{w+Aa2mthq`_Kv>rB12FQo#N$3_2hxh8#$|_SIT~BmCL1Q#N^NO=tF2GE4)K|r zIPXWxc4_9i{1#rAH+AU4oJd@>w!f;cQG2P;uT{W(06>>dmVDuA%TK7hGIA0MfA|** zTE%U6ocKp}>nkrSy5Pk7il?^zqN=c3w8p|A2C7v>?ZGmrQ1P4lv#xSyT;QJRo4+^x zR(oEq!V=DSn|-OUt-7}QZp%ljfrIIH6r{w1j{8roX9fbA9?w z6t=LO`@QV^47lBtb#UC^&gyV*T)i*_IW&Wcz0~}6=2Rbav1M9F>I%K92?3F>8)a%! zm0aJfHmV8HW;BFU3M7>yp0ceJ?6W%epkc*@3S2Ii`2wT$u=A+FzkO(4L*Du1XMv>x zaHBb5=_q9Bh<&*h<6?Gt?P0}lSYY4Vffj{?iQcB{rtagN#WABRA;{mkkczc3)l={6 zI_fgEtBr_{o3#S@!Ay-=yG6{RWiWz(Q1YkWoE{u&c+WXzj*q{fV*(0kO~Ws_FKHr3 znl?EZFjQ97Lw~Wf*p$qwXIIOXLeXsn0g{UUWlSpy zYR0P>1b5u4Z;YGOvC9N4^jkwFc~JUeBn}*kVnun3Fx^7|M#m=dWuarGPkPmsFx!QP)1=Q9rS;%CCv95eo zTrv$tD|ztg>OvCS+xGTD?U0KoOUd<+a>3#TCjA}ix>9_6C+Ez7Zyl6lw(|lHjmI4H zjV)spm&}jq23OL`gECL02V zz1b@FsXEi+7OYlQB`sCu#+x-tEvzrAPbmYjJG!Y4Xsd{(ATQhm5O%Th35dS1CXY?f z`4ak1pcm!OYNNj)gZ9H>vKrHssTrydd0f8O5W6w~1DjKc5 zCC(Qf=$3O?*Xwk59}U&KyC5{w_}{G6vi*fF+Qms%)s8WYJ^&iJvG1yD4R4}#(#vWp zQ0bbpc%pj6gpU~OnC}^v*kiW0MsZfTu-3*!_>goXMYn4C`WY|0^6WJYAAm#MAgHM2 zDUiq1D=5|$;dymi8Q($wE2w<^qFgfi#-&5FbF~NB_U)!$x!tO3Z=%2@RyWi?lxLLB zijg+>t~M!9sZW)0fQQid`Vgm%idKq^wR=E!wwrXJPY z88K;s`ajfP5=Kv`VQ0jwz3QljJ-r+)HbZEX1ThAoECyj!;kytSS=qmCp5pkvYa?povzmPXl-BH7uPVMIYGLWJtm4ADwhh1*h&zE{kWZ!5Pqe(}bw6 z9O7^}uA=q}SBE|u#|rkO45=@LPEKyEKpW{ZgwqSIWq z@qiP@7~-!FK#~D6kFa7Lahn`q2|UNC+Rth|-xg8Y(w@)iurkG=Ndgz^*!2rg zx%?*?_HG4PUWHd>!>Gce zPyVVLTX;xKI!8VI7{9PMDG&#JRJgCw*?Nkc@Q#>ef5W{tXD-DY&NhWcPCGqQ`QFmV_aCLXG%st*G`{`HXK@Qdmcs zl5j{K_6ZF>7sVqmkEX^P6EVLl)mxC8v^009HBcUe-X8$T;3gBt^LjI-zO>P@J1{8^D>U*~{8xeJ6NgF2m@F>I zG+u2IGg8)DJNt(h`8T*&P8P()gRkK`@IA|6$T7KjfafGPG}-@@A>8xt3OqP4%%ygvXgS=QWFUIq?3=D=o{Zx-P%0 ziiaU+PnwPmn6+X)0KpSE&#V(^Ehp+%AAo(U)lwiqGb9H_TSC0|JY0lhgPwsR%4|%+ zfL&G^w!#}sz>&Q0DmC_mSMI^x0EO>x- zsco*$EYUs6+Aec-tWR4ORM9!?mLB8@ZorV$a)T^y{MQvr(oga1DcD^ zxY{9S-Zum<7JK_Ey|gBf?Dipa-P#>|@jf4#GhqESG38IF)J}C#Vk=wZ<^c0@^o-DR z&(KNqml7)$F);WplwNN#))$c(KjDfDY#TxC4cN>=yWQ4J^ExPnUmR(JumhFAv}MfY7IVChyGJEAKd+xl6l*fkl>yBOKVwu-&vq}|I6e}&5N~3W&gV(vmF^+LKYLPs%%ctiQfhKRq<9XkpW~}vhJ^Hkn z>L%nqg>U^Zp#=A$GoNQe(T^mMvG10?bS~pZWd`oT3QZ>xQnXQXeUo|1xQbX?I8Eh> zE9)?5gsQEyl_5)ZHjy6CUT;mii)HxSTf(Vd&NCWSzyy( z2Fz(1nA_;Xdt24pSgU=eTD9P>Tazyh*>S6~`yGrm5UCZT%DY5}yN%W8>u=y7I-7I} zaJ)r}1HKQVd0@P8zfqzGJf`PK-Je7f&Cw;VKbo?p*(;1TaBL36Ln1oRL@ zbqY|)Ojv-v8yIh@kR?M-KXd@(~biJ+NuLv&LcONf!4gnU>?KVLo}twfN`?w$Y~6MwV=q0EQd^Mj&I((b~rl2Ca^>k15|| z_0I5GB`~ z!g_u4I)P`nB_n}~eaWUF_8KX$q-!aOoYBb;?gT^yNp=dF>zbjzlt z+wD(9PPhXiykSI{Bh*UU7Om)6f1$7bSS#Rmzja1H-$t)MQ5c&)Ft*xfyG;i0X$}x& z_~8;RSg+B>n{^7*cE?kz64Mg((_JVeE8RsBW{L|;3I>nsiRX55m9z{({5pbkv7JzW zL~G`9iS&fK&+hlS!$R>8IMzYmlF4hzv9O~y7v)uUt7LqN^`p}lI8}-PoKw`=&xGqQ z&~kd(*9~@IA5RWpoQIbz@g8xFNJwjx5W&V7Tq+L$1I5CFZ4eE&%GbHInKKtDl4+Yf zQ$-op^Be)f5K6B9a%CO~W**mN4{c2f08$ z0P>aaP{EXK%n68yi8*?hK9&&m`Dw?X+Y8soO$%!JP&n4+yRE6kXg7Z*N zdEzD!o1_PMR&)@BP`|)Y#fJ$>%z_-_oGAs-C?hWIgF4|h`kPL|%T=fMA7UrTKV9f+ z_qUFJQd{lnAe#oVY=ygJpre;TiVOezd0b;V^U=W}etMh2ta;yf;u_ z6IP#f2$f}#)*vhngLVuKQL}XbZxT(I-6_w^2^rL9Q}zvm=v02cPLgAm;yPoC|FG$gIu1EpUoXHX$;e> z%*X62ZZ{hwK_OSChZ|W()E`NoE2|C`OWAs&bSjMGSTqmj+03j{-l~DHvAO`IwGSYk z@1pf%&kP29J^+~g59((uJvxf7-nob&3Z%YU>_FyYljyWA=)g?|9t6i2rGt*KQXr>N>sPMBG~Nazz2Mfm5# zFU9GSTBjIeZv`3@y0)iIYU|No(yn{|kOrPf|A5k~ahV$mmN(q{YGJw%G{N0)=V^$A zuX>KZF!;z*UiU}(C%k2pxZK>~<;!@?#ALpVDjM0+>0o|l$g%diPP^fXpraPj`h-J6 zIG(w0_Oh8~%vV=l237QI30$~og4&B|jLF8m$LpcP4oT`IV4~BgcdpDA@QniF-~Gh` zwyNVpnQLC=|(nFszQCl_EC0YzMn`ONC8$ zuaIsD22a0Wgp6UIel8v~SL(pA;kOb@yI31hIie(eu2fYkE-bG5D1ZoQsQ4g5&8E6U zx*v$XWG3FNDu&x;ZMNyC(ZX|3^_7L{u@fOr#rvNyI???hkLZlqLc3^@QP~hfM(_%` z;2tSGtffm_ZZX>gYUPqVwhmd_Ne3M;uRD51T#QGis?>*-lWIvEDigqNWOV<%J;>AhD3wn79HvgL(qG? zAmw)nRI-e-VKSn^BCjBcC4F~d*jH*!OTBy~%|Y&xfXNjZe2}7ch9$e31Fjmm!XqLQ znU)D5s5m=KF{AfS#!W-Ax^SDJx|Bg~@FkkUdPF){KS@B5E5Qjx4@;uT4Ai&BS37i)NgWvz7DkIDp?9g+~L8rpmegeSTBAx_=M$m2GZv% z3>tFz5KVkavwgo1VX2t2?S&L3&fzuD-I8q>kJ&aqZ+Z3WT+JMG58E7$a5r?icyp~K z+=B4BPhnPEMBvP5`|$af;Ey z+2;=SNt~LeTTE_AJ+udWoI1}KaEZI zz$Pi>*7Nj`h6CJC==xQ4ix*kmUG3Kiow;AwKH?3EiuH!iL<^p=&Qwl)Kaarr|0Lzi zX>&7zTk;69{t5lg%M9!Bm;xuEc9E3?srhVlkQ)9_Nb6+WX`dot1Zcj(rIe3u@A2agcaL-JkoxZLAyJj%bw-pPhg7s z9k|=G0Q!9#FPySb>NEju4xyyL%yn@R5C?4VsIxMuN8*GBgSA?ZK&5qeeAvnRB%%>Y zqok8duD0Y^6n&qN8nksV|F{$TPjV_bOlgs&TNSCFX^0OhI16{V#36tK`psKe5T3q zn+zvEs_ATuAnE8CGdtW3b0BQriH&*MabIM?LgC~)alA(PsX{4@JN#x-CY-+2&=|Gf zc8@ti(yd&S4Vk9e$=k{p!TUa&Wv#9G?Tv`DN2PuUcR_8NAi$Dmk;XLN-lNGm zjx%bRnbRYeal)B^YrV*;q4_4xsJgPg1*HUDH~GhxLFOa;W5sVhDcb(?m79I$_jf3g zX7OV1%;tz2nKcQe+get7=@DiEl=EFyY9@Nv2aU_t*J!T;UqYEmf1yofOB6-FCv)L= z^jORVZk|Mkg4RO)n9xP|%6>uG0oM95Y{2{+Jrr3!-|-Z*aMf*QTw{qjncW9G-OG$Y z57VFp74BrgXCYvCZGj`PUFC&|!txZ2iL7{AMUyg0NX=p$d6i-yL(n)!MMDd^|=Yq@B zzMK6+pX)hxg03?$2Z%^Q>0YkVjHRD8eHG*>H=u1mjiFa=xVcLh$=Wg@F9ABm^Mg!7 zt#V{r+crW!@diC)qs_5PB8K^q(8JHMC0JDZmy~}3qGOxv9X0C^xey+@ z9QD=zO%0)rIXO!H9ea_}00sI!YPBVw&H!J7?5ud-@{<1(0{rKlz0Y_p001dZkfjeA zAm#%$qbb8}6j$^Ph?nO%h^4unlb=1H_yAOY8vuL*0mrMK2qjcr%Sw$wDu}l%r2dt0 z{fP$RRkG5D?z0^q4ey-Fz0o4b5$U6fcu}Fs8X@v4s_xkfm(2%81S-*HNVxD5Mq_{} zvu0<1m!umlid1K*jifHu5$1I0NE?^OqCWn)S_N$=^~E8RO(b}-4{04zH*mIMlj=MC zNSvX%89^Jo@gxIO+c#;uFO;|G-FS#8EW!-Z=evaCUU@yM$B-%Essu9Mb~uNy$OdXC z`P4PJYYQ`q!Ow3MZ@y>EX0hG+s?{h?k}SP2Tz2e4u7wJiv|=?h=&^qT4DAuWfb&o| z^|v3Hn5;2Wl{(iY;=)Ni&pX4At>=BVM4NO#`}a?N&g1AeMV$`hGzXI zEb9|$MG{mbFuZmQ$iIoNoBIc4{Tu3rR6?Q%mku*Xj+7;Yl#An{neY50N63*?QI6=@ zk0fE)ggAthC1(iP1yhMPMmGdQP{}t6n-*LZ1xLJF%3#wh6a!-$f&1QXkU;o6euY`w@Z|aV0E z=GQf#lu|^Jrui8*!T4aN2wQq^Roy5_MwuWc*taQqm_-iJ@fj2czmZvzND{Ira%1goseW^{`6p$HL2;=WC==O}toilt_Hhp49hzFnYtz zm`~|V^l}ZiB#QSZ3V6sJELvxWf>~{&b-#C@q)ccTnGH^s3`LOXB8oYokivhFsQHrD z2Wg$ZHJ>uR+)4z8Y7Ns)A(vb;sHj{OkuiW~vmxYo_5mpH^EY-G7NPL}+)CuYzgYbz zZvXd1s((WKK>nikpIgcBn&2;RzsK;2-2bzg{s#CV&7*mzV*C8cZh7T4dgOX=ecItZ zux;Kjb98WVUE04)!JP}R|UfL6mUu;aWqrm8BCYMwqe^W zVDk!>TGO-r$MNyGKPOdMonCG$Cf8pca|NT9+)nu6ivdQabWYFb#h*nyD_`Dpwvzr{ za1yMEBfo4m5`GAE_a@Xk(=(1DLg{4w-s=nlf^;{CEY64+j6mRQa4+nnJPEur0d>L+ znL^|2Qjt7b-ep=vNExXLlU)+&T0<1b+N#Hw&4*J?5N6*wa^G}eaN=%Asp%K41-0xw zlQ2V}cTPcABUzX8a5UawM?BOPThA7%K{Ovq^CsbxI20mX-VpX<{`Bh1!FjI1b+Zp| z9QZU!f?RD;&3f>a0M1S+*sq`4 zY-}}tkKt}GG4+O_OipPvabFF^M&YCT(Y$Je?G;fU*s9R0F13^)?{WW%;z-#oohqPZ({<+Nd@ zVcTNc_SEJpc%2*bWOQ4{xC`bu)4^8}Thect=xgFC$g3@6wj> z0<0XW!nmBSD3fsVHg##BS-kaa1tZ;T>fyaw_X+4^2{y6m9*0-NJHyH5+1%o`m5U_E zhP{*<1vNi=B@BN2@)pP7H~?p2|94ydk2C3S-08o9dH>m#|8geP^&!n&?-AVm$CHEv zQEvx?*4d}{evbkc(tTYXhi=d`;}OLLR_N1h=1#XVv`iqyAFC9hTUzFpOz}>oQfZ|~ zrAUn&nItvjk49bj>1ael`~VDzT%rv6$2(*!_ZF>aT9Gx!B%0=VXIg>VE`?>_E6q~} z%uVn*QWAieYuk^10O+r;jQ{X|`h8~XUvKdFhslUBXYOVsJ^(PsoJ0cBA?zi9R=w7R z#dE}KYKVIESa8VEvYMhyMT!fAj14Bah&!TWA+;!T$hZT7`nU2a?F69L|>@fl#0L)MF)}+bZDsir5RZ z1iND`rhh!q}7h3TyS6vIRSk8ERos|Co!<*t9@=N@=saUy*%<24To+f6x%CdX@ zIq4tcH9W`o&rjpbLP1)14a%5iW?5MC3d5ksi#B%!`Ge`IlFHY!Tq0zG(?+~a&(v?s z+59xft4M_*j4#=?pbwM;=eUdNRO(}8NGef9*U?TRebq`=<6j4<;$2-=Nkg6jrNXn zo-9VVwqRwvZyZnD3;LCm9G2zOiUq+CZ~;)`e&YIdc4|YPen_m`CZ>lESQWs0LJSGs zC_b&qUT|)TzHtyGzEqk6rPR1Zsp6m0E>SLd{{Yu|{yaoB4{&_1f?j3z#Nn#Mx+E9>VfvD-d_g>YM=*h3eTC4ORy1IUo+p8sLB3 z{{ZBM^)(v6c7bu=j~B$IOB}=9_Pdrl{QuKv0En9 zl{^r|;c6>6d^a8=5o2c#`F%kloqQma^ppSC}Kq~C9othZM22hXc){)MPsbn-drcd+|lUV%-Uh3 z$eo3vt&^Hzp&-c8)s~0>66w0SFHBYBHYZNq@6@j0kyQ#~)xwq$`WIaw+&(Q@G;bg4 zh>oaCmz%+=sM~CX3@KH((o}-BbEju$mo&2=Y|(LSQ_!?8>NvNtjLfBh>qKA&TI4UJ zv=k;Q`X!05)kd_b$5;SS+OlG<;ruHbmj3{(nbVD(*PjfrHO$K3-M-}~mZJ(*i;PGq zXqFSmzDMv$a?Vvs{+^)=V&DR>a(Cm9l{A`MIrGCqaStjN(zs^$aqbJaA;>AtZpSn(883d}ChYkFfn)ggqnzv?nRgy`Ne)1=P7QHxdut#bmy zdKa`K&QdI7+b!p#+F2M`4*AA1MY*6+4N+H;xS2afDZy2(er4ChUJ-5RZpA!)%;a67uMB!KD7kofTUoim#vb$8pRvw>fkF0An`- z!K}|WpVX$^5s7uIcKpDYDJ3*ex#=9DEGP`oH!Y}&bi>bRm|R^@+slX$pcDqzr{-LO zD&|qGG1h~ytGdfa1@Wnw6+Fri{{Z#kQ-)-gs_}kf3!68@!lprH%2vEeWf%drgT05i zYGH?T{&CH>XFJE$#k6^aDd5E%VwZDiArr-%VV^mjZLC1hH*-5EIF-j2iS&Ax> zn+0mISDnl*iF}oPn3y4;K&<`Bg2daK;MUkSh(cx^VyXuQ(AS1xlud>ddH(=zFQj&5 z!Ta+9SQ=99wS7-8ygty%wSFOrmn#(-Dl6Gy%Ca`%6<>QKN?6{Nw&smd(x7>WD;o|T z_bAwuQ$>w^T(RusGU1N3P;+R_W3+VG(wAv@bI0O2GL^2?gVucrV1>*6=9zu~_Yp1v zyHEWi13;_ThLD{X4Ug_pyE=~u!B|*Z5VADqCH^^rrMn))te^GD0QQEPD)+n@g8vnrD1vLE8D3+a8~?RXq?^grMdLxB1;XW(=!@j zKq$vwacvxmKbcO`A-*}51LoLqFh@rFS4i!`xN^S{B__xVescui1D}<6gd1D2c#MAo}9xnB@}FU%moFkb$-(^mmuh6#+v-VWGW6Kz5(<6CMr~I$KgaE z!mjMAnF@hk1MMc%9dXXsEfUzxpX-;FDNQYMDce$pn!$GQ?g2}b<#QYLhTZ=Fa)@tt ztLyg~(6gNR@NO4Ir!g6c6-89z{{Y`GQmqsXV{K>eQO|BGru?x2t~R&o9_BLB8Lpd% zNm;PDx^e3jD%*F1T`N5ysvB2+Ncn0lah-OK3!-&#Oy= zwr`3$9rr9c5MI@KPcYt(GDEL;>Mctyz@LW@>33^HTXa}2L4~{+If5Ff@Aza!(1^-x zCk1TOGZp7O@3f+g<1}o#7!c=(NM~M-A~gk@F;~CLtN>=cjfYMl0CiB%`S5O7c#4VN z#4qI#1xv?jiW#a^-^{#1N*U4Tyd$i_fxzW-^8qMLUPYX=q7f_z>T>sc{veZ9ndEBG z)GHQ+x|+nyBC=j;>ue$_1q~Eo@tIpefHw}NIf}4LQsTJZc*JBP+Msjy1DE(Rb{NF% zT^DNpAyf;SQ+a|hV#I0oJ7cuYI0X>O{3DP|t<`Fek`-*IHk%Lm-cb`E9bLq>G-$TB zq6wbmTcGDv(^A|T2D+Z(Et06&AyihZ zyTbl#jDxLFR7L~6!2=0}6C+L|H==GR3Jf&A6DnQ>FASB_P(_xdIaNCCh=~Hd4tSRO z1HHb{HOB=Q@T_7Y%%X%H2$^}8g>4mT`7s1!PG7(3W2)>9dqV?NW-i{u94G(=hgS0Q zn7CZLSk2?1#=5ZCvu89t)=c-ERxLML3-$LHsZE2GI(n$$c9sF2bN>K2l-n-vdi}-} zlQLd+`KAslHUf(_@e;~X3pc;{1Twl;B3agf=zHPGaG1j=OQ(p^g+h=zSyj^EFm1{m zQ*%2s*ZBC z&ZA|(f)qO050+g`&L{yqKrJ`m$=~8L+`~-&0M7GD!~CD>8f4yMju@*^2s6F`-M6t7 zSj85PRm&_eBWp!eVLk6mAF8)_%RcH9)QcEecI(Vu@T!h|^^Jr>btA$)7u;rvje=Rb zdeq3PDl-27cxHcuST!tGjiT`?!lNiK&3mVPWk!*2A>z&->LQMcS%p}BE+BAyuej$& z-)L&;mb6g-KMZALvay}xw7)_Nu`T=(gccRa6jAt`tH8_4nS=%#@b3O<4+C#{FL=Pp zY@?`ayoU0>sC8IWrjLj`G5-LKL>F~`olF66P;vOEwo$#Gs2q!&5q3`1TG-==f;Yy) zX(`QKM=&=?O1}^@&40=p1t`Vg5$xG2t)yLSRt&db)qhSPIw@~Xm?JJka$exw8fn@u2cunJvVd$U#*NdQ^8{6~YWcEd z?Rw%0$SY%a!N*#bsnr6MXOrA*s9(54O(7xul2&0-UXGl zM!a7UELd>bx@W6@aaMqrG4ol9E`7*#X@nKd75U6U7U&MvFT?UgPuG2*bULf^Q4aFp ze~KLS?{Vb`qhypWtgG)7#w~KKQqnb5q>WHZyc#!&SCeD~5wY(G!p(Ywr~#0!gUo&)RkWLB>wHIfVMS?lwD)30wY16Wvk?+C@FDyq!H`_Y z(l45>8h{r_aJft42WTrs4k>|XC{=dm5fOA7cc|x;kX_We77CN1^8r)lqNApe>2WZd zj@{J*Z_mK51{gE4;L6iIFo6uYgD5O5%XKzXb?Tge6t z+E$mJbF5NQAghCUtpkABXd>sz*!5|bz1yR$OJpdWUSctTkq>p)` zDJZ@3D=9Cr1FLJEvWo8;?<{R$TqCAt*B>;ps@GizVB{_VAUt2V{mLktl$k`n(}?f* z5Q?T&qtNYwJ99_$5DcGK?JH(%}PI>n-im$hU zuJCc1KyJ*PlI2ZBc?ic#Jeq`16nve1hD1zGzXWS z?7A*>=+4AIw^#(`u6A_pvY0p(@-n3cJ#f| zmHS@MHMwP7Be6FHQ-c_>5V4h(1zC5dT}LzpRx~$u0j|){G5{M=`6K9sM6tVf!$NY;JTZD}xBV++zNmI}iS=7E`2HFEl5is8TD+cS7q+tZPVwi}me#p$UuDi? z(f(Uf)Qx%D~P!0%}SSA~s;$Eul>%0c&G$)3!FH(L)&$ErKWs5C} z84xcexbZ~9Iz5!Mw%ec{*y?CY7l5R618T#Oi%A8hhbCf*D~dPKjK$V~qWdK}xiP9d zz$6mcd9EeN2JZ6FER2Yj4dRcwh)XO2blLMS2qk(ea*{*%0?&xkT!hM;)W=v;a&00Aq=PjxJlu zQd_hy7cwz&!)RlcW(Mr!`K2HQFJfE1If-Z>5}P@S317IGV%X|AKQfe-OTIhGB`E7~ zs&ZbTWg51tv@}Z^F$DstU68$nq-Lzairn%c-l9}TO51_SbomKj3}!ta{Nf6}ux+sA z(l{**U@xVhMN4iD@s>Xb@Xr`s+xq7Ecw>XpyZ->GW}@{Fgg00ANZNwE7(P@qv)#Yu zn3HD6-oGV76s;y!Y~M?ZFPswYUJ!Jp_J!Pc}(TP(l;w5rrqL?(ZRdo$Fv!pUR zGDrbwVZZq-IW4tJ>ZXK6VZ1;KE zUucUcw@|<%k=Tj2P^&>7Eo!P8h$?{?PVS`*&49cms6NXtHtMpy(ppy@p@Y~Qg4-VAe4VUsaMKp5VcxZ1Vi z2@Y%*y>zhKzN0eX73c{_b33^9g~kJxVQ;dKuT|iZ4juIs;P~X>rgYIn@o2m zT%-uM*f3h6`nFLjB?6?SqLp+wj+iKFd?zE;Cl!v7rv>yb0{F;8TJQw?7PCSds%tsD zRdl%hj6`x3R<8dP?rbgU*413bVhYmmB0QoHq=WfN0r= zAMMJzRZY?ca}*fRG#tVp+JNsgAdjhl(}t#OsVQSFUOGg;tAgEFru71ui()0M+d)qd z$!>jI1Lc5dgSsihgBh9HO_fgkyK=QXv_rts3!q<}s+i2O}+- z-r}-|m?9bgH_8Cohq-5xguu(C;P@jz17T3pfpw)kEF@P;DFY}C!9cfI{|*O{?3!T>aFpyf-4COEq2jQ z3wpDSUEU#6a2qzSabx(V7e$->BqGrBeEt`j}yDY50vroD(-T^ulas6w8&q2w}=81>XMvFvQ_FUD};)Hdb5(@M;i3 zqr*NRi2}hZ9w37R0RW%{)$emQ*q!T872Hytq7)7>?kSBFESM7+Uf2HsV+d_eh_F`e ztDI5PgI$fS97`($Z~$9rvh5jRjRP17iVAJ5* zAWL`!gj89vk@jX_1;C&>Y8xRnllq%}CJXPAwAk0pXWKFwDM;a%-WHwz00<)2gH`=R zK&vd`hOi;kAYyP<{{UGOpc*nGNN+8E;r9@EKHvaVY^(d!zL&BqKM@<^#spwVRgnCU zWiaZXb>oFJTFMrWAlq*SyG-B38{Kb(D z6U+jU%-o%A=td=gNHhw;u*FavGW8iOQk36lmsoC3 zU4i{_-`0gs#1#aDDscqne3dLi5y$fN%4uwn10sP^oEo zH^cgrp@8xJsmY2tc{lo)Os+GU{{Z7NC610FuoR(@?n_e>NY-GgFhzFl3ft8OJisuJ zEN`yj&cWj5G)uSxmFoM0a0OpBfASGf&8`q2xJgs7S-60$(cF&-(5r}NkkUk@_N~6v z_>>TO5YfHAxsNS_k<)W%fr|vh+l>UIZms06#4N};s#L-eIL?zuTN@SkAGw~D6(x*k zj^G+;8ZEXYC@ph-pc`Q1=zbwLM9`v%xHNL>7!IzIv-2M)r4ApNwqdtJ?pLJ>8_K`? z9gj+A)x}pd2}kii&XX{^0~`D`0g< zF~I`iOu0hfG`Tw5$(Jm$yvKW8AlCkSKsHU)F!M3tL%94-7UTTHWQZkYzM~I8Qe&gu zqFqQx*5fQhR?x#UG|7rTPz~U;Ql4N&8qQUSLMd5oXYNv{QyG783cR)_+Y1mKHkJrD zGGLCl)wl5k@yyf0b2o`z((NHIpl9Z4R+w!+xs*#SD{&E+L{IpYxpYf+a}>xZITLyC zz;l*?lataBM~RS@oSNwq(ZTK|$-G$m6P-W{hy8Jkt8C||++v{{WGTxqT=2 zi7dmLej_inWrZtgj9XHIfX+fRAf8^F#<+06i#X`{nOkDnWpiBEiV|EXA0!Mf{?hKG zY+lAH^=<$@bpnw(WTeHu_QCHeQ3~M>wsO*k2GZ)T;FNW6vrsfHFx$CFY_E8+ z31Hy*nX5-u`j#%MqUHz+j*E^KTF^`-FCkx0zu>J}jkyKVpK!n-t3(7S!qTD9&O>cY za&MVVw(&)O1gEhpf%6qMCTJYMqnyceA)xYC&Y(cn<-7B7DD0$3%8PXm6~{QKj1A8i z_vnk3w>Jpf=PG}2SUJTN{4#*LFo|#?9fH4@U6{59+REu`cbZqt#_@%8;|gi8{Kj5ti~E40Jx{2h~981~#!~9Og2bn-`V$8RDme2@eU<3M$z`NC?Xd7>XL#@XtH^pDn zXi)dd{oW|Zc*OM)_8x2kA$eM>;v*ty&@eL!@lDi^*ptaT--{I9G^RxvmOx-SH5PWuOCF6uy{=P}m{Wn;#JI$WhkUW_(oV z+l0J#KpwlyZ~~C@pr=cXNm>G&P@#mNa|Jt=EN<@8HRBBnm^y-iOXwF~NB~;3xbRe6 zo9(D|P-bEhthOEGUOW&VWg&vPBknkFdlfZhm|t06M&HV~GTn5{!gQfiz^~c>dVxgS z&|10T1PsMQ6TRWMH$#R2Pk#xO6?tB?vdisVY52?nw1TRy<_zfz$)Bo~%I>HFeDCHx z&BazA`L#Q3`ygzj*r8st1z5YP@8Vb|ayA;>^or5$OlRFpl~K`EdPqWEvK#MrD=`LG z9|XE-DphdNm}&^szACMGi+oA@_C6y@LWIJ-(3Q$!trU4U?@*Q#7RC4vaU+DJWv$t0 zeq&&~dy84JpAl8`K=vgNXO*I{zzk&>4+#dC-N%v)pk9l8!44kKH_i+fR8sCP&3Nk$ zad2uq!F)s-Ruii5seYs4s~%nQ52;lkT8jDrvGWC76wB6t0PM%s2v{$Kf7oxmN4a79 zh_n<{cTNZ3j~nwszG#BW0ix`^y+aMdc7W|qxRo$s>Uj)Cy;*VM*=b;5G#%G+&;W9G z_+suzV0tCuJ>af6aJ+PniXA8)n8q1K3VtFv3xF5(3J_K@#rn($L9oibkQP>^g;xF; zc;N^+>u=zfAcHHh@kA)xyEObYGxQvVM)H{T5%5(ObzQzAXmC)|9ALz;Kv^L0W8jzf zb%@d(*72adc8eV|n5~6TQzKg#s z7BK1)u5$R5gT*xE+jjLWnRdZTso)3k1KAdA!^M-7CAbdk2j&4LQL66i`ho717#EA! zg-|lVr0YqS!~y`7<(_yvL)R5nr;|t2MuT`Ws0*Jr9NGyJ0nHk|D=ZBRbIo+PG?Am^ zyFy}>C7ydiD4kxh@L;P7v_bKyQioLCFTBjk!_bKdQMBdxxB`iK+BF@j7|cE{1xf_P zWie2Unq0sIDUkm6GO8R~FS$ultM}<->lILo{qV~!7Mzc++LuDPp_qI>sYM5pm2ZT& zMew>5zQKr0K+v^2@9JM8*5n@cVVBj2gqCoR>M?+{Uh**qqEWNXxzqy+ZlC1f)Ebo8 zUihO%Xlyi5_8H!>+6@NUCGy?hX;C$#uWeXD@!DU3(5KKx&x=j=%QNtNP+9j2`-p}5W#!fozm{p&)u+3u|Jmx90xJLj diff --git a/samples/data/dnn/synset_words.txt b/samples/data/dnn/synset_words.txt deleted file mode 100644 index a9e8c7f50d..0000000000 --- a/samples/data/dnn/synset_words.txt +++ /dev/null @@ -1,1000 +0,0 @@ -n01440764 tench, Tinca tinca -n01443537 goldfish, Carassius auratus -n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias -n01491361 tiger shark, Galeocerdo cuvieri -n01494475 hammerhead, hammerhead shark -n01496331 electric ray, crampfish, numbfish, torpedo -n01498041 stingray -n01514668 cock -n01514859 hen -n01518878 ostrich, Struthio camelus -n01530575 brambling, Fringilla montifringilla -n01531178 goldfinch, Carduelis carduelis -n01532829 house finch, linnet, Carpodacus mexicanus -n01534433 junco, snowbird -n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea -n01558993 robin, American robin, Turdus migratorius -n01560419 bulbul -n01580077 jay -n01582220 magpie -n01592084 chickadee -n01601694 water ouzel, dipper -n01608432 kite -n01614925 bald eagle, American eagle, Haliaeetus leucocephalus -n01616318 vulture -n01622779 great grey owl, great gray owl, Strix nebulosa -n01629819 European fire salamander, Salamandra salamandra -n01630670 common newt, Triturus vulgaris -n01631663 eft -n01632458 spotted salamander, Ambystoma maculatum -n01632777 axolotl, mud puppy, Ambystoma mexicanum -n01641577 bullfrog, Rana catesbeiana -n01644373 tree frog, tree-frog -n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui -n01664065 loggerhead, loggerhead turtle, Caretta caretta -n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea -n01667114 mud turtle -n01667778 terrapin -n01669191 box turtle, box tortoise -n01675722 banded gecko -n01677366 common iguana, iguana, Iguana iguana -n01682714 American chameleon, anole, Anolis carolinensis -n01685808 whiptail, whiptail lizard -n01687978 agama -n01688243 frilled lizard, Chlamydosaurus kingi -n01689811 alligator lizard -n01692333 Gila monster, Heloderma suspectum -n01693334 green lizard, Lacerta viridis -n01694178 African chameleon, Chamaeleo chamaeleon -n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis -n01697457 African crocodile, Nile crocodile, Crocodylus niloticus -n01698640 American alligator, Alligator mississipiensis -n01704323 triceratops -n01728572 thunder snake, worm snake, Carphophis amoenus -n01728920 ringneck snake, ring-necked snake, ring snake -n01729322 hognose snake, puff adder, sand viper -n01729977 green snake, grass snake -n01734418 king snake, kingsnake -n01735189 garter snake, grass snake -n01737021 water snake -n01739381 vine snake -n01740131 night snake, Hypsiglena torquata -n01742172 boa constrictor, Constrictor constrictor -n01744401 rock python, rock snake, Python sebae -n01748264 Indian cobra, Naja naja -n01749939 green mamba -n01751748 sea snake -n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus -n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus -n01756291 sidewinder, horned rattlesnake, Crotalus cerastes -n01768244 trilobite -n01770081 harvestman, daddy longlegs, Phalangium opilio -n01770393 scorpion -n01773157 black and gold garden spider, Argiope aurantia -n01773549 barn spider, Araneus cavaticus -n01773797 garden spider, Aranea diademata -n01774384 black widow, Latrodectus mactans -n01774750 tarantula -n01775062 wolf spider, hunting spider -n01776313 tick -n01784675 centipede -n01795545 black grouse -n01796340 ptarmigan -n01797886 ruffed grouse, partridge, Bonasa umbellus -n01798484 prairie chicken, prairie grouse, prairie fowl -n01806143 peacock -n01806567 quail -n01807496 partridge -n01817953 African grey, African gray, Psittacus erithacus -n01818515 macaw -n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita -n01820546 lorikeet -n01824575 coucal -n01828970 bee eater -n01829413 hornbill -n01833805 hummingbird -n01843065 jacamar -n01843383 toucan -n01847000 drake -n01855032 red-breasted merganser, Mergus serrator -n01855672 goose -n01860187 black swan, Cygnus atratus -n01871265 tusker -n01872401 echidna, spiny anteater, anteater -n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus -n01877812 wallaby, brush kangaroo -n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus -n01883070 wombat -n01910747 jellyfish -n01914609 sea anemone, anemone -n01917289 brain coral -n01924916 flatworm, platyhelminth -n01930112 nematode, nematode worm, roundworm -n01943899 conch -n01944390 snail -n01945685 slug -n01950731 sea slug, nudibranch -n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore -n01968897 chambered nautilus, pearly nautilus, nautilus -n01978287 Dungeness crab, Cancer magister -n01978455 rock crab, Cancer irroratus -n01980166 fiddler crab -n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica -n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus -n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish -n01985128 crayfish, crawfish, crawdad, crawdaddy -n01986214 hermit crab -n01990800 isopod -n02002556 white stork, Ciconia ciconia -n02002724 black stork, Ciconia nigra -n02006656 spoonbill -n02007558 flamingo -n02009229 little blue heron, Egretta caerulea -n02009912 American egret, great white heron, Egretta albus -n02011460 bittern -n02012849 crane -n02013706 limpkin, Aramus pictus -n02017213 European gallinule, Porphyrio porphyrio -n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana -n02018795 bustard -n02025239 ruddy turnstone, Arenaria interpres -n02027492 red-backed sandpiper, dunlin, Erolia alpina -n02028035 redshank, Tringa totanus -n02033041 dowitcher -n02037110 oystercatcher, oyster catcher -n02051845 pelican -n02056570 king penguin, Aptenodytes patagonica -n02058221 albatross, mollymawk -n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus -n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca -n02074367 dugong, Dugong dugon -n02077923 sea lion -n02085620 Chihuahua -n02085782 Japanese spaniel -n02085936 Maltese dog, Maltese terrier, Maltese -n02086079 Pekinese, Pekingese, Peke -n02086240 Shih-Tzu -n02086646 Blenheim spaniel -n02086910 papillon -n02087046 toy terrier -n02087394 Rhodesian ridgeback -n02088094 Afghan hound, Afghan -n02088238 basset, basset hound -n02088364 beagle -n02088466 bloodhound, sleuthhound -n02088632 bluetick -n02089078 black-and-tan coonhound -n02089867 Walker hound, Walker foxhound -n02089973 English foxhound -n02090379 redbone -n02090622 borzoi, Russian wolfhound -n02090721 Irish wolfhound -n02091032 Italian greyhound -n02091134 whippet -n02091244 Ibizan hound, Ibizan Podenco -n02091467 Norwegian elkhound, elkhound -n02091635 otterhound, otter hound -n02091831 Saluki, gazelle hound -n02092002 Scottish deerhound, deerhound -n02092339 Weimaraner -n02093256 Staffordshire bullterrier, Staffordshire bull terrier -n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier -n02093647 Bedlington terrier -n02093754 Border terrier -n02093859 Kerry blue terrier -n02093991 Irish terrier -n02094114 Norfolk terrier -n02094258 Norwich terrier -n02094433 Yorkshire terrier -n02095314 wire-haired fox terrier -n02095570 Lakeland terrier -n02095889 Sealyham terrier, Sealyham -n02096051 Airedale, Airedale terrier -n02096177 cairn, cairn terrier -n02096294 Australian terrier -n02096437 Dandie Dinmont, Dandie Dinmont terrier -n02096585 Boston bull, Boston terrier -n02097047 miniature schnauzer -n02097130 giant schnauzer -n02097209 standard schnauzer -n02097298 Scotch terrier, Scottish terrier, Scottie -n02097474 Tibetan terrier, chrysanthemum dog -n02097658 silky terrier, Sydney silky -n02098105 soft-coated wheaten terrier -n02098286 West Highland white terrier -n02098413 Lhasa, Lhasa apso -n02099267 flat-coated retriever -n02099429 curly-coated retriever -n02099601 golden retriever -n02099712 Labrador retriever -n02099849 Chesapeake Bay retriever -n02100236 German short-haired pointer -n02100583 vizsla, Hungarian pointer -n02100735 English setter -n02100877 Irish setter, red setter -n02101006 Gordon setter -n02101388 Brittany spaniel -n02101556 clumber, clumber spaniel -n02102040 English springer, English springer spaniel -n02102177 Welsh springer spaniel -n02102318 cocker spaniel, English cocker spaniel, cocker -n02102480 Sussex spaniel -n02102973 Irish water spaniel -n02104029 kuvasz -n02104365 schipperke -n02105056 groenendael -n02105162 malinois -n02105251 briard -n02105412 kelpie -n02105505 komondor -n02105641 Old English sheepdog, bobtail -n02105855 Shetland sheepdog, Shetland sheep dog, Shetland -n02106030 collie -n02106166 Border collie -n02106382 Bouvier des Flandres, Bouviers des Flandres -n02106550 Rottweiler -n02106662 German shepherd, German shepherd dog, German police dog, alsatian -n02107142 Doberman, Doberman pinscher -n02107312 miniature pinscher -n02107574 Greater Swiss Mountain dog -n02107683 Bernese mountain dog -n02107908 Appenzeller -n02108000 EntleBucher -n02108089 boxer -n02108422 bull mastiff -n02108551 Tibetan mastiff -n02108915 French bulldog -n02109047 Great Dane -n02109525 Saint Bernard, St Bernard -n02109961 Eskimo dog, husky -n02110063 malamute, malemute, Alaskan malamute -n02110185 Siberian husky -n02110341 dalmatian, coach dog, carriage dog -n02110627 affenpinscher, monkey pinscher, monkey dog -n02110806 basenji -n02110958 pug, pug-dog -n02111129 Leonberg -n02111277 Newfoundland, Newfoundland dog -n02111500 Great Pyrenees -n02111889 Samoyed, Samoyede -n02112018 Pomeranian -n02112137 chow, chow chow -n02112350 keeshond -n02112706 Brabancon griffon -n02113023 Pembroke, Pembroke Welsh corgi -n02113186 Cardigan, Cardigan Welsh corgi -n02113624 toy poodle -n02113712 miniature poodle -n02113799 standard poodle -n02113978 Mexican hairless -n02114367 timber wolf, grey wolf, gray wolf, Canis lupus -n02114548 white wolf, Arctic wolf, Canis lupus tundrarum -n02114712 red wolf, maned wolf, Canis rufus, Canis niger -n02114855 coyote, prairie wolf, brush wolf, Canis latrans -n02115641 dingo, warrigal, warragal, Canis dingo -n02115913 dhole, Cuon alpinus -n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus -n02117135 hyena, hyaena -n02119022 red fox, Vulpes vulpes -n02119789 kit fox, Vulpes macrotis -n02120079 Arctic fox, white fox, Alopex lagopus -n02120505 grey fox, gray fox, Urocyon cinereoargenteus -n02123045 tabby, tabby cat -n02123159 tiger cat -n02123394 Persian cat -n02123597 Siamese cat, Siamese -n02124075 Egyptian cat -n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor -n02127052 lynx, catamount -n02128385 leopard, Panthera pardus -n02128757 snow leopard, ounce, Panthera uncia -n02128925 jaguar, panther, Panthera onca, Felis onca -n02129165 lion, king of beasts, Panthera leo -n02129604 tiger, Panthera tigris -n02130308 cheetah, chetah, Acinonyx jubatus -n02132136 brown bear, bruin, Ursus arctos -n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus -n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus -n02134418 sloth bear, Melursus ursinus, Ursus ursinus -n02137549 mongoose -n02138441 meerkat, mierkat -n02165105 tiger beetle -n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle -n02167151 ground beetle, carabid beetle -n02168699 long-horned beetle, longicorn, longicorn beetle -n02169497 leaf beetle, chrysomelid -n02172182 dung beetle -n02174001 rhinoceros beetle -n02177972 weevil -n02190166 fly -n02206856 bee -n02219486 ant, emmet, pismire -n02226429 grasshopper, hopper -n02229544 cricket -n02231487 walking stick, walkingstick, stick insect -n02233338 cockroach, roach -n02236044 mantis, mantid -n02256656 cicada, cicala -n02259212 leafhopper -n02264363 lacewing, lacewing fly -n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk -n02268853 damselfly -n02276258 admiral -n02277742 ringlet, ringlet butterfly -n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus -n02280649 cabbage butterfly -n02281406 sulphur butterfly, sulfur butterfly -n02281787 lycaenid, lycaenid butterfly -n02317335 starfish, sea star -n02319095 sea urchin -n02321529 sea cucumber, holothurian -n02325366 wood rabbit, cottontail, cottontail rabbit -n02326432 hare -n02328150 Angora, Angora rabbit -n02342885 hamster -n02346627 porcupine, hedgehog -n02356798 fox squirrel, eastern fox squirrel, Sciurus niger -n02361337 marmot -n02363005 beaver -n02364673 guinea pig, Cavia cobaya -n02389026 sorrel -n02391049 zebra -n02395406 hog, pig, grunter, squealer, Sus scrofa -n02396427 wild boar, boar, Sus scrofa -n02397096 warthog -n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius -n02403003 ox -n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis -n02410509 bison -n02412080 ram, tup -n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis -n02417914 ibex, Capra ibex -n02422106 hartebeest -n02422699 impala, Aepyceros melampus -n02423022 gazelle -n02437312 Arabian camel, dromedary, Camelus dromedarius -n02437616 llama -n02441942 weasel -n02442845 mink -n02443114 polecat, fitch, foulmart, foumart, Mustela putorius -n02443484 black-footed ferret, ferret, Mustela nigripes -n02444819 otter -n02445715 skunk, polecat, wood pussy -n02447366 badger -n02454379 armadillo -n02457408 three-toed sloth, ai, Bradypus tridactylus -n02480495 orangutan, orang, orangutang, Pongo pygmaeus -n02480855 gorilla, Gorilla gorilla -n02481823 chimpanzee, chimp, Pan troglodytes -n02483362 gibbon, Hylobates lar -n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus -n02484975 guenon, guenon monkey -n02486261 patas, hussar monkey, Erythrocebus patas -n02486410 baboon -n02487347 macaque -n02488291 langur -n02488702 colobus, colobus monkey -n02489166 proboscis monkey, Nasalis larvatus -n02490219 marmoset -n02492035 capuchin, ringtail, Cebus capucinus -n02492660 howler monkey, howler -n02493509 titi, titi monkey -n02493793 spider monkey, Ateles geoffroyi -n02494079 squirrel monkey, Saimiri sciureus -n02497673 Madagascar cat, ring-tailed lemur, Lemur catta -n02500267 indri, indris, Indri indri, Indri brevicaudatus -n02504013 Indian elephant, Elephas maximus -n02504458 African elephant, Loxodonta africana -n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens -n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca -n02514041 barracouta, snoek -n02526121 eel -n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch -n02606052 rock beauty, Holocanthus tricolor -n02607072 anemone fish -n02640242 sturgeon -n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus -n02643566 lionfish -n02655020 puffer, pufferfish, blowfish, globefish -n02666196 abacus -n02667093 abaya -n02669723 academic gown, academic robe, judge's robe -n02672831 accordion, piano accordion, squeeze box -n02676566 acoustic guitar -n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier -n02690373 airliner -n02692877 airship, dirigible -n02699494 altar -n02701002 ambulance -n02704792 amphibian, amphibious vehicle -n02708093 analog clock -n02727426 apiary, bee house -n02730930 apron -n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin -n02749479 assault rifle, assault gun -n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack -n02776631 bakery, bakeshop, bakehouse -n02777292 balance beam, beam -n02782093 balloon -n02783161 ballpoint, ballpoint pen, ballpen, Biro -n02786058 Band Aid -n02787622 banjo -n02788148 bannister, banister, balustrade, balusters, handrail -n02790996 barbell -n02791124 barber chair -n02791270 barbershop -n02793495 barn -n02794156 barometer -n02795169 barrel, cask -n02797295 barrow, garden cart, lawn cart, wheelbarrow -n02799071 baseball -n02802426 basketball -n02804414 bassinet -n02804610 bassoon -n02807133 bathing cap, swimming cap -n02808304 bath towel -n02808440 bathtub, bathing tub, bath, tub -n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon -n02814860 beacon, lighthouse, beacon light, pharos -n02815834 beaker -n02817516 bearskin, busby, shako -n02823428 beer bottle -n02823750 beer glass -n02825657 bell cote, bell cot -n02834397 bib -n02835271 bicycle-built-for-two, tandem bicycle, tandem -n02837789 bikini, two-piece -n02840245 binder, ring-binder -n02841315 binoculars, field glasses, opera glasses -n02843684 birdhouse -n02859443 boathouse -n02860847 bobsled, bobsleigh, bob -n02865351 bolo tie, bolo, bola tie, bola -n02869837 bonnet, poke bonnet -n02870880 bookcase -n02871525 bookshop, bookstore, bookstall -n02877765 bottlecap -n02879718 bow -n02883205 bow tie, bow-tie, bowtie -n02892201 brass, memorial tablet, plaque -n02892767 brassiere, bra, bandeau -n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty -n02895154 breastplate, aegis, egis -n02906734 broom -n02909870 bucket, pail -n02910353 buckle -n02916936 bulletproof vest -n02917067 bullet train, bullet -n02927161 butcher shop, meat market -n02930766 cab, hack, taxi, taxicab -n02939185 caldron, cauldron -n02948072 candle, taper, wax light -n02950826 cannon -n02951358 canoe -n02951585 can opener, tin opener -n02963159 cardigan -n02965783 car mirror -n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig -n02966687 carpenter's kit, tool kit -n02971356 carton -n02974003 car wheel -n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM -n02978881 cassette -n02979186 cassette player -n02980441 castle -n02981792 catamaran -n02988304 CD player -n02992211 cello, violoncello -n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone -n02999410 chain -n03000134 chainlink fence -n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour -n03000684 chain saw, chainsaw -n03014705 chest -n03016953 chiffonier, commode -n03017168 chime, bell, gong -n03018349 china cabinet, china closet -n03026506 Christmas stocking -n03028079 church, church building -n03032252 cinema, movie theater, movie theatre, movie house, picture palace -n03041632 cleaver, meat cleaver, chopper -n03042490 cliff dwelling -n03045698 cloak -n03047690 clog, geta, patten, sabot -n03062245 cocktail shaker -n03063599 coffee mug -n03063689 coffeepot -n03065424 coil, spiral, volute, whorl, helix -n03075370 combination lock -n03085013 computer keyboard, keypad -n03089624 confectionery, confectionary, candy store -n03095699 container ship, containership, container vessel -n03100240 convertible -n03109150 corkscrew, bottle screw -n03110669 cornet, horn, trumpet, trump -n03124043 cowboy boot -n03124170 cowboy hat, ten-gallon hat -n03125729 cradle -n03126707 crane -n03127747 crash helmet -n03127925 crate -n03131574 crib, cot -n03133878 Crock Pot -n03134739 croquet ball -n03141823 crutch -n03146219 cuirass -n03160309 dam, dike, dyke -n03179701 desk -n03180011 desktop computer -n03187595 dial telephone, dial phone -n03188531 diaper, nappy, napkin -n03196217 digital clock -n03197337 digital watch -n03201208 dining table, board -n03207743 dishrag, dishcloth -n03207941 dishwasher, dish washer, dishwashing machine -n03208938 disk brake, disc brake -n03216828 dock, dockage, docking facility -n03218198 dogsled, dog sled, dog sleigh -n03220513 dome -n03223299 doormat, welcome mat -n03240683 drilling platform, offshore rig -n03249569 drum, membranophone, tympan -n03250847 drumstick -n03255030 dumbbell -n03259280 Dutch oven -n03271574 electric fan, blower -n03272010 electric guitar -n03272562 electric locomotive -n03290653 entertainment center -n03291819 envelope -n03297495 espresso maker -n03314780 face powder -n03325584 feather boa, boa -n03337140 file, file cabinet, filing cabinet -n03344393 fireboat -n03345487 fire engine, fire truck -n03347037 fire screen, fireguard -n03355925 flagpole, flagstaff -n03372029 flute, transverse flute -n03376595 folding chair -n03379051 football helmet -n03384352 forklift -n03388043 fountain -n03388183 fountain pen -n03388549 four-poster -n03393912 freight car -n03394916 French horn, horn -n03400231 frying pan, frypan, skillet -n03404251 fur coat -n03417042 garbage truck, dustcart -n03424325 gasmask, respirator, gas helmet -n03425413 gas pump, gasoline pump, petrol pump, island dispenser -n03443371 goblet -n03444034 go-kart -n03445777 golf ball -n03445924 golfcart, golf cart -n03447447 gondola -n03447721 gong, tam-tam -n03450230 gown -n03452741 grand piano, grand -n03457902 greenhouse, nursery, glasshouse -n03459775 grille, radiator grille -n03461385 grocery store, grocery, food market, market -n03467068 guillotine -n03476684 hair slide -n03476991 hair spray -n03478589 half track -n03481172 hammer -n03482405 hamper -n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier -n03485407 hand-held computer, hand-held microcomputer -n03485794 handkerchief, hankie, hanky, hankey -n03492542 hard disc, hard disk, fixed disk -n03494278 harmonica, mouth organ, harp, mouth harp -n03495258 harp -n03496892 harvester, reaper -n03498962 hatchet -n03527444 holster -n03529860 home theater, home theatre -n03530642 honeycomb -n03532672 hook, claw -n03534580 hoopskirt, crinoline -n03535780 horizontal bar, high bar -n03538406 horse cart, horse-cart -n03544143 hourglass -n03584254 iPod -n03584829 iron, smoothing iron -n03590841 jack-o'-lantern -n03594734 jean, blue jean, denim -n03594945 jeep, landrover -n03595614 jersey, T-shirt, tee shirt -n03598930 jigsaw puzzle -n03599486 jinrikisha, ricksha, rickshaw -n03602883 joystick -n03617480 kimono -n03623198 knee pad -n03627232 knot -n03630383 lab coat, laboratory coat -n03633091 ladle -n03637318 lampshade, lamp shade -n03642806 laptop, laptop computer -n03649909 lawn mower, mower -n03657121 lens cap, lens cover -n03658185 letter opener, paper knife, paperknife -n03661043 library -n03662601 lifeboat -n03666591 lighter, light, igniter, ignitor -n03670208 limousine, limo -n03673027 liner, ocean liner -n03676483 lipstick, lip rouge -n03680355 Loafer -n03690938 lotion -n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system -n03692522 loupe, jeweler's loupe -n03697007 lumbermill, sawmill -n03706229 magnetic compass -n03709823 mailbag, postbag -n03710193 mailbox, letter box -n03710637 maillot -n03710721 maillot, tank suit -n03717622 manhole cover -n03720891 maraca -n03721384 marimba, xylophone -n03724870 mask -n03729826 matchstick -n03733131 maypole -n03733281 maze, labyrinth -n03733805 measuring cup -n03742115 medicine chest, medicine cabinet -n03743016 megalith, megalithic structure -n03759954 microphone, mike -n03761084 microwave, microwave oven -n03763968 military uniform -n03764736 milk can -n03769881 minibus -n03770439 miniskirt, mini -n03770679 minivan -n03773504 missile -n03775071 mitten -n03775546 mixing bowl -n03776460 mobile home, manufactured home -n03777568 Model T -n03777754 modem -n03781244 monastery -n03782006 monitor -n03785016 moped -n03786901 mortar -n03787032 mortarboard -n03788195 mosque -n03788365 mosquito net -n03791053 motor scooter, scooter -n03792782 mountain bike, all-terrain bike, off-roader -n03792972 mountain tent -n03793489 mouse, computer mouse -n03794056 mousetrap -n03796401 moving van -n03803284 muzzle -n03804744 nail -n03814639 neck brace -n03814906 necklace -n03825788 nipple -n03832673 notebook, notebook computer -n03837869 obelisk -n03838899 oboe, hautboy, hautbois -n03840681 ocarina, sweet potato -n03841143 odometer, hodometer, mileometer, milometer -n03843555 oil filter -n03854065 organ, pipe organ -n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO -n03866082 overskirt -n03868242 oxcart -n03868863 oxygen mask -n03871628 packet -n03873416 paddle, boat paddle -n03874293 paddlewheel, paddle wheel -n03874599 padlock -n03876231 paintbrush -n03877472 pajama, pyjama, pj's, jammies -n03877845 palace -n03884397 panpipe, pandean pipe, syrinx -n03887697 paper towel -n03888257 parachute, chute -n03888605 parallel bars, bars -n03891251 park bench -n03891332 parking meter -n03895866 passenger car, coach, carriage -n03899768 patio, terrace -n03902125 pay-phone, pay-station -n03903868 pedestal, plinth, footstall -n03908618 pencil box, pencil case -n03908714 pencil sharpener -n03916031 perfume, essence -n03920288 Petri dish -n03924679 photocopier -n03929660 pick, plectrum, plectron -n03929855 pickelhaube -n03930313 picket fence, paling -n03930630 pickup, pickup truck -n03933933 pier -n03935335 piggy bank, penny bank -n03937543 pill bottle -n03938244 pillow -n03942813 ping-pong ball -n03944341 pinwheel -n03947888 pirate, pirate ship -n03950228 pitcher, ewer -n03954731 plane, carpenter's plane, woodworking plane -n03956157 planetarium -n03958227 plastic bag -n03961711 plate rack -n03967562 plow, plough -n03970156 plunger, plumber's helper -n03976467 Polaroid camera, Polaroid Land camera -n03976657 pole -n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria -n03980874 poncho -n03982430 pool table, billiard table, snooker table -n03983396 pop bottle, soda bottle -n03991062 pot, flowerpot -n03992509 potter's wheel -n03995372 power drill -n03998194 prayer rug, prayer mat -n04004767 printer -n04005630 prison, prison house -n04008634 projectile, missile -n04009552 projector -n04019541 puck, hockey puck -n04023962 punching bag, punch bag, punching ball, punchball -n04026417 purse -n04033901 quill, quill pen -n04033995 quilt, comforter, comfort, puff -n04037443 racer, race car, racing car -n04039381 racket, racquet -n04040759 radiator -n04041544 radio, wireless -n04044716 radio telescope, radio reflector -n04049303 rain barrel -n04065272 recreational vehicle, RV, R.V. -n04067472 reel -n04069434 reflex camera -n04070727 refrigerator, icebox -n04074963 remote control, remote -n04081281 restaurant, eating house, eating place, eatery -n04086273 revolver, six-gun, six-shooter -n04090263 rifle -n04099969 rocking chair, rocker -n04111531 rotisserie -n04116512 rubber eraser, rubber, pencil eraser -n04118538 rugby ball -n04118776 rule, ruler -n04120489 running shoe -n04125021 safe -n04127249 safety pin -n04131690 saltshaker, salt shaker -n04133789 sandal -n04136333 sarong -n04141076 sax, saxophone -n04141327 scabbard -n04141975 scale, weighing machine -n04146614 school bus -n04147183 schooner -n04149813 scoreboard -n04152593 screen, CRT screen -n04153751 screw -n04154565 screwdriver -n04162706 seat belt, seatbelt -n04179913 sewing machine -n04192698 shield, buckler -n04200800 shoe shop, shoe-shop, shoe store -n04201297 shoji -n04204238 shopping basket -n04204347 shopping cart -n04208210 shovel -n04209133 shower cap -n04209239 shower curtain -n04228054 ski -n04229816 ski mask -n04235860 sleeping bag -n04238763 slide rule, slipstick -n04239074 sliding door -n04243546 slot, one-armed bandit -n04251144 snorkel -n04252077 snowmobile -n04252225 snowplow, snowplough -n04254120 soap dispenser -n04254680 soccer ball -n04254777 sock -n04258138 solar dish, solar collector, solar furnace -n04259630 sombrero -n04263257 soup bowl -n04264628 space bar -n04265275 space heater -n04266014 space shuttle -n04270147 spatula -n04273569 speedboat -n04275548 spider web, spider's web -n04277352 spindle -n04285008 sports car, sport car -n04286575 spotlight, spot -n04296562 stage -n04310018 steam locomotive -n04311004 steel arch bridge -n04311174 steel drum -n04317175 stethoscope -n04325704 stole -n04326547 stone wall -n04328186 stopwatch, stop watch -n04330267 stove -n04332243 strainer -n04335435 streetcar, tram, tramcar, trolley, trolley car -n04336792 stretcher -n04344873 studio couch, day bed -n04346328 stupa, tope -n04347754 submarine, pigboat, sub, U-boat -n04350905 suit, suit of clothes -n04355338 sundial -n04355933 sunglass -n04356056 sunglasses, dark glasses, shades -n04357314 sunscreen, sunblock, sun blocker -n04366367 suspension bridge -n04367480 swab, swob, mop -n04370456 sweatshirt -n04371430 swimming trunks, bathing trunks -n04371774 swing -n04372370 switch, electric switch, electrical switch -n04376876 syringe -n04380533 table lamp -n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle -n04392985 tape player -n04398044 teapot -n04399382 teddy, teddy bear -n04404412 television, television system -n04409515 tennis ball -n04417672 thatch, thatched roof -n04418357 theater curtain, theatre curtain -n04423845 thimble -n04428191 thresher, thrasher, threshing machine -n04429376 throne -n04435653 tile roof -n04442312 toaster -n04443257 tobacco shop, tobacconist shop, tobacconist -n04447861 toilet seat -n04456115 torch -n04458633 totem pole -n04461696 tow truck, tow car, wrecker -n04462240 toyshop -n04465501 tractor -n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi -n04476259 tray -n04479046 trench coat -n04482393 tricycle, trike, velocipede -n04483307 trimaran -n04485082 tripod -n04486054 triumphal arch -n04487081 trolleybus, trolley coach, trackless trolley -n04487394 trombone -n04493381 tub, vat -n04501370 turnstile -n04505470 typewriter keyboard -n04507155 umbrella -n04509417 unicycle, monocycle -n04515003 upright, upright piano -n04517823 vacuum, vacuum cleaner -n04522168 vase -n04523525 vault -n04525038 velvet -n04525305 vending machine -n04532106 vestment -n04532670 viaduct -n04536866 violin, fiddle -n04540053 volleyball -n04542943 waffle iron -n04548280 wall clock -n04548362 wallet, billfold, notecase, pocketbook -n04550184 wardrobe, closet, press -n04552348 warplane, military plane -n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin -n04554684 washer, automatic washer, washing machine -n04557648 water bottle -n04560804 water jug -n04562935 water tower -n04579145 whiskey jug -n04579432 whistle -n04584207 wig -n04589890 window screen -n04590129 window shade -n04591157 Windsor tie -n04591713 wine bottle -n04592741 wing -n04596742 wok -n04597913 wooden spoon -n04599235 wool, woolen, woollen -n04604644 worm fence, snake fence, snake-rail fence, Virginia fence -n04606251 wreck -n04612504 yawl -n04613696 yurt -n06359193 web site, website, internet site, site -n06596364 comic book -n06785654 crossword puzzle, crossword -n06794110 street sign -n06874185 traffic light, traffic signal, stoplight -n07248320 book jacket, dust cover, dust jacket, dust wrapper -n07565083 menu -n07579787 plate -n07583066 guacamole -n07584110 consomme -n07590611 hot pot, hotpot -n07613480 trifle -n07614500 ice cream, icecream -n07615774 ice lolly, lolly, lollipop, popsicle -n07684084 French loaf -n07693725 bagel, beigel -n07695742 pretzel -n07697313 cheeseburger -n07697537 hotdog, hot dog, red hot -n07711569 mashed potato -n07714571 head cabbage -n07714990 broccoli -n07715103 cauliflower -n07716358 zucchini, courgette -n07716906 spaghetti squash -n07717410 acorn squash -n07717556 butternut squash -n07718472 cucumber, cuke -n07718747 artichoke, globe artichoke -n07720875 bell pepper -n07730033 cardoon -n07734744 mushroom -n07742313 Granny Smith -n07745940 strawberry -n07747607 orange -n07749582 lemon -n07753113 fig -n07753275 pineapple, ananas -n07753592 banana -n07754684 jackfruit, jak, jack -n07760859 custard apple -n07768694 pomegranate -n07802026 hay -n07831146 carbonara -n07836838 chocolate sauce, chocolate syrup -n07860988 dough -n07871810 meat loaf, meatloaf -n07873807 pizza, pizza pie -n07875152 potpie -n07880968 burrito -n07892512 red wine -n07920052 espresso -n07930864 cup -n07932039 eggnog -n09193705 alp -n09229709 bubble -n09246464 cliff, drop, drop-off -n09256479 coral reef -n09288635 geyser -n09332890 lakeside, lakeshore -n09399592 promontory, headland, head, foreland -n09421951 sandbar, sand bar -n09428293 seashore, coast, seacoast, sea-coast -n09468604 valley, vale -n09472597 volcano -n09835506 ballplayer, baseball player -n10148035 groom, bridegroom -n10565667 scuba diver -n11879895 rapeseed -n11939491 daisy -n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum -n12144580 corn -n12267677 acorn -n12620546 hip, rose hip, rosehip -n12768682 buckeye, horse chestnut, conker -n12985857 coral fungus -n12998815 agaric -n13037406 gyromitra -n13040303 stinkhorn, carrion fungus -n13044778 earthstar -n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa -n13054560 bolete -n13133613 ear, spike, capitulum -n15075141 toilet tissue, toilet paper, bathroom tissue diff --git a/samples/dnn/README.md b/samples/dnn/README.md index 3a7523b4b1..07c04ecfa4 100644 --- a/samples/dnn/README.md +++ b/samples/dnn/README.md @@ -14,6 +14,14 @@ | [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801, 115.9465, 122.7717` | BGR | | [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR | + +### Classification +| Model | Scale | Size WxH| Mean subtraction | Channels order | +|---------------|-------|-----------|--------------------|-------| +| GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR | +| [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR | + + ## References * [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py) * [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn) diff --git a/samples/dnn/caffe_googlenet.cpp b/samples/dnn/caffe_googlenet.cpp deleted file mode 100644 index 08195ef2f6..0000000000 --- a/samples/dnn/caffe_googlenet.cpp +++ /dev/null @@ -1,181 +0,0 @@ -/**M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ -#include -#include -#include -#include -using namespace cv; -using namespace cv::dnn; - -#include -#include -#include -using namespace std; - -/* Find best class for the blob (i. e. class with maximal probability) */ -static void getMaxClass(const Mat &probBlob, int *classId, double *classProb) -{ - Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix - Point classNumber; - - minMaxLoc(probMat, NULL, classProb, NULL, &classNumber); - *classId = classNumber.x; -} - -static std::vector readClassNames(const char *filename ) -{ - std::vector classNames; - - std::ifstream fp(filename); - if (!fp.is_open()) - { - std::cerr << "File with classes labels not found: " << filename << std::endl; - exit(-1); - } - - std::string name; - while (!fp.eof()) - { - std::getline(fp, name); - if (name.length()) - classNames.push_back( name.substr(name.find(' ')+1) ); - } - - fp.close(); - return classNames; -} - -const char* params - = "{ help | false | Sample app for loading googlenet model }" - "{ proto | bvlc_googlenet.prototxt | model configuration }" - "{ model | bvlc_googlenet.caffemodel | model weights }" - "{ label | synset_words.txt | names of ILSVRC2012 classes }" - "{ image | space_shuttle.jpg | path to image file }" - "{ opencl | false | enable OpenCL }" -; - -int main(int argc, char **argv) -{ - CV_TRACE_FUNCTION(); - - CommandLineParser parser(argc, argv, params); - - if (parser.get("help")) - { - parser.printMessage(); - return 0; - } - - String modelTxt = parser.get("proto"); - String modelBin = parser.get("model"); - String imageFile = parser.get("image"); - String classNameFile = parser.get("label"); - - Net net; - try { - //! [Read and initialize network] - net = dnn::readNetFromCaffe(modelTxt, modelBin); - //! [Read and initialize network] - } - catch (const cv::Exception& e) { - std::cerr << "Exception: " << e.what() << std::endl; - //! [Check that network was read successfully] - if (net.empty()) - { - std::cerr << "Can't load network by using the following files: " << std::endl; - std::cerr << "prototxt: " << modelTxt << std::endl; - std::cerr << "caffemodel: " << modelBin << std::endl; - std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl; - std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl; - exit(-1); - } - //! [Check that network was read successfully] - } - - if (parser.get("opencl")) - { - net.setPreferableTarget(DNN_TARGET_OPENCL); - } - - //! [Prepare blob] - Mat img = imread(imageFile); - if (img.empty()) - { - std::cerr << "Can't read image from the file: " << imageFile << std::endl; - exit(-1); - } - - //GoogLeNet accepts only 224x224 BGR-images - Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), - Scalar(104, 117, 123), false); //Convert Mat to batch of images - //! [Prepare blob] - net.setInput(inputBlob, "data"); //set the network input - Mat prob = net.forward("prob"); //compute output - - cv::TickMeter t; - for (int i = 0; i < 10; i++) - { - CV_TRACE_REGION("forward"); - //! [Set input blob] - net.setInput(inputBlob, "data"); //set the network input - //! [Set input blob] - t.start(); - //! [Make forward pass] - prob = net.forward("prob"); //compute output - //! [Make forward pass] - t.stop(); - } - - //! [Gather output] - int classId; - double classProb; - getMaxClass(prob, &classId, &classProb);//find the best class - //! [Gather output] - - //! [Print results] - std::vector classNames = readClassNames(classNameFile.c_str()); - std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl; - std::cout << "Probability: " << classProb * 100 << "%" << std::endl; - //! [Print results] - std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl; - - return 0; -} //main diff --git a/samples/dnn/classification.cpp b/samples/dnn/classification.cpp new file mode 100644 index 0000000000..74b72a43a3 --- /dev/null +++ b/samples/dnn/classification.cpp @@ -0,0 +1,153 @@ +#include +#include +#include + +#include +#include + +const char* keys = + "{ help h | | Print help message. }" + "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}" + "{ model m | | Path to a binary file of model contains trained weights. " + "It could be a file with extensions .caffemodel (Caffe), " + ".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }" + "{ config c | | Path to a text file of model contains network configuration. " + "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }" + "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }" + "{ classes | | Optional path to a text file with names of classes. }" + "{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }" + "{ scale | 1 | Preprocess input image by multiplying on a scale factor. }" + "{ width | -1 | Preprocess input image by resizing to a specific width. }" + "{ height | -1 | Preprocess input image by resizing to a specific height. }" + "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }" + "{ backend | 0 | Choose one of computation backends: " + "0: default C++ backend, " + "1: Halide language (http://halide-lang.org/), " + "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}" + "{ target | 0 | Choose one of target computation devices: " + "0: CPU target (by default)," + "1: OpenCL }"; + +using namespace cv; +using namespace dnn; + +std::vector classes; + +Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = ""); + +int main(int argc, char** argv) +{ + CommandLineParser parser(argc, argv, keys); + parser.about("Use this script to run classification deep learning networks using OpenCV."); + if (argc == 1 || parser.has("help")) + { + parser.printMessage(); + return 0; + } + + float scale = parser.get("scale"); + bool swapRB = parser.get("rgb"); + int inpWidth = parser.get("width"); + int inpHeight = parser.get("height"); + + // Parse mean values. + Scalar mean; + if (parser.has("mean")) + { + std::istringstream meanStr(parser.get("mean")); + std::vector meanValues; + float val; + while (meanStr >> val) + meanValues.push_back(val); + CV_Assert(meanValues.size() == 3); + mean = Scalar(meanValues[0], meanValues[1], meanValues[2]); + } + + // Open file with classes names. + if (parser.has("classes")) + { + std::string file = parser.get("classes"); + std::ifstream ifs(file.c_str()); + if (!ifs.is_open()) + CV_Error(Error::StsError, "File " + file + " not found"); + std::string line; + while (std::getline(ifs, line)) + { + classes.push_back(line); + } + } + + // Load a model. + CV_Assert(parser.has("model")); + Net net = readNet(parser.get("model"), parser.get("config"), parser.get("framework")); + net.setPreferableBackend(parser.get("backend")); + net.setPreferableTarget(parser.get("target")); + + // Create a window + static const std::string kWinName = "Deep learning image classification in OpenCV"; + namedWindow(kWinName, WINDOW_NORMAL); + + // Open a video file or an image file or a camera stream. + VideoCapture cap; + if (parser.has("input")) + cap.open(parser.get("input")); + else + cap.open(0); + + // Process frames. + Mat frame, blob; + while (waitKey(1) < 0) + { + cap >> frame; + if (frame.empty()) + { + waitKey(); + break; + } + + // Create a 4D blob from a frame. + blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false); + + // Run a model. + net.setInput(blob); + Mat out = net.forward(); + out = out.reshape(1, 1); + + // Get a class with a highest score. + Point classIdPoint; + double confidence; + minMaxLoc(out, 0, &confidence, 0, &classIdPoint); + int classId = classIdPoint.x; + + // Put efficiency information. + std::vector layersTimes; + double t = net.getPerfProfile(layersTimes); + std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency()); + putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); + + // Print predicted class. + label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() : + classes[classId].c_str()), + confidence); + putText(frame, label, Point(0, 40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); + + imshow(kWinName, frame); + } + return 0; +} + +Net readNet(const std::string& model, const std::string& config, const std::string& framework) +{ + std::string modelExt = model.substr(model.rfind('.')); + if (framework == "caffe" || modelExt == ".caffemodel") + return readNetFromCaffe(config, model); + else if (framework == "tensorflow" || modelExt == ".pb") + return readNetFromTensorflow(model, config); + else if (framework == "torch" || modelExt == ".t7" || modelExt == ".net") + return readNetFromTorch(model); + else if (framework == "darknet" || modelExt == ".weights") + return readNetFromDarknet(config, model); + else + CV_Error(Error::StsError, "Cannot determine an origin framework of model from file " + model); + return Net(); +} diff --git a/samples/dnn/classification.py b/samples/dnn/classification.py new file mode 100644 index 0000000000..446c9b0577 --- /dev/null +++ b/samples/dnn/classification.py @@ -0,0 +1,98 @@ +import cv2 as cv +import argparse +import numpy as np +import sys + +backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) +targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL) + +parser = argparse.ArgumentParser(description='Use this script to run classification deep learning networks using OpenCV.') +parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.') +parser.add_argument('--model', required=True, + help='Path to a binary file of model contains trained weights. ' + 'It could be a file with extensions .caffemodel (Caffe), ' + '.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet)') +parser.add_argument('--config', + help='Path to a text file of model contains network configuration. ' + 'It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet)') +parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'], + help='Optional name of an origin framework of the model. ' + 'Detect it automatically if it does not set.') +parser.add_argument('--classes', help='Optional path to a text file with names of classes.') +parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0], + help='Preprocess input image by subtracting mean values. ' + 'Mean values should be in BGR order.') +parser.add_argument('--scale', type=float, default=1.0, + help='Preprocess input image by multiplying on a scale factor.') +parser.add_argument('--width', type=int, required=True, + help='Preprocess input image by resizing to a specific width.') +parser.add_argument('--height', type=int, required=True, + help='Preprocess input image by resizing to a specific height.') +parser.add_argument('--rgb', action='store_true', + help='Indicate that model works with RGB input images instead BGR ones.') +parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int, + help="Choose one of computation backends: " + "%d: default C++ backend, " + "%d: Halide language (http://halide-lang.org/), " + "%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends) +parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int, + help='Choose one of target computation devices: ' + '%d: CPU target (by default), ' + '%d: OpenCL' % targets) +args = parser.parse_args() + +# Load names of classes +classes = None +if args.classes: + with open(args.classes, 'rt') as f: + classes = f.read().rstrip('\n').split('\n') + +# Load a network +modelExt = args.model[args.model.rfind('.'):] +if args.framework == 'caffe' or modelExt == '.caffemodel': + net = cv.dnn.readNetFromCaffe(args.config, args.model) +elif args.framework == 'tensorflow' or modelExt == '.pb': + net = cv.dnn.readNetFromTensorflow(args.model, args.config) +elif args.framework == 'torch' or modelExt in ['.t7', '.net']: + net = cv.dnn.readNetFromTorch(args.model) +elif args.framework == 'darknet' or modelExt == '.weights': + net = cv.dnn.readNetFromDarknet(args.config, args.model) +else: + print('Cannot determine an origin framework of model from file %s' % args.model) + sys.exit(0) + +net.setPreferableBackend(args.backend) +net.setPreferableTarget(args.target) + +winName = 'Deep learning image classification in OpenCV' +cv.namedWindow(winName, cv.WINDOW_NORMAL) + +cap = cv.VideoCapture(args.input if args.input else 0) +while cv.waitKey(1) < 0: + hasFrame, frame = cap.read() + if not hasFrame: + cv.waitKey() + break + + # Create a 4D blob from a frame. + blob = cv.dnn.blobFromImage(frame, args.scale, (args.width, args.height), args.mean, args.rgb, crop=False) + + # Run a model + net.setInput(blob) + out = net.forward() + + # Get a class with a highest score. + out = out.flatten() + classId = np.argmax(out) + confidence = out[classId] + + # Put efficiency information. + t, _ = net.getPerfProfile() + label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency()) + cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) + + # Print predicted class. + label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence) + cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) + + cv.imshow(winName, frame) diff --git a/samples/dnn/classification_classes_ILSVRC2012.txt b/samples/dnn/classification_classes_ILSVRC2012.txt new file mode 100644 index 0000000000..a509c00748 --- /dev/null +++ b/samples/dnn/classification_classes_ILSVRC2012.txt @@ -0,0 +1,1000 @@ +tench, Tinca tinca +goldfish, Carassius auratus +great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +tiger shark, Galeocerdo cuvieri +hammerhead, hammerhead shark +electric ray, crampfish, numbfish, torpedo +stingray +cock +hen +ostrich, Struthio camelus +brambling, Fringilla montifringilla +goldfinch, Carduelis carduelis +house finch, linnet, Carpodacus mexicanus +junco, snowbird +indigo bunting, indigo finch, indigo bird, Passerina cyanea +robin, American robin, Turdus migratorius +bulbul +jay +magpie +chickadee +water ouzel, dipper +kite +bald eagle, American eagle, Haliaeetus leucocephalus +vulture +great grey owl, great gray owl, Strix nebulosa +European fire salamander, Salamandra salamandra +common newt, Triturus vulgaris +eft +spotted salamander, Ambystoma maculatum +axolotl, mud puppy, Ambystoma mexicanum +bullfrog, Rana catesbeiana +tree frog, tree-frog +tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +loggerhead, loggerhead turtle, Caretta caretta +leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +mud turtle +terrapin +box turtle, box tortoise +banded gecko +common iguana, iguana, Iguana iguana +American chameleon, anole, Anolis carolinensis +whiptail, whiptail lizard +agama +frilled lizard, Chlamydosaurus kingi +alligator lizard +Gila monster, Heloderma suspectum +green lizard, Lacerta viridis +African chameleon, Chamaeleo chamaeleon +Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +African crocodile, Nile crocodile, Crocodylus niloticus +American alligator, Alligator mississipiensis +triceratops +thunder snake, worm snake, Carphophis amoenus +ringneck snake, ring-necked snake, ring snake +hognose snake, puff adder, sand viper +green snake, grass snake +king snake, kingsnake +garter snake, grass snake +water snake +vine snake +night snake, Hypsiglena torquata +boa constrictor, Constrictor constrictor +rock python, rock snake, Python sebae +Indian cobra, Naja naja +green mamba +sea snake +horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +diamondback, diamondback rattlesnake, Crotalus adamanteus +sidewinder, horned rattlesnake, Crotalus cerastes +trilobite +harvestman, daddy longlegs, Phalangium opilio +scorpion +black and gold garden spider, Argiope aurantia +barn spider, Araneus cavaticus +garden spider, Aranea diademata +black widow, Latrodectus mactans +tarantula +wolf spider, hunting spider +tick +centipede +black grouse +ptarmigan +ruffed grouse, partridge, Bonasa umbellus +prairie chicken, prairie grouse, prairie fowl +peacock +quail +partridge +African grey, African gray, Psittacus erithacus +macaw +sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +lorikeet +coucal +bee eater +hornbill +hummingbird +jacamar +toucan +drake +red-breasted merganser, Mergus serrator +goose +black swan, Cygnus atratus +tusker +echidna, spiny anteater, anteater +platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +wallaby, brush kangaroo +koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +wombat +jellyfish +sea anemone, anemone +brain coral +flatworm, platyhelminth +nematode, nematode worm, roundworm +conch +snail +slug +sea slug, nudibranch +chiton, coat-of-mail shell, sea cradle, polyplacophore +chambered nautilus, pearly nautilus, nautilus +Dungeness crab, Cancer magister +rock crab, Cancer irroratus +fiddler crab +king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +American lobster, Northern lobster, Maine lobster, Homarus americanus +spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +crayfish, crawfish, crawdad, crawdaddy +hermit crab +isopod +white stork, Ciconia ciconia +black stork, Ciconia nigra +spoonbill +flamingo +little blue heron, Egretta caerulea +American egret, great white heron, Egretta albus +bittern +crane +limpkin, Aramus pictus +European gallinule, Porphyrio porphyrio +American coot, marsh hen, mud hen, water hen, Fulica americana +bustard +ruddy turnstone, Arenaria interpres +red-backed sandpiper, dunlin, Erolia alpina +redshank, Tringa totanus +dowitcher +oystercatcher, oyster catcher +pelican +king penguin, Aptenodytes patagonica +albatross, mollymawk +grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +killer whale, killer, orca, grampus, sea wolf, Orcinus orca +dugong, Dugong dugon +sea lion +Chihuahua +Japanese spaniel +Maltese dog, Maltese terrier, Maltese +Pekinese, Pekingese, Peke +Shih-Tzu +Blenheim spaniel +papillon +toy terrier +Rhodesian ridgeback +Afghan hound, Afghan +basset, basset hound +beagle +bloodhound, sleuthhound +bluetick +black-and-tan coonhound +Walker hound, Walker foxhound +English foxhound +redbone +borzoi, Russian wolfhound +Irish wolfhound +Italian greyhound +whippet +Ibizan hound, Ibizan Podenco +Norwegian elkhound, elkhound +otterhound, otter hound +Saluki, gazelle hound +Scottish deerhound, deerhound +Weimaraner +Staffordshire bullterrier, Staffordshire bull terrier +American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +Bedlington terrier +Border terrier +Kerry blue terrier +Irish terrier +Norfolk terrier +Norwich terrier +Yorkshire terrier +wire-haired fox terrier +Lakeland terrier +Sealyham terrier, Sealyham +Airedale, Airedale terrier +cairn, cairn terrier +Australian terrier +Dandie Dinmont, Dandie Dinmont terrier +Boston bull, Boston terrier +miniature schnauzer +giant schnauzer +standard schnauzer +Scotch terrier, Scottish terrier, Scottie +Tibetan terrier, chrysanthemum dog +silky terrier, Sydney silky +soft-coated wheaten terrier +West Highland white terrier +Lhasa, Lhasa apso +flat-coated retriever +curly-coated retriever +golden retriever +Labrador retriever +Chesapeake Bay retriever +German short-haired pointer +vizsla, Hungarian pointer +English setter +Irish setter, red setter +Gordon setter +Brittany spaniel +clumber, clumber spaniel +English springer, English springer spaniel +Welsh springer spaniel +cocker spaniel, English cocker spaniel, cocker +Sussex spaniel +Irish water spaniel +kuvasz +schipperke +groenendael +malinois +briard +kelpie +komondor +Old English sheepdog, bobtail +Shetland sheepdog, Shetland sheep dog, Shetland +collie +Border collie +Bouvier des Flandres, Bouviers des Flandres +Rottweiler +German shepherd, German shepherd dog, German police dog, alsatian +Doberman, Doberman pinscher +miniature pinscher +Greater Swiss Mountain dog +Bernese mountain dog +Appenzeller +EntleBucher +boxer +bull mastiff +Tibetan mastiff +French bulldog +Great Dane +Saint Bernard, St Bernard +Eskimo dog, husky +malamute, malemute, Alaskan malamute +Siberian husky +dalmatian, coach dog, carriage dog +affenpinscher, monkey pinscher, monkey dog +basenji +pug, pug-dog +Leonberg +Newfoundland, Newfoundland dog +Great Pyrenees +Samoyed, Samoyede +Pomeranian +chow, chow chow +keeshond +Brabancon griffon +Pembroke, Pembroke Welsh corgi +Cardigan, Cardigan Welsh corgi +toy poodle +miniature poodle +standard poodle +Mexican hairless +timber wolf, grey wolf, gray wolf, Canis lupus +white wolf, Arctic wolf, Canis lupus tundrarum +red wolf, maned wolf, Canis rufus, Canis niger +coyote, prairie wolf, brush wolf, Canis latrans +dingo, warrigal, warragal, Canis dingo +dhole, Cuon alpinus +African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +hyena, hyaena +red fox, Vulpes vulpes +kit fox, Vulpes macrotis +Arctic fox, white fox, Alopex lagopus +grey fox, gray fox, Urocyon cinereoargenteus +tabby, tabby cat +tiger cat +Persian cat +Siamese cat, Siamese +Egyptian cat +cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +lynx, catamount +leopard, Panthera pardus +snow leopard, ounce, Panthera uncia +jaguar, panther, Panthera onca, Felis onca +lion, king of beasts, Panthera leo +tiger, Panthera tigris +cheetah, chetah, Acinonyx jubatus +brown bear, bruin, Ursus arctos +American black bear, black bear, Ursus americanus, Euarctos americanus +ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +sloth bear, Melursus ursinus, Ursus ursinus +mongoose +meerkat, mierkat +tiger beetle +ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +ground beetle, carabid beetle +long-horned beetle, longicorn, longicorn beetle +leaf beetle, chrysomelid +dung beetle +rhinoceros beetle +weevil +fly +bee +ant, emmet, pismire +grasshopper, hopper +cricket +walking stick, walkingstick, stick insect +cockroach, roach +mantis, mantid +cicada, cicala +leafhopper +lacewing, lacewing fly +dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +damselfly +admiral +ringlet, ringlet butterfly +monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +cabbage butterfly +sulphur butterfly, sulfur butterfly +lycaenid, lycaenid butterfly +starfish, sea star +sea urchin +sea cucumber, holothurian +wood rabbit, cottontail, cottontail rabbit +hare +Angora, Angora rabbit +hamster +porcupine, hedgehog +fox squirrel, eastern fox squirrel, Sciurus niger +marmot +beaver +guinea pig, Cavia cobaya +sorrel +zebra +hog, pig, grunter, squealer, Sus scrofa +wild boar, boar, Sus scrofa +warthog +hippopotamus, hippo, river horse, Hippopotamus amphibius +ox +water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +bison +ram, tup +bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +ibex, Capra ibex +hartebeest +impala, Aepyceros melampus +gazelle +Arabian camel, dromedary, Camelus dromedarius +llama +weasel +mink +polecat, fitch, foulmart, foumart, Mustela putorius +black-footed ferret, ferret, Mustela nigripes +otter +skunk, polecat, wood pussy +badger +armadillo +three-toed sloth, ai, Bradypus tridactylus +orangutan, orang, orangutang, Pongo pygmaeus +gorilla, Gorilla gorilla +chimpanzee, chimp, Pan troglodytes +gibbon, Hylobates lar +siamang, Hylobates syndactylus, Symphalangus syndactylus +guenon, guenon monkey +patas, hussar monkey, Erythrocebus patas +baboon +macaque +langur +colobus, colobus monkey +proboscis monkey, Nasalis larvatus +marmoset +capuchin, ringtail, Cebus capucinus +howler monkey, howler +titi, titi monkey +spider monkey, Ateles geoffroyi +squirrel monkey, Saimiri sciureus +Madagascar cat, ring-tailed lemur, Lemur catta +indri, indris, Indri indri, Indri brevicaudatus +Indian elephant, Elephas maximus +African elephant, Loxodonta africana +lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +barracouta, snoek +eel +coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +rock beauty, Holocanthus tricolor +anemone fish +sturgeon +gar, garfish, garpike, billfish, Lepisosteus osseus +lionfish +puffer, pufferfish, blowfish, globefish +abacus +abaya +academic gown, academic robe, judge's robe +accordion, piano accordion, squeeze box +acoustic guitar +aircraft carrier, carrier, flattop, attack aircraft carrier +airliner +airship, dirigible +altar +ambulance +amphibian, amphibious vehicle +analog clock +apiary, bee house +apron +ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +assault rifle, assault gun +backpack, back pack, knapsack, packsack, rucksack, haversack +bakery, bakeshop, bakehouse +balance beam, beam +balloon +ballpoint, ballpoint pen, ballpen, Biro +Band Aid +banjo +bannister, banister, balustrade, balusters, handrail +barbell +barber chair +barbershop +barn +barometer +barrel, cask +barrow, garden cart, lawn cart, wheelbarrow +baseball +basketball +bassinet +bassoon +bathing cap, swimming cap +bath towel +bathtub, bathing tub, bath, tub +beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +beacon, lighthouse, beacon light, pharos +beaker +bearskin, busby, shako +beer bottle +beer glass +bell cote, bell cot +bib +bicycle-built-for-two, tandem bicycle, tandem +bikini, two-piece +binder, ring-binder +binoculars, field glasses, opera glasses +birdhouse +boathouse +bobsled, bobsleigh, bob +bolo tie, bolo, bola tie, bola +bonnet, poke bonnet +bookcase +bookshop, bookstore, bookstall +bottlecap +bow +bow tie, bow-tie, bowtie +brass, memorial tablet, plaque +brassiere, bra, bandeau +breakwater, groin, groyne, mole, bulwark, seawall, jetty +breastplate, aegis, egis +broom +bucket, pail +buckle +bulletproof vest +bullet train, bullet +butcher shop, meat market +cab, hack, taxi, taxicab +caldron, cauldron +candle, taper, wax light +cannon +canoe +can opener, tin opener +cardigan +car mirror +carousel, carrousel, merry-go-round, roundabout, whirligig +carpenter's kit, tool kit +carton +car wheel +cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +cassette +cassette player +castle +catamaran +CD player +cello, violoncello +cellular telephone, cellular phone, cellphone, cell, mobile phone +chain +chainlink fence +chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +chain saw, chainsaw +chest +chiffonier, commode +chime, bell, gong +china cabinet, china closet +Christmas stocking +church, church building +cinema, movie theater, movie theatre, movie house, picture palace +cleaver, meat cleaver, chopper +cliff dwelling +cloak +clog, geta, patten, sabot +cocktail shaker +coffee mug +coffeepot +coil, spiral, volute, whorl, helix +combination lock +computer keyboard, keypad +confectionery, confectionary, candy store +container ship, containership, container vessel +convertible +corkscrew, bottle screw +cornet, horn, trumpet, trump +cowboy boot +cowboy hat, ten-gallon hat +cradle +crane +crash helmet +crate +crib, cot +Crock Pot +croquet ball +crutch +cuirass +dam, dike, dyke +desk +desktop computer +dial telephone, dial phone +diaper, nappy, napkin +digital clock +digital watch +dining table, board +dishrag, dishcloth +dishwasher, dish washer, dishwashing machine +disk brake, disc brake +dock, dockage, docking facility +dogsled, dog sled, dog sleigh +dome +doormat, welcome mat +drilling platform, offshore rig +drum, membranophone, tympan +drumstick +dumbbell +Dutch oven +electric fan, blower +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa, boa +file, file cabinet, filing cabinet +fireboat +fire engine, fire truck +fire screen, fireguard +flagpole, flagstaff +flute, transverse flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn, horn +frying pan, frypan, skillet +fur coat +garbage truck, dustcart +gasmask, respirator, gas helmet +gas pump, gasoline pump, petrol pump, island dispenser +goblet +go-kart +golf ball +golfcart, golf cart +gondola +gong, tam-tam +gown +grand piano, grand +greenhouse, nursery, glasshouse +grille, radiator grille +grocery store, grocery, food market, market +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower, blow dryer, blow drier, hair dryer, hair drier +hand-held computer, hand-held microcomputer +handkerchief, hankie, hanky, hankey +hard disc, hard disk, fixed disk +harmonica, mouth organ, harp, mouth harp +harp +harvester, reaper +hatchet +holster +home theater, home theatre +honeycomb +hook, claw +hoopskirt, crinoline +horizontal bar, high bar +horse cart, horse-cart +hourglass +iPod +iron, smoothing iron +jack-o'-lantern +jean, blue jean, denim +jeep, landrover +jersey, T-shirt, tee shirt +jigsaw puzzle +jinrikisha, ricksha, rickshaw +joystick +kimono +knee pad +knot +lab coat, laboratory coat +ladle +lampshade, lamp shade +laptop, laptop computer +lawn mower, mower +lens cap, lens cover +letter opener, paper knife, paperknife +library +lifeboat +lighter, light, igniter, ignitor +limousine, limo +liner, ocean liner +lipstick, lip rouge +Loafer +lotion +loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +loupe, jeweler's loupe +lumbermill, sawmill +magnetic compass +mailbag, postbag +mailbox, letter box +maillot +maillot, tank suit +manhole cover +maraca +marimba, xylophone +mask +matchstick +maypole +maze, labyrinth +measuring cup +medicine chest, medicine cabinet +megalith, megalithic structure +microphone, mike +microwave, microwave oven +military uniform +milk can +minibus +miniskirt, mini +minivan +missile +mitten +mixing bowl +mobile home, manufactured home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter, scooter +mountain bike, all-terrain bike, off-roader +mountain tent +mouse, computer mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook, notebook computer +obelisk +oboe, hautboy, hautbois +ocarina, sweet potato +odometer, hodometer, mileometer, milometer +oil filter +organ, pipe organ +oscilloscope, scope, cathode-ray oscilloscope, CRO +overskirt +oxcart +oxygen mask +packet +paddle, boat paddle +paddlewheel, paddle wheel +padlock +paintbrush +pajama, pyjama, pj's, jammies +palace +panpipe, pandean pipe, syrinx +paper towel +parachute, chute +parallel bars, bars +park bench +parking meter +passenger car, coach, carriage +patio, terrace +pay-phone, pay-station +pedestal, plinth, footstall +pencil box, pencil case +pencil sharpener +perfume, essence +Petri dish +photocopier +pick, plectrum, plectron +pickelhaube +picket fence, paling +pickup, pickup truck +pier +piggy bank, penny bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate, pirate ship +pitcher, ewer +plane, carpenter's plane, woodworking plane +planetarium +plastic bag +plate rack +plow, plough +plunger, plumber's helper +Polaroid camera, Polaroid Land camera +pole +police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +poncho +pool table, billiard table, snooker table +pop bottle, soda bottle +pot, flowerpot +potter's wheel +power drill +prayer rug, prayer mat +printer +prison, prison house +projectile, missile +projector +puck, hockey puck +punching bag, punch bag, punching ball, punchball +purse +quill, quill pen +quilt, comforter, comfort, puff +racer, race car, racing car +racket, racquet +radiator +radio, wireless +radio telescope, radio reflector +rain barrel +recreational vehicle, RV, R.V. +reel +reflex camera +refrigerator, icebox +remote control, remote +restaurant, eating house, eating place, eatery +revolver, six-gun, six-shooter +rifle +rocking chair, rocker +rotisserie +rubber eraser, rubber, pencil eraser +rugby ball +rule, ruler +running shoe +safe +safety pin +saltshaker, salt shaker +sandal +sarong +sax, saxophone +scabbard +scale, weighing machine +school bus +schooner +scoreboard +screen, CRT screen +screw +screwdriver +seat belt, seatbelt +sewing machine +shield, buckler +shoe shop, shoe-shop, shoe store +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule, slipstick +sliding door +slot, one-armed bandit +snorkel +snowmobile +snowplow, snowplough +soap dispenser +soccer ball +sock +solar dish, solar collector, solar furnace +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web, spider's web +spindle +sports car, sport car +spotlight, spot +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch, stop watch +stove +strainer +streetcar, tram, tramcar, trolley, trolley car +stretcher +studio couch, day bed +stupa, tope +submarine, pigboat, sub, U-boat +suit, suit of clothes +sundial +sunglass +sunglasses, dark glasses, shades +sunscreen, sunblock, sun blocker +suspension bridge +swab, swob, mop +sweatshirt +swimming trunks, bathing trunks +swing +switch, electric switch, electrical switch +syringe +table lamp +tank, army tank, armored combat vehicle, armoured combat vehicle +tape player +teapot +teddy, teddy bear +television, television system +tennis ball +thatch, thatched roof +theater curtain, theatre curtain +thimble +thresher, thrasher, threshing machine +throne +tile roof +toaster +tobacco shop, tobacconist shop, tobacconist +toilet seat +torch +totem pole +tow truck, tow car, wrecker +toyshop +tractor +trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +tray +trench coat +tricycle, trike, velocipede +trimaran +tripod +triumphal arch +trolleybus, trolley coach, trackless trolley +trombone +tub, vat +turnstile +typewriter keyboard +umbrella +unicycle, monocycle +upright, upright piano +vacuum, vacuum cleaner +vase +vault +velvet +vending machine +vestment +viaduct +violin, fiddle +volleyball +waffle iron +wall clock +wallet, billfold, notecase, pocketbook +wardrobe, closet, press +warplane, military plane +washbasin, handbasin, washbowl, lavabo, wash-hand basin +washer, automatic washer, washing machine +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool, woolen, woollen +worm fence, snake fence, snake-rail fence, Virginia fence +wreck +yawl +yurt +web site, website, internet site, site +comic book +crossword puzzle, crossword +street sign +traffic light, traffic signal, stoplight +book jacket, dust cover, dust jacket, dust wrapper +menu +plate +guacamole +consomme +hot pot, hotpot +trifle +ice cream, icecream +ice lolly, lolly, lollipop, popsicle +French loaf +bagel, beigel +pretzel +cheeseburger +hotdog, hot dog, red hot +mashed potato +head cabbage +broccoli +cauliflower +zucchini, courgette +spaghetti squash +acorn squash +butternut squash +cucumber, cuke +artichoke, globe artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple, ananas +banana +jackfruit, jak, jack +custard apple +pomegranate +hay +carbonara +chocolate sauce, chocolate syrup +dough +meat loaf, meatloaf +pizza, pizza pie +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff, drop, drop-off +coral reef +geyser +lakeside, lakeshore +promontory, headland, head, foreland +sandbar, sand bar +seashore, coast, seacoast, sea-coast +valley, vale +volcano +ballplayer, baseball player +groom, bridegroom +scuba diver +rapeseed +daisy +yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +corn +acorn +hip, rose hip, rosehip +buckeye, horse chestnut, conker +coral fungus +agaric +gyromitra +stinkhorn, carrion fungus +earthstar +hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +bolete +ear, spike, capitulum +toilet tissue, toilet paper, bathroom tissue diff --git a/samples/dnn/googlenet_python.py b/samples/dnn/googlenet_python.py deleted file mode 100644 index 81ba146891..0000000000 --- a/samples/dnn/googlenet_python.py +++ /dev/null @@ -1,24 +0,0 @@ -from __future__ import print_function -import numpy as np -import cv2 as cv -from cv2 import dnn -import timeit - -def timeit_forward(net): - print("Runtime:", timeit.timeit(lambda: net.forward(), number=10)) - -def get_class_list(): - with open('synset_words.txt', 'rt') as f: - return [x[x.find(" ") + 1:] for x in f] - -blob = dnn.blobFromImage(cv.imread('space_shuttle.jpg'), 1, (224, 224), (104, 117, 123), False) -print("Input:", blob.shape, blob.dtype) - -net = dnn.readNetFromCaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel') -net.setInput(blob) -prob = net.forward() -#timeit_forward(net) #Uncomment to check performance - -print("Output:", prob.shape, prob.dtype) -classes = get_class_list() -print("Best match", classes[prob.argmax()]) \ No newline at end of file diff --git a/samples/dnn/object_detection.cpp b/samples/dnn/object_detection.cpp index 8f9369b007..bb6f6f08bc 100644 --- a/samples/dnn/object_detection.cpp +++ b/samples/dnn/object_detection.cpp @@ -1,8 +1,10 @@ -#include #include #include #include +#include +#include + const char* keys = "{ help h | | Print help message. }" "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}" @@ -19,7 +21,13 @@ const char* keys = "{ height | -1 | Preprocess input image by resizing to a specific height. }" "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }" "{ thr | .5 | Confidence threshold. }" - "{ opencl | | Enable OpenCL }"; + "{ backend | 0 | Choose one of computation backends: " + "0: default C++ backend, " + "1: Halide language (http://halide-lang.org/), " + "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}" + "{ target | 0 | Choose one of target computation devices: " + "0: CPU target (by default)," + "1: OpenCL }"; using namespace cv; using namespace dnn; @@ -27,8 +35,6 @@ using namespace dnn; float confThreshold; std::vector classes; -void loadClasses(const std::string& file); - Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = ""); void postprocess(Mat& frame, const Mat& out, Net& net); @@ -74,7 +80,7 @@ int main(int argc, char** argv) if (!ifs.is_open()) CV_Error(Error::StsError, "File " + file + " not found"); std::string line; - while (ifs >> line) + while (std::getline(ifs, line)) { classes.push_back(line); } @@ -83,17 +89,14 @@ int main(int argc, char** argv) // Load a model. CV_Assert(parser.has("model")); Net net = readNet(parser.get("model"), parser.get("config"), parser.get("framework")); - - if (parser.get("opencl")) - { - net.setPreferableTarget(DNN_TARGET_OPENCL); - } + net.setPreferableBackend(parser.get("backend")); + net.setPreferableTarget(parser.get("target")); // Create a window static const std::string kWinName = "Deep learning object detection in OpenCV"; namedWindow(kWinName, WINDOW_NORMAL); int initialConf = confThreshold * 100; - createTrackbar("Confidence threshold", kWinName, &initialConf, 99, callback); + createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback); // Open a video file or an image file or a camera stream. VideoCapture cap; @@ -134,7 +137,7 @@ int main(int argc, char** argv) std::vector layersTimes; double t = net.getPerfProfile(layersTimes); std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency()); - putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar()); + putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); imshow(kWinName, frame); } @@ -240,7 +243,7 @@ void callback(int pos, void*) Net readNet(const std::string& model, const std::string& config, const std::string& framework) { - std::string modelExt = model.substr(model.find('.')); + std::string modelExt = model.substr(model.rfind('.')); if (framework == "caffe" || modelExt == ".caffemodel") return readNetFromCaffe(config, model); else if (framework == "tensorflow" || modelExt == ".pb") diff --git a/samples/dnn/object_detection.py b/samples/dnn/object_detection.py index 76d6e5ab50..661395f012 100644 --- a/samples/dnn/object_detection.py +++ b/samples/dnn/object_detection.py @@ -3,6 +3,9 @@ import argparse import sys import numpy as np +backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) +targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL) + parser = argparse.ArgumentParser(description='Use this script to run object detection deep learning networks using OpenCV.') parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.') parser.add_argument('--model', required=True, @@ -28,6 +31,15 @@ parser.add_argument('--height', type=int, parser.add_argument('--rgb', action='store_true', help='Indicate that model works with RGB input images instead BGR ones.') parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold') +parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int, + help="Choose one of computation backends: " + "%d: default C++ backend, " + "%d: Halide language (http://halide-lang.org/), " + "%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends) +parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int, + help='Choose one of target computation devices: ' + '%d: CPU target (by default), ' + '%d: OpenCL' % targets) args = parser.parse_args() # Load names of classes @@ -37,7 +49,7 @@ if args.classes: classes = f.read().rstrip('\n').split('\n') # Load a network -modelExt = args.model[args.model.find('.'):] +modelExt = args.model[args.model.rfind('.'):] if args.framework == 'caffe' or modelExt == '.caffemodel': net = cv.dnn.readNetFromCaffe(args.config, args.model) elif args.framework == 'tensorflow' or modelExt == '.pb': @@ -50,6 +62,9 @@ else: print('Cannot determine an origin framework of model from file %s' % args.model) sys.exit(0) +net.setPreferableBackend(args.backend) +net.setPreferableTarget(args.target) + confThreshold = args.thr def postprocess(frame, out): @@ -156,6 +171,6 @@ while cv.waitKey(1) < 0: # Put efficiency information. t, _ = net.getPerfProfile() label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency()) - cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) + cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) cv.imshow(winName, frame) diff --git a/samples/dnn/squeezenet_halide.cpp b/samples/dnn/squeezenet_halide.cpp deleted file mode 100644 index f79c146df2..0000000000 --- a/samples/dnn/squeezenet_halide.cpp +++ /dev/null @@ -1,110 +0,0 @@ -// Sample of using Halide backend in OpenCV deep learning module. -// Based on caffe_googlenet.cpp. - -#include -#include -#include -using namespace cv; -using namespace cv::dnn; - -#include -#include -#include - -/* Find best class for the blob (i. e. class with maximal probability) */ -static void getMaxClass(const Mat &probBlob, int *classId, double *classProb) -{ - Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix - Point classNumber; - - minMaxLoc(probMat, NULL, classProb, NULL, &classNumber); - *classId = classNumber.x; -} - -static std::vector readClassNames(const char *filename = "synset_words.txt") -{ - std::vector classNames; - - std::ifstream fp(filename); - if (!fp.is_open()) - { - std::cerr << "File with classes labels not found: " << filename << std::endl; - exit(-1); - } - - std::string name; - while (!fp.eof()) - { - std::getline(fp, name); - if (name.length()) - classNames.push_back( name.substr(name.find(' ')+1) ); - } - - fp.close(); - return classNames; -} - -int main(int argc, char **argv) -{ - std::string modelTxt = "train_val.prototxt"; - std::string modelBin = "squeezenet_v1.1.caffemodel"; - std::string imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg"; - - //! [Read and initialize network] - Net net = dnn::readNetFromCaffe(modelTxt, modelBin); - //! [Read and initialize network] - - //! [Check that network was read successfully] - if (net.empty()) - { - std::cerr << "Can't load network by using the following files: " << std::endl; - std::cerr << "prototxt: " << modelTxt << std::endl; - std::cerr << "caffemodel: " << modelBin << std::endl; - std::cerr << "SqueezeNet v1.1 can be downloaded from:" << std::endl; - std::cerr << "https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1" << std::endl; - exit(-1); - } - //! [Check that network was read successfully] - - //! [Prepare blob] - Mat img = imread(imageFile); - if (img.empty()) - { - std::cerr << "Can't read image from the file: " << imageFile << std::endl; - exit(-1); - } - if (img.channels() != 3) - { - std::cerr << "Image " << imageFile << " isn't 3-channel" << std::endl; - exit(-1); - } - - Mat inputBlob = blobFromImage(img, 1.0, Size(227, 227), Scalar(), false, false); // Convert Mat to 4-dimensional batch. - //! [Prepare blob] - - //! [Set input blob] - net.setInput(inputBlob); // Set the network input. - //! [Set input blob] - - //! [Enable Halide backend] - net.setPreferableBackend(DNN_BACKEND_HALIDE); // Tell engine to use Halide where it possible. - //! [Enable Halide backend] - - //! [Make forward pass] - Mat prob = net.forward("prob"); // Compute output. - //! [Make forward pass] - - //! [Determine the best class] - int classId; - double classProb; - getMaxClass(prob, &classId, &classProb); // Find the best class. - //! [Determine the best class] - - //! [Print results] - std::vector classNames = readClassNames(); - std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl; - std::cout << "Probability: " << classProb * 100 << "%" << std::endl; - //! [Print results] - - return 0; -} //main diff --git a/samples/dnn/tf_inception.cpp b/samples/dnn/tf_inception.cpp deleted file mode 100644 index e411cc8519..0000000000 --- a/samples/dnn/tf_inception.cpp +++ /dev/null @@ -1,154 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (C) 2016, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. - -/* -Sample of using OpenCV dnn module with Tensorflow Inception model. -*/ - -#include -#include -#include -using namespace cv; -using namespace cv::dnn; - -#include -#include -#include -using namespace std; - -const String keys = - "{help h || Sample app for loading Inception TensorFlow model. " - "The model and class names list can be downloaded here: " - "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip }" - "{model m |tensorflow_inception_graph.pb| path to TensorFlow .pb model file }" - "{image i || path to image file }" - "{i_blob | input | input blob name) }" - "{o_blob | softmax2 | output blob name) }" - "{c_names c | imagenet_comp_graph_label_strings.txt | path to file with classnames for class id }" - "{result r || path to save output blob (optional, binary format, NCHW order) }" - ; - -void getMaxClass(const Mat &probBlob, int *classId, double *classProb); -std::vector readClassNames(const char *filename); - -int main(int argc, char **argv) -{ - cv::CommandLineParser parser(argc, argv, keys); - - if (parser.has("help")) - { - parser.printMessage(); - return 0; - } - - String modelFile = parser.get("model"); - String imageFile = parser.get("image"); - String inBlobName = parser.get("i_blob"); - String outBlobName = parser.get("o_blob"); - - if (!parser.check()) - { - parser.printErrors(); - return 0; - } - - String classNamesFile = parser.get("c_names"); - String resultFile = parser.get("result"); - - //! [Initialize network] - dnn::Net net = readNetFromTensorflow(modelFile); - //! [Initialize network] - - if (net.empty()) - { - std::cerr << "Can't load network by using the mode file: " << std::endl; - std::cerr << modelFile << std::endl; - exit(-1); - } - - //! [Prepare blob] - Mat img = imread(imageFile); - if (img.empty()) - { - std::cerr << "Can't read image from the file: " << imageFile << std::endl; - exit(-1); - } - - Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), Scalar(), true, false); //Convert Mat to batch of images - //! [Prepare blob] - inputBlob -= 117.0; - //! [Set input blob] - net.setInput(inputBlob, inBlobName); //set the network input - //! [Set input blob] - - cv::TickMeter tm; - tm.start(); - - //! [Make forward pass] - Mat result = net.forward(outBlobName); //compute output - //! [Make forward pass] - - tm.stop(); - - if (!resultFile.empty()) { - CV_Assert(result.isContinuous()); - - ofstream fout(resultFile.c_str(), ios::out | ios::binary); - fout.write((char*)result.data, result.total() * sizeof(float)); - fout.close(); - } - - std::cout << "Output blob shape " << result.size[0] << " x " << result.size[1] << " x " << result.size[2] << " x " << result.size[3] << std::endl; - std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl; - - if (!classNamesFile.empty()) { - std::vector classNames = readClassNames(classNamesFile.c_str()); - - int classId; - double classProb; - getMaxClass(result, &classId, &classProb);//find the best class - - //! [Print results] - std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl; - std::cout << "Probability: " << classProb * 100 << "%" << std::endl; - } - return 0; -} //main - - -/* Find best class for the blob (i. e. class with maximal probability) */ -void getMaxClass(const Mat &probBlob, int *classId, double *classProb) -{ - Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix - Point classNumber; - - minMaxLoc(probMat, NULL, classProb, NULL, &classNumber); - *classId = classNumber.x; -} - -std::vector readClassNames(const char *filename) -{ - std::vector classNames; - - std::ifstream fp(filename); - if (!fp.is_open()) - { - std::cerr << "File with classes labels not found: " << filename << std::endl; - exit(-1); - } - - std::string name; - while (!fp.eof()) - { - std::getline(fp, name); - if (name.length()) - classNames.push_back( name ); - } - - fp.close(); - return classNames; -}