├── LICENSE
├── README.md
├── cat.jpg
├── eval_image.py
├── mobilenet.caffemodel
├── mobilenet_deploy.prototxt
├── mobilenet_v2.caffemodel
├── mobilenet_v2_deploy.prototxt
└── synset.txt


/LICENSE:
--------------------------------------------------------------------------------
 1 | BSD 3-Clause License
 2 | 
 3 | Copyright (c) 2017-, Shicai Yang
 4 | All rights reserved.
 5 | 
 6 | Redistribution and use in source and binary forms, with or without
 7 | modification, are permitted provided that the following conditions are met:
 8 | 
 9 | * Redistributions of source code must retain the above copyright notice, this
10 |   list of conditions and the following disclaimer.
11 | 
12 | * Redistributions in binary form must reproduce the above copyright notice,
13 |   this list of conditions and the following disclaimer in the documentation
14 |   and/or other materials provided with the distribution.
15 | 
16 | * Neither the name of the copyright holder nor the names of its
17 |   contributors may be used to endorse or promote products derived from
18 |   this software without specific prior written permission.
19 | 
20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
30 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # MobileNet-Caffe
  2 | 
  3 | ### Introduction
  4 | 
  5 | This is a Caffe implementation of Google's MobileNets (v1 and v2). For details, please read the following papers:
  6 | - [v1] [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
  7 | - [v2] [Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381)
  8 | 
  9 | 
 10 | ### Pretrained Models on ImageNet
 11 | 
 12 | We provide pretrained MobileNet models on ImageNet, which achieve slightly better accuracy rates than the original ones reported in the paper. 
 13 | 
 14 | The top-1/5 accuracy rates by using single center crop (crop size: 224x224, image size: 256xN):
 15 | 
 16 | Network|Top-1|Top-5|sha256sum|Architecture
 17 | :---:|:---:|:---:|:---:|:---:
 18 | MobileNet v1| 70.81| 89.85| 8d6edcd3 (16.2 MB) | [netscope](http://ethereon.github.io/netscope/#/gist/2883d142ae486d4237e50f392f32994e), [netron](http://lutzroeder.github.io/netron?gist=2883d142ae486d4237e50f392f32994e)
 19 | MobileNet v2| 71.90| 90.49| a3124ce7 (13.5 MB)| [netscope](http://ethereon.github.io/netscope/#/gist/d01b5b8783b4582a42fe07bd46243986), [netron](http://lutzroeder.github.io/netron?gist=d01b5b8783b4582a42fe07bd46243986)
 20 | 
 21 | 
 22 | ### Evaluate Models with a single image
 23 | 
 24 | Evaluate MobileNet v1:
 25 | 
 26 | `python eval_image.py --proto mobilenet_deploy.prototxt --model mobilenet.caffemodel --image ./cat.jpg`
 27 | 
 28 | Expected Outputs:
 29 | 
 30 | ```
 31 | 0.42 - 'n02123159 tiger cat'
 32 | 0.08 - 'n02119022 red fox, Vulpes vulpes'
 33 | 0.07 - 'n02119789 kit fox, Vulpes macrotis'
 34 | 0.06 - 'n02113023 Pembroke, Pembroke Welsh corgi'
 35 | 0.06 - 'n02123045 tabby, tabby cat'
 36 | ```
 37 | 
 38 | Evaluate MobileNet v2:
 39 | 
 40 | `python eval_image.py --proto mobilenet_v2_deploy.prototxt --model mobilenet_v2.caffemodel  --image ./cat.jpg`
 41 | 
 42 | Expected Outputs:
 43 | 
 44 | ```
 45 | 0.26 - 'n02123159 tiger cat'
 46 | 0.22 - 'n02124075 Egyptian cat'
 47 | 0.15 - 'n02123045 tabby, tabby cat'
 48 | 0.04 - 'n02119022 red fox, Vulpes vulpes'
 49 | 0.02 - 'n02326432 hare'
 50 | ```
 51 | 
 52 | ### Finetuning on your own data
 53 | Modify `deploy.prototxt` and save it as your `train.prototxt` as follows:
 54 | Remove the first 5 `input`/`input_dim` lines, and add `Image Data` layer in the beginning like this:
 55 | ```
 56 | layer {
 57 |   name: "data"
 58 |   type: "ImageData"
 59 |   top: "data"
 60 |   top: "label"
 61 |   include {
 62 |     phase: TRAIN
 63 |   }
 64 |   transform_param {
 65 |     scale: 0.017
 66 |     mirror: true
 67 |     crop_size: 224
 68 |     mean_value: [103.94, 116.78, 123.68]
 69 |   }
 70 |   image_data_param {
 71 |     source: "your_list_train_txt"
 72 |     batch_size: 32 # your batch size
 73 |     new_height: 256
 74 |     new_width: 256
 75 |     root_folder: "your_path_to_training_data_folder"
 76 |   }
 77 | }
 78 | 
 79 | ```
 80 | 
 81 | Remove the last `prob` layer, and add `Loss` and `Accuracy` layers in the end like this:
 82 | ```
 83 | layer {
 84 |   name: "loss"
 85 |   type: "SoftmaxWithLoss"
 86 |   bottom: "fc7"
 87 |   bottom: "label"
 88 |   top: "loss"
 89 | }
 90 | layer {
 91 |   name: "top1/acc"
 92 |   type: "Accuracy"
 93 |   bottom: "fc7"
 94 |   bottom: "label"
 95 |   top: "top1/acc"
 96 |   include {
 97 |     phase: TEST
 98 |   }
 99 | }
100 | layer {
101 |   name: "top5/acc"
102 |   type: "Accuracy"
103 |   bottom: "fc7"
104 |   bottom: "label"
105 |   top: "top5/acc"
106 |   include {
107 |     phase: TEST
108 |   }
109 |   accuracy_param {
110 |     top_k: 5
111 |   }
112 | }
113 | ```
114 | ### Related Projects
115 | MobileNet in this repo has been used in the following projects, we recommend you to take a look:
116 | - The MobileNet neural network using Apple's new CoreML framework
117 |  [hollance/MobileNet-CoreML](https://github.com/hollance/MobileNet-CoreML)
118 | - Mobile-deep-learning [baidu/mobile-deep-learning](https://github.com/baidu/mobile-deep-learning)
119 | - Receptive Field Block Net for Accurate and Fast Object Detection [ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet)
120 | - Depthwise Convolutional Layer [yonghenglh6/DepthwiseConvolution](https://github.com/yonghenglh6/DepthwiseConvolution)
121 | - MobileNet-MXNet [KeyKy/mobilenet-mxnet](https://github.com/KeyKy/mobilenet-mxnet)
122 | - Caffe2-MobileNet [camel007/caffe2-mobilenet](https://github.com/camel007/caffe2-mobilenet)
123 | 
124 | 
125 | ### Updates (Feb. 5, 2018)
126 | 
127 | - Add pretrained MobileNet v2 models (including deploy.prototxt and weights)
128 | - Hold pretrained weights in this repo
129 | - Add sha256sum code for pretrained weights
130 | - Add some code snippets for single image evaluation
131 | - Uncomment **engine: CAFFE** used in `mobilenet_deploy.prototxt`
132 | - Add params (`lr_mult` and `decay_mult`) for `Scale` layers of `mobilenet_deploy.prototxt`
133 | - Add `prob` layer for `mobilenet_deploy.prototxt`
134 | 


--------------------------------------------------------------------------------
/cat.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/shicai/MobileNet-Caffe/a0b0c46152e658559956158d7c2bf42bb029c09e/cat.jpg


--------------------------------------------------------------------------------
/eval_image.py:
--------------------------------------------------------------------------------
 1 | from __future__ import print_function
 2 | import argparse
 3 | import numpy as np
 4 | import caffe
 5 | 
 6 | 
 7 | def parse_args():
 8 |     parser = argparse.ArgumentParser(
 9 |         description='evaluate pretrained mobilenet models')
10 |     parser.add_argument('--proto', dest='proto',
11 |                         help="path to deploy prototxt.", type=str)
12 |     parser.add_argument('--model', dest='model',
13 |                         help='path to pretrained weights', type=str)
14 |     parser.add_argument('--image', dest='image',
15 |                         help='path to color image', type=str)
16 | 
17 |     args = parser.parse_args()
18 |     return args, parser
19 | 
20 | 
21 | global args, parser
22 | args, parser = parse_args()
23 | 
24 | 
25 | def eval():
26 |     nh, nw = 224, 224
27 |     img_mean = np.array([103.94, 116.78, 123.68], dtype=np.float32)
28 | 
29 |     caffe.set_mode_cpu()
30 |     net = caffe.Net(args.proto, args.model, caffe.TEST)
31 | 
32 |     im = caffe.io.load_image(args.image)
33 |     h, w, _ = im.shape
34 |     if h < w:
35 |         off = (w - h) / 2
36 |         im = im[:, off:off + h]
37 |     else:
38 |         off = (h - w) / 2
39 |         im = im[off:off + h, :]
40 |     im = caffe.io.resize_image(im, [nh, nw])
41 | 
42 |     transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
43 |     transformer.set_transpose('data', (2, 0, 1))  # row to col
44 |     transformer.set_channel_swap('data', (2, 1, 0))  # RGB to BGR
45 |     transformer.set_raw_scale('data', 255)  # [0,1] to [0,255]
46 |     transformer.set_mean('data', img_mean)
47 |     transformer.set_input_scale('data', 0.017)
48 | 
49 |     net.blobs['data'].reshape(1, 3, nh, nw)
50 |     net.blobs['data'].data[...] = transformer.preprocess('data', im)
51 |     out = net.forward()
52 |     prob = out['prob']
53 |     prob = np.squeeze(prob)
54 |     idx = np.argsort(-prob)
55 | 
56 |     label_names = np.loadtxt('synset.txt', str, delimiter='\t')
57 |     for i in range(5):
58 |         label = idx[i]
59 |         print('%.2f - %s' % (prob[label], label_names[label]))
60 |     return
61 | 
62 | 
63 | if __name__ == '__main__':
64 |     eval()
65 | 


--------------------------------------------------------------------------------
/mobilenet.caffemodel:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/shicai/MobileNet-Caffe/a0b0c46152e658559956158d7c2bf42bb029c09e/mobilenet.caffemodel


--------------------------------------------------------------------------------
/mobilenet_deploy.prototxt:
--------------------------------------------------------------------------------
   1 | name: "MOBILENET"
   2 | #  transform_param {
   3 | #    scale: 0.017
   4 | #    mirror: false
   5 | #    crop_size: 224
   6 | #    mean_value: [103.94,116.78,123.68]
   7 | #  }
   8 | input: "data"
   9 | input_dim: 1
  10 | input_dim: 3
  11 | input_dim: 224
  12 | input_dim: 224
  13 | layer {
  14 |   name: "conv1"
  15 |   type: "Convolution"
  16 |   bottom: "data"
  17 |   top: "conv1"
  18 |   param {
  19 |     lr_mult: 1
  20 |     decay_mult: 1
  21 |   }
  22 |   convolution_param {
  23 |     num_output: 32
  24 |     bias_term: false
  25 |     pad: 1
  26 |     kernel_size: 3
  27 |     stride: 2
  28 |     weight_filler {
  29 |       type: "msra"
  30 |     }
  31 |   }
  32 | }
  33 | layer {
  34 |   name: "conv1/bn"
  35 |   type: "BatchNorm"
  36 |   bottom: "conv1"
  37 |   top: "conv1"
  38 |   param {
  39 |     lr_mult: 0
  40 |     decay_mult: 0
  41 |   }
  42 |   param {
  43 |     lr_mult: 0
  44 |     decay_mult: 0
  45 |   }
  46 |   param {
  47 |     lr_mult: 0
  48 |     decay_mult: 0
  49 |   }
  50 |   batch_norm_param {
  51 |     use_global_stats: true
  52 |     eps: 1e-5
  53 |   }
  54 | }
  55 | layer {
  56 |   name: "conv1/scale"
  57 |   type: "Scale"
  58 |   bottom: "conv1"
  59 |   top: "conv1"
  60 |   param {
  61 |     lr_mult: 1
  62 |     decay_mult: 0
  63 |   }
  64 |   param {
  65 |     lr_mult: 1
  66 |     decay_mult: 0
  67 |   }
  68 |   scale_param {
  69 |     filler {
  70 |       value: 1
  71 |     }
  72 |     bias_term: true
  73 |     bias_filler {
  74 |       value: 0
  75 |     }
  76 |   }
  77 | }
  78 | layer {
  79 |   name: "relu1"
  80 |   type: "ReLU"
  81 |   bottom: "conv1"
  82 |   top: "conv1"
  83 | }
  84 | layer {
  85 |   name: "conv2_1/dw"
  86 |   type: "Convolution"
  87 |   bottom: "conv1"
  88 |   top: "conv2_1/dw"
  89 |   param {
  90 |     lr_mult: 1
  91 |     decay_mult: 1
  92 |   }
  93 |   convolution_param {
  94 |     num_output: 32
  95 |     bias_term: false
  96 |     pad: 1
  97 |     kernel_size: 3
  98 |     group: 32
  99 |     engine: CAFFE
 100 |     stride: 1
 101 |     weight_filler {
 102 |       type: "msra"
 103 |     }
 104 |   }
 105 | }
 106 | layer {
 107 |   name: "conv2_1/dw/bn"
 108 |   type: "BatchNorm"
 109 |   bottom: "conv2_1/dw"
 110 |   top: "conv2_1/dw"
 111 |   param {
 112 |     lr_mult: 0
 113 |     decay_mult: 0
 114 |   }
 115 |   param {
 116 |     lr_mult: 0
 117 |     decay_mult: 0
 118 |   }
 119 |   param {
 120 |     lr_mult: 0
 121 |     decay_mult: 0
 122 |   }
 123 |   batch_norm_param {
 124 |     use_global_stats: true
 125 |     eps: 1e-5
 126 |   }
 127 | }
 128 | layer {
 129 |   name: "conv2_1/dw/scale"
 130 |   type: "Scale"
 131 |   bottom: "conv2_1/dw"
 132 |   top: "conv2_1/dw"
 133 |   param {
 134 |     lr_mult: 1
 135 |     decay_mult: 0
 136 |   }
 137 |   param {
 138 |     lr_mult: 1
 139 |     decay_mult: 0
 140 |   }
 141 |   scale_param {
 142 |     filler {
 143 |       value: 1
 144 |     }
 145 |     bias_term: true
 146 |     bias_filler {
 147 |       value: 0
 148 |     }
 149 |   }
 150 | }
 151 | layer {
 152 |   name: "relu2_1/dw"
 153 |   type: "ReLU"
 154 |   bottom: "conv2_1/dw"
 155 |   top: "conv2_1/dw"
 156 | }
 157 | layer {
 158 |   name: "conv2_1/sep"
 159 |   type: "Convolution"
 160 |   bottom: "conv2_1/dw"
 161 |   top: "conv2_1/sep"
 162 |   param {
 163 |     lr_mult: 1
 164 |     decay_mult: 1
 165 |   }
 166 |   convolution_param {
 167 |     num_output: 64
 168 |     bias_term: false
 169 |     pad: 0
 170 |     kernel_size: 1
 171 |     stride: 1
 172 |     weight_filler {
 173 |       type: "msra"
 174 |     }
 175 |   }
 176 | }
 177 | layer {
 178 |   name: "conv2_1/sep/bn"
 179 |   type: "BatchNorm"
 180 |   bottom: "conv2_1/sep"
 181 |   top: "conv2_1/sep"
 182 |   param {
 183 |     lr_mult: 0
 184 |     decay_mult: 0
 185 |   }
 186 |   param {
 187 |     lr_mult: 0
 188 |     decay_mult: 0
 189 |   }
 190 |   param {
 191 |     lr_mult: 0
 192 |     decay_mult: 0
 193 |   }
 194 |   batch_norm_param {
 195 |     use_global_stats: true
 196 |     eps: 1e-5
 197 |   }
 198 | }
 199 | layer {
 200 |   name: "conv2_1/sep/scale"
 201 |   type: "Scale"
 202 |   bottom: "conv2_1/sep"
 203 |   top: "conv2_1/sep"
 204 |   param {
 205 |     lr_mult: 1
 206 |     decay_mult: 0
 207 |   }
 208 |   param {
 209 |     lr_mult: 1
 210 |     decay_mult: 0
 211 |   }
 212 |   scale_param {
 213 |     filler {
 214 |       value: 1
 215 |     }
 216 |     bias_term: true
 217 |     bias_filler {
 218 |       value: 0
 219 |     }
 220 |   }
 221 | }
 222 | layer {
 223 |   name: "relu2_1/sep"
 224 |   type: "ReLU"
 225 |   bottom: "conv2_1/sep"
 226 |   top: "conv2_1/sep"
 227 | }
 228 | layer {
 229 |   name: "conv2_2/dw"
 230 |   type: "Convolution"
 231 |   bottom: "conv2_1/sep"
 232 |   top: "conv2_2/dw"
 233 |   param {
 234 |     lr_mult: 1
 235 |     decay_mult: 1
 236 |   }
 237 |   convolution_param {
 238 |     num_output: 64
 239 |     bias_term: false
 240 |     pad: 1
 241 |     kernel_size: 3
 242 |     group: 64
 243 |     engine: CAFFE
 244 |     stride: 2
 245 |     weight_filler {
 246 |       type: "msra"
 247 |     }
 248 |   }
 249 | }
 250 | layer {
 251 |   name: "conv2_2/dw/bn"
 252 |   type: "BatchNorm"
 253 |   bottom: "conv2_2/dw"
 254 |   top: "conv2_2/dw"
 255 |   param {
 256 |     lr_mult: 0
 257 |     decay_mult: 0
 258 |   }
 259 |   param {
 260 |     lr_mult: 0
 261 |     decay_mult: 0
 262 |   }
 263 |   param {
 264 |     lr_mult: 0
 265 |     decay_mult: 0
 266 |   }
 267 |   batch_norm_param {
 268 |     use_global_stats: true
 269 |     eps: 1e-5
 270 |   }
 271 | }
 272 | layer {
 273 |   name: "conv2_2/dw/scale"
 274 |   type: "Scale"
 275 |   bottom: "conv2_2/dw"
 276 |   top: "conv2_2/dw"
 277 |   param {
 278 |     lr_mult: 1
 279 |     decay_mult: 0
 280 |   }
 281 |   param {
 282 |     lr_mult: 1
 283 |     decay_mult: 0
 284 |   }
 285 |   scale_param {
 286 |     filler {
 287 |       value: 1
 288 |     }
 289 |     bias_term: true
 290 |     bias_filler {
 291 |       value: 0
 292 |     }
 293 |   }
 294 | }
 295 | layer {
 296 |   name: "relu2_2/dw"
 297 |   type: "ReLU"
 298 |   bottom: "conv2_2/dw"
 299 |   top: "conv2_2/dw"
 300 | }
 301 | layer {
 302 |   name: "conv2_2/sep"
 303 |   type: "Convolution"
 304 |   bottom: "conv2_2/dw"
 305 |   top: "conv2_2/sep"
 306 |   param {
 307 |     lr_mult: 1
 308 |     decay_mult: 1
 309 |   }
 310 |   convolution_param {
 311 |     num_output: 128
 312 |     bias_term: false
 313 |     pad: 0
 314 |     kernel_size: 1
 315 |     stride: 1
 316 |     weight_filler {
 317 |       type: "msra"
 318 |     }
 319 |   }
 320 | }
 321 | layer {
 322 |   name: "conv2_2/sep/bn"
 323 |   type: "BatchNorm"
 324 |   bottom: "conv2_2/sep"
 325 |   top: "conv2_2/sep"
 326 |   param {
 327 |     lr_mult: 0
 328 |     decay_mult: 0
 329 |   }
 330 |   param {
 331 |     lr_mult: 0
 332 |     decay_mult: 0
 333 |   }
 334 |   param {
 335 |     lr_mult: 0
 336 |     decay_mult: 0
 337 |   }
 338 |   batch_norm_param {
 339 |     use_global_stats: true
 340 |     eps: 1e-5
 341 |   }
 342 | }
 343 | layer {
 344 |   name: "conv2_2/sep/scale"
 345 |   type: "Scale"
 346 |   bottom: "conv2_2/sep"
 347 |   top: "conv2_2/sep"
 348 |   param {
 349 |     lr_mult: 1
 350 |     decay_mult: 0
 351 |   }
 352 |   param {
 353 |     lr_mult: 1
 354 |     decay_mult: 0
 355 |   }
 356 |   scale_param {
 357 |     filler {
 358 |       value: 1
 359 |     }
 360 |     bias_term: true
 361 |     bias_filler {
 362 |       value: 0
 363 |     }
 364 |   }
 365 | }
 366 | layer {
 367 |   name: "relu2_2/sep"
 368 |   type: "ReLU"
 369 |   bottom: "conv2_2/sep"
 370 |   top: "conv2_2/sep"
 371 | }
 372 | layer {
 373 |   name: "conv3_1/dw"
 374 |   type: "Convolution"
 375 |   bottom: "conv2_2/sep"
 376 |   top: "conv3_1/dw"
 377 |   param {
 378 |     lr_mult: 1
 379 |     decay_mult: 1
 380 |   }
 381 |   convolution_param {
 382 |     num_output: 128
 383 |     bias_term: false
 384 |     pad: 1
 385 |     kernel_size: 3
 386 |     group: 128
 387 |     engine: CAFFE
 388 |     stride: 1
 389 |     weight_filler {
 390 |       type: "msra"
 391 |     }
 392 |   }
 393 | }
 394 | layer {
 395 |   name: "conv3_1/dw/bn"
 396 |   type: "BatchNorm"
 397 |   bottom: "conv3_1/dw"
 398 |   top: "conv3_1/dw"
 399 |   param {
 400 |     lr_mult: 0
 401 |     decay_mult: 0
 402 |   }
 403 |   param {
 404 |     lr_mult: 0
 405 |     decay_mult: 0
 406 |   }
 407 |   param {
 408 |     lr_mult: 0
 409 |     decay_mult: 0
 410 |   }
 411 |   batch_norm_param {
 412 |     use_global_stats: true
 413 |     eps: 1e-5
 414 |   }
 415 | }
 416 | layer {
 417 |   name: "conv3_1/dw/scale"
 418 |   type: "Scale"
 419 |   bottom: "conv3_1/dw"
 420 |   top: "conv3_1/dw"
 421 |   param {
 422 |     lr_mult: 1
 423 |     decay_mult: 0
 424 |   }
 425 |   param {
 426 |     lr_mult: 1
 427 |     decay_mult: 0
 428 |   }
 429 |   scale_param {
 430 |     filler {
 431 |       value: 1
 432 |     }
 433 |     bias_term: true
 434 |     bias_filler {
 435 |       value: 0
 436 |     }
 437 |   }
 438 | }
 439 | layer {
 440 |   name: "relu3_1/dw"
 441 |   type: "ReLU"
 442 |   bottom: "conv3_1/dw"
 443 |   top: "conv3_1/dw"
 444 | }
 445 | layer {
 446 |   name: "conv3_1/sep"
 447 |   type: "Convolution"
 448 |   bottom: "conv3_1/dw"
 449 |   top: "conv3_1/sep"
 450 |   param {
 451 |     lr_mult: 1
 452 |     decay_mult: 1
 453 |   }
 454 |   convolution_param {
 455 |     num_output: 128
 456 |     bias_term: false
 457 |     pad: 0
 458 |     kernel_size: 1
 459 |     stride: 1
 460 |     weight_filler {
 461 |       type: "msra"
 462 |     }
 463 |   }
 464 | }
 465 | layer {
 466 |   name: "conv3_1/sep/bn"
 467 |   type: "BatchNorm"
 468 |   bottom: "conv3_1/sep"
 469 |   top: "conv3_1/sep"
 470 |   param {
 471 |     lr_mult: 0
 472 |     decay_mult: 0
 473 |   }
 474 |   param {
 475 |     lr_mult: 0
 476 |     decay_mult: 0
 477 |   }
 478 |   param {
 479 |     lr_mult: 0
 480 |     decay_mult: 0
 481 |   }
 482 |   batch_norm_param {
 483 |     use_global_stats: true
 484 |     eps: 1e-5
 485 |   }
 486 | }
 487 | layer {
 488 |   name: "conv3_1/sep/scale"
 489 |   type: "Scale"
 490 |   bottom: "conv3_1/sep"
 491 |   top: "conv3_1/sep"
 492 |   param {
 493 |     lr_mult: 1
 494 |     decay_mult: 0
 495 |   }
 496 |   param {
 497 |     lr_mult: 1
 498 |     decay_mult: 0
 499 |   }
 500 |   scale_param {
 501 |     filler {
 502 |       value: 1
 503 |     }
 504 |     bias_term: true
 505 |     bias_filler {
 506 |       value: 0
 507 |     }
 508 |   }
 509 | }
 510 | layer {
 511 |   name: "relu3_1/sep"
 512 |   type: "ReLU"
 513 |   bottom: "conv3_1/sep"
 514 |   top: "conv3_1/sep"
 515 | }
 516 | layer {
 517 |   name: "conv3_2/dw"
 518 |   type: "Convolution"
 519 |   bottom: "conv3_1/sep"
 520 |   top: "conv3_2/dw"
 521 |   param {
 522 |     lr_mult: 1
 523 |     decay_mult: 1
 524 |   }
 525 |   convolution_param {
 526 |     num_output: 128
 527 |     bias_term: false
 528 |     pad: 1
 529 |     kernel_size: 3
 530 |     group: 128
 531 |     engine: CAFFE
 532 |     stride: 2
 533 |     weight_filler {
 534 |       type: "msra"
 535 |     }
 536 |   }
 537 | }
 538 | layer {
 539 |   name: "conv3_2/dw/bn"
 540 |   type: "BatchNorm"
 541 |   bottom: "conv3_2/dw"
 542 |   top: "conv3_2/dw"
 543 |   param {
 544 |     lr_mult: 0
 545 |     decay_mult: 0
 546 |   }
 547 |   param {
 548 |     lr_mult: 0
 549 |     decay_mult: 0
 550 |   }
 551 |   param {
 552 |     lr_mult: 0
 553 |     decay_mult: 0
 554 |   }
 555 |   batch_norm_param {
 556 |     use_global_stats: true
 557 |     eps: 1e-5
 558 |   }
 559 | }
 560 | layer {
 561 |   name: "conv3_2/dw/scale"
 562 |   type: "Scale"
 563 |   bottom: "conv3_2/dw"
 564 |   top: "conv3_2/dw"
 565 |   param {
 566 |     lr_mult: 1
 567 |     decay_mult: 0
 568 |   }
 569 |   param {
 570 |     lr_mult: 1
 571 |     decay_mult: 0
 572 |   }
 573 |   scale_param {
 574 |     filler {
 575 |       value: 1
 576 |     }
 577 |     bias_term: true
 578 |     bias_filler {
 579 |       value: 0
 580 |     }
 581 |   }
 582 | }
 583 | layer {
 584 |   name: "relu3_2/dw"
 585 |   type: "ReLU"
 586 |   bottom: "conv3_2/dw"
 587 |   top: "conv3_2/dw"
 588 | }
 589 | layer {
 590 |   name: "conv3_2/sep"
 591 |   type: "Convolution"
 592 |   bottom: "conv3_2/dw"
 593 |   top: "conv3_2/sep"
 594 |   param {
 595 |     lr_mult: 1
 596 |     decay_mult: 1
 597 |   }
 598 |   convolution_param {
 599 |     num_output: 256
 600 |     bias_term: false
 601 |     pad: 0
 602 |     kernel_size: 1
 603 |     stride: 1
 604 |     weight_filler {
 605 |       type: "msra"
 606 |     }
 607 |   }
 608 | }
 609 | layer {
 610 |   name: "conv3_2/sep/bn"
 611 |   type: "BatchNorm"
 612 |   bottom: "conv3_2/sep"
 613 |   top: "conv3_2/sep"
 614 |   param {
 615 |     lr_mult: 0
 616 |     decay_mult: 0
 617 |   }
 618 |   param {
 619 |     lr_mult: 0
 620 |     decay_mult: 0
 621 |   }
 622 |   param {
 623 |     lr_mult: 0
 624 |     decay_mult: 0
 625 |   }
 626 |   batch_norm_param {
 627 |     use_global_stats: true
 628 |     eps: 1e-5
 629 |   }
 630 | }
 631 | layer {
 632 |   name: "conv3_2/sep/scale"
 633 |   type: "Scale"
 634 |   bottom: "conv3_2/sep"
 635 |   top: "conv3_2/sep"
 636 |   param {
 637 |     lr_mult: 1
 638 |     decay_mult: 0
 639 |   }
 640 |   param {
 641 |     lr_mult: 1
 642 |     decay_mult: 0
 643 |   }
 644 |   scale_param {
 645 |     filler {
 646 |       value: 1
 647 |     }
 648 |     bias_term: true
 649 |     bias_filler {
 650 |       value: 0
 651 |     }
 652 |   }
 653 | }
 654 | layer {
 655 |   name: "relu3_2/sep"
 656 |   type: "ReLU"
 657 |   bottom: "conv3_2/sep"
 658 |   top: "conv3_2/sep"
 659 | }
 660 | layer {
 661 |   name: "conv4_1/dw"
 662 |   type: "Convolution"
 663 |   bottom: "conv3_2/sep"
 664 |   top: "conv4_1/dw"
 665 |   param {
 666 |     lr_mult: 1
 667 |     decay_mult: 1
 668 |   }
 669 |   convolution_param {
 670 |     num_output: 256
 671 |     bias_term: false
 672 |     pad: 1
 673 |     kernel_size: 3
 674 |     group: 256
 675 |     engine: CAFFE
 676 |     stride: 1
 677 |     weight_filler {
 678 |       type: "msra"
 679 |     }
 680 |   }
 681 | }
 682 | layer {
 683 |   name: "conv4_1/dw/bn"
 684 |   type: "BatchNorm"
 685 |   bottom: "conv4_1/dw"
 686 |   top: "conv4_1/dw"
 687 |   param {
 688 |     lr_mult: 0
 689 |     decay_mult: 0
 690 |   }
 691 |   param {
 692 |     lr_mult: 0
 693 |     decay_mult: 0
 694 |   }
 695 |   param {
 696 |     lr_mult: 0
 697 |     decay_mult: 0
 698 |   }
 699 |   batch_norm_param {
 700 |     use_global_stats: true
 701 |     eps: 1e-5
 702 |   }
 703 | }
 704 | layer {
 705 |   name: "conv4_1/dw/scale"
 706 |   type: "Scale"
 707 |   bottom: "conv4_1/dw"
 708 |   top: "conv4_1/dw"
 709 |   param {
 710 |     lr_mult: 1
 711 |     decay_mult: 0
 712 |   }
 713 |   param {
 714 |     lr_mult: 1
 715 |     decay_mult: 0
 716 |   }
 717 |   scale_param {
 718 |     filler {
 719 |       value: 1
 720 |     }
 721 |     bias_term: true
 722 |     bias_filler {
 723 |       value: 0
 724 |     }
 725 |   }
 726 | }
 727 | layer {
 728 |   name: "relu4_1/dw"
 729 |   type: "ReLU"
 730 |   bottom: "conv4_1/dw"
 731 |   top: "conv4_1/dw"
 732 | }
 733 | layer {
 734 |   name: "conv4_1/sep"
 735 |   type: "Convolution"
 736 |   bottom: "conv4_1/dw"
 737 |   top: "conv4_1/sep"
 738 |   param {
 739 |     lr_mult: 1
 740 |     decay_mult: 1
 741 |   }
 742 |   convolution_param {
 743 |     num_output: 256
 744 |     bias_term: false
 745 |     pad: 0
 746 |     kernel_size: 1
 747 |     stride: 1
 748 |     weight_filler {
 749 |       type: "msra"
 750 |     }
 751 |   }
 752 | }
 753 | layer {
 754 |   name: "conv4_1/sep/bn"
 755 |   type: "BatchNorm"
 756 |   bottom: "conv4_1/sep"
 757 |   top: "conv4_1/sep"
 758 |   param {
 759 |     lr_mult: 0
 760 |     decay_mult: 0
 761 |   }
 762 |   param {
 763 |     lr_mult: 0
 764 |     decay_mult: 0
 765 |   }
 766 |   param {
 767 |     lr_mult: 0
 768 |     decay_mult: 0
 769 |   }
 770 |   batch_norm_param {
 771 |     use_global_stats: true
 772 |     eps: 1e-5
 773 |   }
 774 | }
 775 | layer {
 776 |   name: "conv4_1/sep/scale"
 777 |   type: "Scale"
 778 |   bottom: "conv4_1/sep"
 779 |   top: "conv4_1/sep"
 780 |   param {
 781 |     lr_mult: 1
 782 |     decay_mult: 0
 783 |   }
 784 |   param {
 785 |     lr_mult: 1
 786 |     decay_mult: 0
 787 |   }
 788 |   scale_param {
 789 |     filler {
 790 |       value: 1
 791 |     }
 792 |     bias_term: true
 793 |     bias_filler {
 794 |       value: 0
 795 |     }
 796 |   }
 797 | }
 798 | layer {
 799 |   name: "relu4_1/sep"
 800 |   type: "ReLU"
 801 |   bottom: "conv4_1/sep"
 802 |   top: "conv4_1/sep"
 803 | }
 804 | layer {
 805 |   name: "conv4_2/dw"
 806 |   type: "Convolution"
 807 |   bottom: "conv4_1/sep"
 808 |   top: "conv4_2/dw"
 809 |   param {
 810 |     lr_mult: 1
 811 |     decay_mult: 1
 812 |   }
 813 |   convolution_param {
 814 |     num_output: 256
 815 |     bias_term: false
 816 |     pad: 1
 817 |     kernel_size: 3
 818 |     group: 256
 819 |     engine: CAFFE
 820 |     stride: 2
 821 |     weight_filler {
 822 |       type: "msra"
 823 |     }
 824 |   }
 825 | }
 826 | layer {
 827 |   name: "conv4_2/dw/bn"
 828 |   type: "BatchNorm"
 829 |   bottom: "conv4_2/dw"
 830 |   top: "conv4_2/dw"
 831 |   param {
 832 |     lr_mult: 0
 833 |     decay_mult: 0
 834 |   }
 835 |   param {
 836 |     lr_mult: 0
 837 |     decay_mult: 0
 838 |   }
 839 |   param {
 840 |     lr_mult: 0
 841 |     decay_mult: 0
 842 |   }
 843 |   batch_norm_param {
 844 |     use_global_stats: true
 845 |     eps: 1e-5
 846 |   }
 847 | }
 848 | layer {
 849 |   name: "conv4_2/dw/scale"
 850 |   type: "Scale"
 851 |   bottom: "conv4_2/dw"
 852 |   top: "conv4_2/dw"
 853 |   param {
 854 |     lr_mult: 1
 855 |     decay_mult: 0
 856 |   }
 857 |   param {
 858 |     lr_mult: 1
 859 |     decay_mult: 0
 860 |   }
 861 |   scale_param {
 862 |     filler {
 863 |       value: 1
 864 |     }
 865 |     bias_term: true
 866 |     bias_filler {
 867 |       value: 0
 868 |     }
 869 |   }
 870 | }
 871 | layer {
 872 |   name: "relu4_2/dw"
 873 |   type: "ReLU"
 874 |   bottom: "conv4_2/dw"
 875 |   top: "conv4_2/dw"
 876 | }
 877 | layer {
 878 |   name: "conv4_2/sep"
 879 |   type: "Convolution"
 880 |   bottom: "conv4_2/dw"
 881 |   top: "conv4_2/sep"
 882 |   param {
 883 |     lr_mult: 1
 884 |     decay_mult: 1
 885 |   }
 886 |   convolution_param {
 887 |     num_output: 512
 888 |     bias_term: false
 889 |     pad: 0
 890 |     kernel_size: 1
 891 |     stride: 1
 892 |     weight_filler {
 893 |       type: "msra"
 894 |     }
 895 |   }
 896 | }
 897 | layer {
 898 |   name: "conv4_2/sep/bn"
 899 |   type: "BatchNorm"
 900 |   bottom: "conv4_2/sep"
 901 |   top: "conv4_2/sep"
 902 |   param {
 903 |     lr_mult: 0
 904 |     decay_mult: 0
 905 |   }
 906 |   param {
 907 |     lr_mult: 0
 908 |     decay_mult: 0
 909 |   }
 910 |   param {
 911 |     lr_mult: 0
 912 |     decay_mult: 0
 913 |   }
 914 |   batch_norm_param {
 915 |     use_global_stats: true
 916 |     eps: 1e-5
 917 |   }
 918 | }
 919 | layer {
 920 |   name: "conv4_2/sep/scale"
 921 |   type: "Scale"
 922 |   bottom: "conv4_2/sep"
 923 |   top: "conv4_2/sep"
 924 |   param {
 925 |     lr_mult: 1
 926 |     decay_mult: 0
 927 |   }
 928 |   param {
 929 |     lr_mult: 1
 930 |     decay_mult: 0
 931 |   }
 932 |   scale_param {
 933 |     filler {
 934 |       value: 1
 935 |     }
 936 |     bias_term: true
 937 |     bias_filler {
 938 |       value: 0
 939 |     }
 940 |   }
 941 | }
 942 | layer {
 943 |   name: "relu4_2/sep"
 944 |   type: "ReLU"
 945 |   bottom: "conv4_2/sep"
 946 |   top: "conv4_2/sep"
 947 | }
 948 | layer {
 949 |   name: "conv5_1/dw"
 950 |   type: "Convolution"
 951 |   bottom: "conv4_2/sep"
 952 |   top: "conv5_1/dw"
 953 |   param {
 954 |     lr_mult: 1
 955 |     decay_mult: 1
 956 |   }
 957 |   convolution_param {
 958 |     num_output: 512
 959 |     bias_term: false
 960 |     pad: 1
 961 |     kernel_size: 3
 962 |     group: 512
 963 |     engine: CAFFE
 964 |     stride: 1
 965 |     weight_filler {
 966 |       type: "msra"
 967 |     }
 968 |   }
 969 | }
 970 | layer {
 971 |   name: "conv5_1/dw/bn"
 972 |   type: "BatchNorm"
 973 |   bottom: "conv5_1/dw"
 974 |   top: "conv5_1/dw"
 975 |   param {
 976 |     lr_mult: 0
 977 |     decay_mult: 0
 978 |   }
 979 |   param {
 980 |     lr_mult: 0
 981 |     decay_mult: 0
 982 |   }
 983 |   param {
 984 |     lr_mult: 0
 985 |     decay_mult: 0
 986 |   }
 987 |   batch_norm_param {
 988 |     use_global_stats: true
 989 |     eps: 1e-5
 990 |   }
 991 | }
 992 | layer {
 993 |   name: "conv5_1/dw/scale"
 994 |   type: "Scale"
 995 |   bottom: "conv5_1/dw"
 996 |   top: "conv5_1/dw"
 997 |   param {
 998 |     lr_mult: 1
 999 |     decay_mult: 0
1000 |   }
1001 |   param {
1002 |     lr_mult: 1
1003 |     decay_mult: 0
1004 |   }
1005 |   scale_param {
1006 |     filler {
1007 |       value: 1
1008 |     }
1009 |     bias_term: true
1010 |     bias_filler {
1011 |       value: 0
1012 |     }
1013 |   }
1014 | }
1015 | layer {
1016 |   name: "relu5_1/dw"
1017 |   type: "ReLU"
1018 |   bottom: "conv5_1/dw"
1019 |   top: "conv5_1/dw"
1020 | }
1021 | layer {
1022 |   name: "conv5_1/sep"
1023 |   type: "Convolution"
1024 |   bottom: "conv5_1/dw"
1025 |   top: "conv5_1/sep"
1026 |   param {
1027 |     lr_mult: 1
1028 |     decay_mult: 1
1029 |   }
1030 |   convolution_param {
1031 |     num_output: 512
1032 |     bias_term: false
1033 |     pad: 0
1034 |     kernel_size: 1
1035 |     stride: 1
1036 |     weight_filler {
1037 |       type: "msra"
1038 |     }
1039 |   }
1040 | }
1041 | layer {
1042 |   name: "conv5_1/sep/bn"
1043 |   type: "BatchNorm"
1044 |   bottom: "conv5_1/sep"
1045 |   top: "conv5_1/sep"
1046 |   param {
1047 |     lr_mult: 0
1048 |     decay_mult: 0
1049 |   }
1050 |   param {
1051 |     lr_mult: 0
1052 |     decay_mult: 0
1053 |   }
1054 |   param {
1055 |     lr_mult: 0
1056 |     decay_mult: 0
1057 |   }
1058 |   batch_norm_param {
1059 |     use_global_stats: true
1060 |     eps: 1e-5
1061 |   }
1062 | }
1063 | layer {
1064 |   name: "conv5_1/sep/scale"
1065 |   type: "Scale"
1066 |   bottom: "conv5_1/sep"
1067 |   top: "conv5_1/sep"
1068 |   param {
1069 |     lr_mult: 1
1070 |     decay_mult: 0
1071 |   }
1072 |   param {
1073 |     lr_mult: 1
1074 |     decay_mult: 0
1075 |   }
1076 |   scale_param {
1077 |     filler {
1078 |       value: 1
1079 |     }
1080 |     bias_term: true
1081 |     bias_filler {
1082 |       value: 0
1083 |     }
1084 |   }
1085 | }
1086 | layer {
1087 |   name: "relu5_1/sep"
1088 |   type: "ReLU"
1089 |   bottom: "conv5_1/sep"
1090 |   top: "conv5_1/sep"
1091 | }
1092 | layer {
1093 |   name: "conv5_2/dw"
1094 |   type: "Convolution"
1095 |   bottom: "conv5_1/sep"
1096 |   top: "conv5_2/dw"
1097 |   param {
1098 |     lr_mult: 1
1099 |     decay_mult: 1
1100 |   }
1101 |   convolution_param {
1102 |     num_output: 512
1103 |     bias_term: false
1104 |     pad: 1
1105 |     kernel_size: 3
1106 |     group: 512
1107 |     engine: CAFFE
1108 |     stride: 1
1109 |     weight_filler {
1110 |       type: "msra"
1111 |     }
1112 |   }
1113 | }
1114 | layer {
1115 |   name: "conv5_2/dw/bn"
1116 |   type: "BatchNorm"
1117 |   bottom: "conv5_2/dw"
1118 |   top: "conv5_2/dw"
1119 |   param {
1120 |     lr_mult: 0
1121 |     decay_mult: 0
1122 |   }
1123 |   param {
1124 |     lr_mult: 0
1125 |     decay_mult: 0
1126 |   }
1127 |   param {
1128 |     lr_mult: 0
1129 |     decay_mult: 0
1130 |   }
1131 |   batch_norm_param {
1132 |     use_global_stats: true
1133 |     eps: 1e-5
1134 |   }
1135 | }
1136 | layer {
1137 |   name: "conv5_2/dw/scale"
1138 |   type: "Scale"
1139 |   bottom: "conv5_2/dw"
1140 |   top: "conv5_2/dw"
1141 |   param {
1142 |     lr_mult: 1
1143 |     decay_mult: 0
1144 |   }
1145 |   param {
1146 |     lr_mult: 1
1147 |     decay_mult: 0
1148 |   }
1149 |   scale_param {
1150 |     filler {
1151 |       value: 1
1152 |     }
1153 |     bias_term: true
1154 |     bias_filler {
1155 |       value: 0
1156 |     }
1157 |   }
1158 | }
1159 | layer {
1160 |   name: "relu5_2/dw"
1161 |   type: "ReLU"
1162 |   bottom: "conv5_2/dw"
1163 |   top: "conv5_2/dw"
1164 | }
1165 | layer {
1166 |   name: "conv5_2/sep"
1167 |   type: "Convolution"
1168 |   bottom: "conv5_2/dw"
1169 |   top: "conv5_2/sep"
1170 |   param {
1171 |     lr_mult: 1
1172 |     decay_mult: 1
1173 |   }
1174 |   convolution_param {
1175 |     num_output: 512
1176 |     bias_term: false
1177 |     pad: 0
1178 |     kernel_size: 1
1179 |     stride: 1
1180 |     weight_filler {
1181 |       type: "msra"
1182 |     }
1183 |   }
1184 | }
1185 | layer {
1186 |   name: "conv5_2/sep/bn"
1187 |   type: "BatchNorm"
1188 |   bottom: "conv5_2/sep"
1189 |   top: "conv5_2/sep"
1190 |   param {
1191 |     lr_mult: 0
1192 |     decay_mult: 0
1193 |   }
1194 |   param {
1195 |     lr_mult: 0
1196 |     decay_mult: 0
1197 |   }
1198 |   param {
1199 |     lr_mult: 0
1200 |     decay_mult: 0
1201 |   }
1202 |   batch_norm_param {
1203 |     use_global_stats: true
1204 |     eps: 1e-5
1205 |   }
1206 | }
1207 | layer {
1208 |   name: "conv5_2/sep/scale"
1209 |   type: "Scale"
1210 |   bottom: "conv5_2/sep"
1211 |   top: "conv5_2/sep"
1212 |   param {
1213 |     lr_mult: 1
1214 |     decay_mult: 0
1215 |   }
1216 |   param {
1217 |     lr_mult: 1
1218 |     decay_mult: 0
1219 |   }
1220 |   scale_param {
1221 |     filler {
1222 |       value: 1
1223 |     }
1224 |     bias_term: true
1225 |     bias_filler {
1226 |       value: 0
1227 |     }
1228 |   }
1229 | }
1230 | layer {
1231 |   name: "relu5_2/sep"
1232 |   type: "ReLU"
1233 |   bottom: "conv5_2/sep"
1234 |   top: "conv5_2/sep"
1235 | }
1236 | layer {
1237 |   name: "conv5_3/dw"
1238 |   type: "Convolution"
1239 |   bottom: "conv5_2/sep"
1240 |   top: "conv5_3/dw"
1241 |   param {
1242 |     lr_mult: 1
1243 |     decay_mult: 1
1244 |   }
1245 |   convolution_param {
1246 |     num_output: 512
1247 |     bias_term: false
1248 |     pad: 1
1249 |     kernel_size: 3
1250 |     group: 512
1251 |     engine: CAFFE
1252 |     stride: 1
1253 |     weight_filler {
1254 |       type: "msra"
1255 |     }
1256 |   }
1257 | }
1258 | layer {
1259 |   name: "conv5_3/dw/bn"
1260 |   type: "BatchNorm"
1261 |   bottom: "conv5_3/dw"
1262 |   top: "conv5_3/dw"
1263 |   param {
1264 |     lr_mult: 0
1265 |     decay_mult: 0
1266 |   }
1267 |   param {
1268 |     lr_mult: 0
1269 |     decay_mult: 0
1270 |   }
1271 |   param {
1272 |     lr_mult: 0
1273 |     decay_mult: 0
1274 |   }
1275 |   batch_norm_param {
1276 |     use_global_stats: true
1277 |     eps: 1e-5
1278 |   }
1279 | }
1280 | layer {
1281 |   name: "conv5_3/dw/scale"
1282 |   type: "Scale"
1283 |   bottom: "conv5_3/dw"
1284 |   top: "conv5_3/dw"
1285 |   param {
1286 |     lr_mult: 1
1287 |     decay_mult: 0
1288 |   }
1289 |   param {
1290 |     lr_mult: 1
1291 |     decay_mult: 0
1292 |   }
1293 |   scale_param {
1294 |     filler {
1295 |       value: 1
1296 |     }
1297 |     bias_term: true
1298 |     bias_filler {
1299 |       value: 0
1300 |     }
1301 |   }
1302 | }
1303 | layer {
1304 |   name: "relu5_3/dw"
1305 |   type: "ReLU"
1306 |   bottom: "conv5_3/dw"
1307 |   top: "conv5_3/dw"
1308 | }
1309 | layer {
1310 |   name: "conv5_3/sep"
1311 |   type: "Convolution"
1312 |   bottom: "conv5_3/dw"
1313 |   top: "conv5_3/sep"
1314 |   param {
1315 |     lr_mult: 1
1316 |     decay_mult: 1
1317 |   }
1318 |   convolution_param {
1319 |     num_output: 512
1320 |     bias_term: false
1321 |     pad: 0
1322 |     kernel_size: 1
1323 |     stride: 1
1324 |     weight_filler {
1325 |       type: "msra"
1326 |     }
1327 |   }
1328 | }
1329 | layer {
1330 |   name: "conv5_3/sep/bn"
1331 |   type: "BatchNorm"
1332 |   bottom: "conv5_3/sep"
1333 |   top: "conv5_3/sep"
1334 |   param {
1335 |     lr_mult: 0
1336 |     decay_mult: 0
1337 |   }
1338 |   param {
1339 |     lr_mult: 0
1340 |     decay_mult: 0
1341 |   }
1342 |   param {
1343 |     lr_mult: 0
1344 |     decay_mult: 0
1345 |   }
1346 |   batch_norm_param {
1347 |     use_global_stats: true
1348 |     eps: 1e-5
1349 |   }
1350 | }
1351 | layer {
1352 |   name: "conv5_3/sep/scale"
1353 |   type: "Scale"
1354 |   bottom: "conv5_3/sep"
1355 |   top: "conv5_3/sep"
1356 |   param {
1357 |     lr_mult: 1
1358 |     decay_mult: 0
1359 |   }
1360 |   param {
1361 |     lr_mult: 1
1362 |     decay_mult: 0
1363 |   }
1364 |   scale_param {
1365 |     filler {
1366 |       value: 1
1367 |     }
1368 |     bias_term: true
1369 |     bias_filler {
1370 |       value: 0
1371 |     }
1372 |   }
1373 | }
1374 | layer {
1375 |   name: "relu5_3/sep"
1376 |   type: "ReLU"
1377 |   bottom: "conv5_3/sep"
1378 |   top: "conv5_3/sep"
1379 | }
1380 | layer {
1381 |   name: "conv5_4/dw"
1382 |   type: "Convolution"
1383 |   bottom: "conv5_3/sep"
1384 |   top: "conv5_4/dw"
1385 |   param {
1386 |     lr_mult: 1
1387 |     decay_mult: 1
1388 |   }
1389 |   convolution_param {
1390 |     num_output: 512
1391 |     bias_term: false
1392 |     pad: 1
1393 |     kernel_size: 3
1394 |     group: 512
1395 |     engine: CAFFE
1396 |     stride: 1
1397 |     weight_filler {
1398 |       type: "msra"
1399 |     }
1400 |   }
1401 | }
1402 | layer {
1403 |   name: "conv5_4/dw/bn"
1404 |   type: "BatchNorm"
1405 |   bottom: "conv5_4/dw"
1406 |   top: "conv5_4/dw"
1407 |   param {
1408 |     lr_mult: 0
1409 |     decay_mult: 0
1410 |   }
1411 |   param {
1412 |     lr_mult: 0
1413 |     decay_mult: 0
1414 |   }
1415 |   param {
1416 |     lr_mult: 0
1417 |     decay_mult: 0
1418 |   }
1419 |   batch_norm_param {
1420 |     use_global_stats: true
1421 |     eps: 1e-5
1422 |   }
1423 | }
1424 | layer {
1425 |   name: "conv5_4/dw/scale"
1426 |   type: "Scale"
1427 |   bottom: "conv5_4/dw"
1428 |   top: "conv5_4/dw"
1429 |   param {
1430 |     lr_mult: 1
1431 |     decay_mult: 0
1432 |   }
1433 |   param {
1434 |     lr_mult: 1
1435 |     decay_mult: 0
1436 |   }
1437 |   scale_param {
1438 |     filler {
1439 |       value: 1
1440 |     }
1441 |     bias_term: true
1442 |     bias_filler {
1443 |       value: 0
1444 |     }
1445 |   }
1446 | }
1447 | layer {
1448 |   name: "relu5_4/dw"
1449 |   type: "ReLU"
1450 |   bottom: "conv5_4/dw"
1451 |   top: "conv5_4/dw"
1452 | }
1453 | layer {
1454 |   name: "conv5_4/sep"
1455 |   type: "Convolution"
1456 |   bottom: "conv5_4/dw"
1457 |   top: "conv5_4/sep"
1458 |   param {
1459 |     lr_mult: 1
1460 |     decay_mult: 1
1461 |   }
1462 |   convolution_param {
1463 |     num_output: 512
1464 |     bias_term: false
1465 |     pad: 0
1466 |     kernel_size: 1
1467 |     stride: 1
1468 |     weight_filler {
1469 |       type: "msra"
1470 |     }
1471 |   }
1472 | }
1473 | layer {
1474 |   name: "conv5_4/sep/bn"
1475 |   type: "BatchNorm"
1476 |   bottom: "conv5_4/sep"
1477 |   top: "conv5_4/sep"
1478 |   param {
1479 |     lr_mult: 0
1480 |     decay_mult: 0
1481 |   }
1482 |   param {
1483 |     lr_mult: 0
1484 |     decay_mult: 0
1485 |   }
1486 |   param {
1487 |     lr_mult: 0
1488 |     decay_mult: 0
1489 |   }
1490 |   batch_norm_param {
1491 |     use_global_stats: true
1492 |     eps: 1e-5
1493 |   }
1494 | }
1495 | layer {
1496 |   name: "conv5_4/sep/scale"
1497 |   type: "Scale"
1498 |   bottom: "conv5_4/sep"
1499 |   top: "conv5_4/sep"
1500 |   param {
1501 |     lr_mult: 1
1502 |     decay_mult: 0
1503 |   }
1504 |   param {
1505 |     lr_mult: 1
1506 |     decay_mult: 0
1507 |   }
1508 |   scale_param {
1509 |     filler {
1510 |       value: 1
1511 |     }
1512 |     bias_term: true
1513 |     bias_filler {
1514 |       value: 0
1515 |     }
1516 |   }
1517 | }
1518 | layer {
1519 |   name: "relu5_4/sep"
1520 |   type: "ReLU"
1521 |   bottom: "conv5_4/sep"
1522 |   top: "conv5_4/sep"
1523 | }
1524 | layer {
1525 |   name: "conv5_5/dw"
1526 |   type: "Convolution"
1527 |   bottom: "conv5_4/sep"
1528 |   top: "conv5_5/dw"
1529 |   param {
1530 |     lr_mult: 1
1531 |     decay_mult: 1
1532 |   }
1533 |   convolution_param {
1534 |     num_output: 512
1535 |     bias_term: false
1536 |     pad: 1
1537 |     kernel_size: 3
1538 |     group: 512
1539 |     engine: CAFFE
1540 |     stride: 1
1541 |     weight_filler {
1542 |       type: "msra"
1543 |     }
1544 |   }
1545 | }
1546 | layer {
1547 |   name: "conv5_5/dw/bn"
1548 |   type: "BatchNorm"
1549 |   bottom: "conv5_5/dw"
1550 |   top: "conv5_5/dw"
1551 |   param {
1552 |     lr_mult: 0
1553 |     decay_mult: 0
1554 |   }
1555 |   param {
1556 |     lr_mult: 0
1557 |     decay_mult: 0
1558 |   }
1559 |   param {
1560 |     lr_mult: 0
1561 |     decay_mult: 0
1562 |   }
1563 |   batch_norm_param {
1564 |     use_global_stats: true
1565 |     eps: 1e-5
1566 |   }
1567 | }
1568 | layer {
1569 |   name: "conv5_5/dw/scale"
1570 |   type: "Scale"
1571 |   bottom: "conv5_5/dw"
1572 |   top: "conv5_5/dw"
1573 |   param {
1574 |     lr_mult: 1
1575 |     decay_mult: 0
1576 |   }
1577 |   param {
1578 |     lr_mult: 1
1579 |     decay_mult: 0
1580 |   }
1581 |   scale_param {
1582 |     filler {
1583 |       value: 1
1584 |     }
1585 |     bias_term: true
1586 |     bias_filler {
1587 |       value: 0
1588 |     }
1589 |   }
1590 | }
1591 | layer {
1592 |   name: "relu5_5/dw"
1593 |   type: "ReLU"
1594 |   bottom: "conv5_5/dw"
1595 |   top: "conv5_5/dw"
1596 | }
1597 | layer {
1598 |   name: "conv5_5/sep"
1599 |   type: "Convolution"
1600 |   bottom: "conv5_5/dw"
1601 |   top: "conv5_5/sep"
1602 |   param {
1603 |     lr_mult: 1
1604 |     decay_mult: 1
1605 |   }
1606 |   convolution_param {
1607 |     num_output: 512
1608 |     bias_term: false
1609 |     pad: 0
1610 |     kernel_size: 1
1611 |     stride: 1
1612 |     weight_filler {
1613 |       type: "msra"
1614 |     }
1615 |   }
1616 | }
1617 | layer {
1618 |   name: "conv5_5/sep/bn"
1619 |   type: "BatchNorm"
1620 |   bottom: "conv5_5/sep"
1621 |   top: "conv5_5/sep"
1622 |   param {
1623 |     lr_mult: 0
1624 |     decay_mult: 0
1625 |   }
1626 |   param {
1627 |     lr_mult: 0
1628 |     decay_mult: 0
1629 |   }
1630 |   param {
1631 |     lr_mult: 0
1632 |     decay_mult: 0
1633 |   }
1634 |   batch_norm_param {
1635 |     use_global_stats: true
1636 |     eps: 1e-5
1637 |   }
1638 | }
1639 | layer {
1640 |   name: "conv5_5/sep/scale"
1641 |   type: "Scale"
1642 |   bottom: "conv5_5/sep"
1643 |   top: "conv5_5/sep"
1644 |   param {
1645 |     lr_mult: 1
1646 |     decay_mult: 0
1647 |   }
1648 |   param {
1649 |     lr_mult: 1
1650 |     decay_mult: 0
1651 |   }
1652 |   scale_param {
1653 |     filler {
1654 |       value: 1
1655 |     }
1656 |     bias_term: true
1657 |     bias_filler {
1658 |       value: 0
1659 |     }
1660 |   }
1661 | }
1662 | layer {
1663 |   name: "relu5_5/sep"
1664 |   type: "ReLU"
1665 |   bottom: "conv5_5/sep"
1666 |   top: "conv5_5/sep"
1667 | }
1668 | layer {
1669 |   name: "conv5_6/dw"
1670 |   type: "Convolution"
1671 |   bottom: "conv5_5/sep"
1672 |   top: "conv5_6/dw"
1673 |   param {
1674 |     lr_mult: 1
1675 |     decay_mult: 1
1676 |   }
1677 |   convolution_param {
1678 |     num_output: 512
1679 |     bias_term: false
1680 |     pad: 1
1681 |     kernel_size: 3
1682 |     group: 512
1683 |     engine: CAFFE
1684 |     stride: 2
1685 |     weight_filler {
1686 |       type: "msra"
1687 |     }
1688 |   }
1689 | }
1690 | layer {
1691 |   name: "conv5_6/dw/bn"
1692 |   type: "BatchNorm"
1693 |   bottom: "conv5_6/dw"
1694 |   top: "conv5_6/dw"
1695 |   param {
1696 |     lr_mult: 0
1697 |     decay_mult: 0
1698 |   }
1699 |   param {
1700 |     lr_mult: 0
1701 |     decay_mult: 0
1702 |   }
1703 |   param {
1704 |     lr_mult: 0
1705 |     decay_mult: 0
1706 |   }
1707 |   batch_norm_param {
1708 |     use_global_stats: true
1709 |     eps: 1e-5
1710 |   }
1711 | }
1712 | layer {
1713 |   name: "conv5_6/dw/scale"
1714 |   type: "Scale"
1715 |   bottom: "conv5_6/dw"
1716 |   top: "conv5_6/dw"
1717 |   param {
1718 |     lr_mult: 1
1719 |     decay_mult: 0
1720 |   }
1721 |   param {
1722 |     lr_mult: 1
1723 |     decay_mult: 0
1724 |   }
1725 |   scale_param {
1726 |     filler {
1727 |       value: 1
1728 |     }
1729 |     bias_term: true
1730 |     bias_filler {
1731 |       value: 0
1732 |     }
1733 |   }
1734 | }
1735 | layer {
1736 |   name: "relu5_6/dw"
1737 |   type: "ReLU"
1738 |   bottom: "conv5_6/dw"
1739 |   top: "conv5_6/dw"
1740 | }
1741 | layer {
1742 |   name: "conv5_6/sep"
1743 |   type: "Convolution"
1744 |   bottom: "conv5_6/dw"
1745 |   top: "conv5_6/sep"
1746 |   param {
1747 |     lr_mult: 1
1748 |     decay_mult: 1
1749 |   }
1750 |   convolution_param {
1751 |     num_output: 1024
1752 |     bias_term: false
1753 |     pad: 0
1754 |     kernel_size: 1
1755 |     stride: 1
1756 |     weight_filler {
1757 |       type: "msra"
1758 |     }
1759 |   }
1760 | }
1761 | layer {
1762 |   name: "conv5_6/sep/bn"
1763 |   type: "BatchNorm"
1764 |   bottom: "conv5_6/sep"
1765 |   top: "conv5_6/sep"
1766 |   param {
1767 |     lr_mult: 0
1768 |     decay_mult: 0
1769 |   }
1770 |   param {
1771 |     lr_mult: 0
1772 |     decay_mult: 0
1773 |   }
1774 |   param {
1775 |     lr_mult: 0
1776 |     decay_mult: 0
1777 |   }
1778 |   batch_norm_param {
1779 |     use_global_stats: true
1780 |     eps: 1e-5
1781 |   }
1782 | }
1783 | layer {
1784 |   name: "conv5_6/sep/scale"
1785 |   type: "Scale"
1786 |   bottom: "conv5_6/sep"
1787 |   top: "conv5_6/sep"
1788 |   param {
1789 |     lr_mult: 1
1790 |     decay_mult: 0
1791 |   }
1792 |   param {
1793 |     lr_mult: 1
1794 |     decay_mult: 0
1795 |   }
1796 |   scale_param {
1797 |     filler {
1798 |       value: 1
1799 |     }
1800 |     bias_term: true
1801 |     bias_filler {
1802 |       value: 0
1803 |     }
1804 |   }
1805 | }
1806 | layer {
1807 |   name: "relu5_6/sep"
1808 |   type: "ReLU"
1809 |   bottom: "conv5_6/sep"
1810 |   top: "conv5_6/sep"
1811 | }
1812 | layer {
1813 |   name: "conv6/dw"
1814 |   type: "Convolution"
1815 |   bottom: "conv5_6/sep"
1816 |   top: "conv6/dw"
1817 |   param {
1818 |     lr_mult: 1
1819 |     decay_mult: 1
1820 |   }
1821 |   convolution_param {
1822 |     num_output: 1024
1823 |     bias_term: false
1824 |     pad: 1
1825 |     kernel_size: 3
1826 |     group: 1024
1827 |     engine: CAFFE
1828 |     stride: 1
1829 |     weight_filler {
1830 |       type: "msra"
1831 |     }
1832 |   }
1833 | }
1834 | layer {
1835 |   name: "conv6/dw/bn"
1836 |   type: "BatchNorm"
1837 |   bottom: "conv6/dw"
1838 |   top: "conv6/dw"
1839 |   param {
1840 |     lr_mult: 0
1841 |     decay_mult: 0
1842 |   }
1843 |   param {
1844 |     lr_mult: 0
1845 |     decay_mult: 0
1846 |   }
1847 |   param {
1848 |     lr_mult: 0
1849 |     decay_mult: 0
1850 |   }
1851 |   batch_norm_param {
1852 |     use_global_stats: true
1853 |     eps: 1e-5
1854 |   }
1855 | }
1856 | layer {
1857 |   name: "conv6/dw/scale"
1858 |   type: "Scale"
1859 |   bottom: "conv6/dw"
1860 |   top: "conv6/dw"
1861 |   param {
1862 |     lr_mult: 1
1863 |     decay_mult: 0
1864 |   }
1865 |   param {
1866 |     lr_mult: 1
1867 |     decay_mult: 0
1868 |   }
1869 |   scale_param {
1870 |     filler {
1871 |       value: 1
1872 |     }
1873 |     bias_term: true
1874 |     bias_filler {
1875 |       value: 0
1876 |     }
1877 |   }
1878 | }
1879 | layer {
1880 |   name: "relu6/dw"
1881 |   type: "ReLU"
1882 |   bottom: "conv6/dw"
1883 |   top: "conv6/dw"
1884 | }
1885 | layer {
1886 |   name: "conv6/sep"
1887 |   type: "Convolution"
1888 |   bottom: "conv6/dw"
1889 |   top: "conv6/sep"
1890 |   param {
1891 |     lr_mult: 1
1892 |     decay_mult: 1
1893 |   }
1894 |   convolution_param {
1895 |     num_output: 1024
1896 |     bias_term: false
1897 |     pad: 0
1898 |     kernel_size: 1
1899 |     stride: 1
1900 |     weight_filler {
1901 |       type: "msra"
1902 |     }
1903 |   }
1904 | }
1905 | layer {
1906 |   name: "conv6/sep/bn"
1907 |   type: "BatchNorm"
1908 |   bottom: "conv6/sep"
1909 |   top: "conv6/sep"
1910 |   param {
1911 |     lr_mult: 0
1912 |     decay_mult: 0
1913 |   }
1914 |   param {
1915 |     lr_mult: 0
1916 |     decay_mult: 0
1917 |   }
1918 |   param {
1919 |     lr_mult: 0
1920 |     decay_mult: 0
1921 |   }
1922 |   batch_norm_param {
1923 |     use_global_stats: true
1924 |     eps: 1e-5
1925 |   }
1926 | }
1927 | layer {
1928 |   name: "conv6/sep/scale"
1929 |   type: "Scale"
1930 |   bottom: "conv6/sep"
1931 |   top: "conv6/sep"
1932 |   param {
1933 |     lr_mult: 1
1934 |     decay_mult: 0
1935 |   }
1936 |   param {
1937 |     lr_mult: 1
1938 |     decay_mult: 0
1939 |   }
1940 |   scale_param {
1941 |     filler {
1942 |       value: 1
1943 |     }
1944 |     bias_term: true
1945 |     bias_filler {
1946 |       value: 0
1947 |     }
1948 |   }
1949 | }
1950 | layer {
1951 |   name: "relu6/sep"
1952 |   type: "ReLU"
1953 |   bottom: "conv6/sep"
1954 |   top: "conv6/sep"
1955 | }
1956 | layer {
1957 |   name: "pool6"
1958 |   type: "Pooling"
1959 |   bottom: "conv6/sep"
1960 |   top: "pool6"
1961 |   pooling_param {
1962 |     pool: AVE
1963 |     global_pooling: true
1964 |   }
1965 | }
1966 | layer {
1967 |   name: "fc7"
1968 |   type: "Convolution"
1969 |   bottom: "pool6"
1970 |   top: "fc7"
1971 |   param {
1972 |     lr_mult: 1
1973 |     decay_mult: 1
1974 |   }
1975 |   param {
1976 |     lr_mult: 2
1977 |     decay_mult: 0
1978 |   }
1979 |   convolution_param {
1980 |     num_output: 1000
1981 |     kernel_size: 1
1982 |     weight_filler {
1983 |       type: "msra"
1984 |     }
1985 |     bias_filler {
1986 |       type: "constant"
1987 |       value: 0
1988 |     }
1989 |   }
1990 | }
1991 | layer {
1992 |   name: "prob"
1993 |   type: "Softmax"
1994 |   bottom: "fc7"
1995 |   top: "prob"
1996 | }
1997 | 


--------------------------------------------------------------------------------
/mobilenet_v2.caffemodel:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/shicai/MobileNet-Caffe/a0b0c46152e658559956158d7c2bf42bb029c09e/mobilenet_v2.caffemodel


--------------------------------------------------------------------------------
/mobilenet_v2_deploy.prototxt:
--------------------------------------------------------------------------------
   1 | name: "MOBILENET_V2"
   2 | #  transform_param {
   3 | #    scale: 0.017
   4 | #    mirror: false
   5 | #    crop_size: 224
   6 | #    mean_value: [103.94,116.78,123.68]
   7 | #  }
   8 | input: "data"
   9 | input_dim: 1
  10 | input_dim: 3
  11 | input_dim: 224
  12 | input_dim: 224
  13 | layer {
  14 |   name: "conv1"
  15 |   type: "Convolution"
  16 |   bottom: "data"
  17 |   top: "conv1"
  18 |   param {
  19 |     lr_mult: 1
  20 |     decay_mult: 1
  21 |   }
  22 |   convolution_param {
  23 |     num_output: 32
  24 |     bias_term: false
  25 |     pad: 1
  26 |     kernel_size: 3
  27 |     stride: 2
  28 |     weight_filler {
  29 |       type: "msra"
  30 |     }
  31 |   }
  32 | }
  33 | layer {
  34 |   name: "conv1/bn"
  35 |   type: "BatchNorm"
  36 |   bottom: "conv1"
  37 |   top: "conv1/bn"
  38 |   param {
  39 |     lr_mult: 0
  40 |     decay_mult: 0
  41 |   }
  42 |   param {
  43 |     lr_mult: 0
  44 |     decay_mult: 0
  45 |   }
  46 |   param {
  47 |     lr_mult: 0
  48 |     decay_mult: 0
  49 |   }
  50 |   batch_norm_param {
  51 |     use_global_stats: true
  52 |     eps: 1e-5
  53 |   }
  54 | }
  55 | layer {
  56 |   name: "conv1/scale"
  57 |   type: "Scale"
  58 |   bottom: "conv1/bn"
  59 |   top: "conv1/bn"
  60 |   param {
  61 |     lr_mult: 1
  62 |     decay_mult: 0
  63 |   }
  64 |   param {
  65 |     lr_mult: 1
  66 |     decay_mult: 0
  67 |   }
  68 |   scale_param {
  69 |     bias_term: true
  70 |   }
  71 | }
  72 | layer {
  73 |   name: "relu1"
  74 |   type: "ReLU"
  75 |   bottom: "conv1/bn"
  76 |   top: "conv1/bn"
  77 | }
  78 | layer {
  79 |   name: "conv2_1/expand"
  80 |   type: "Convolution"
  81 |   bottom: "conv1/bn"
  82 |   top: "conv2_1/expand"
  83 |   param {
  84 |     lr_mult: 1
  85 |     decay_mult: 1
  86 |   }
  87 |   convolution_param {
  88 |     num_output: 32
  89 |     bias_term: false
  90 |     kernel_size: 1
  91 |     weight_filler {
  92 |       type: "msra"
  93 |     }
  94 |   }
  95 | }
  96 | layer {
  97 |   name: "conv2_1/expand/bn"
  98 |   type: "BatchNorm"
  99 |   bottom: "conv2_1/expand"
 100 |   top: "conv2_1/expand/bn"
 101 |   param {
 102 |     lr_mult: 0
 103 |     decay_mult: 0
 104 |   }
 105 |   param {
 106 |     lr_mult: 0
 107 |     decay_mult: 0
 108 |   }
 109 |   param {
 110 |     lr_mult: 0
 111 |     decay_mult: 0
 112 |   }
 113 |   batch_norm_param {
 114 |     use_global_stats: true
 115 |     eps: 1e-5
 116 |   }
 117 | }
 118 | layer {
 119 |   name: "conv2_1/expand/scale"
 120 |   type: "Scale"
 121 |   bottom: "conv2_1/expand/bn"
 122 |   top: "conv2_1/expand/bn"
 123 |   param {
 124 |     lr_mult: 1
 125 |     decay_mult: 0
 126 |   }
 127 |   param {
 128 |     lr_mult: 1
 129 |     decay_mult: 0
 130 |   }
 131 |   scale_param {
 132 |     bias_term: true
 133 |   }
 134 | }
 135 | layer {
 136 |   name: "relu2_1/expand"
 137 |   type: "ReLU"
 138 |   bottom: "conv2_1/expand/bn"
 139 |   top: "conv2_1/expand/bn"
 140 | }
 141 | layer {
 142 |   name: "conv2_1/dwise"
 143 |   type: "Convolution"
 144 |   bottom: "conv2_1/expand/bn"
 145 |   top: "conv2_1/dwise"
 146 |   param {
 147 |     lr_mult: 1
 148 |     decay_mult: 1
 149 |   }
 150 |   convolution_param {
 151 |     num_output: 32
 152 |     bias_term: false
 153 |     pad: 1
 154 |     kernel_size: 3
 155 |     group: 32
 156 |     weight_filler {
 157 |       type: "msra"
 158 |     }
 159 |     engine: CAFFE
 160 |   }
 161 | }
 162 | layer {
 163 |   name: "conv2_1/dwise/bn"
 164 |   type: "BatchNorm"
 165 |   bottom: "conv2_1/dwise"
 166 |   top: "conv2_1/dwise/bn"
 167 |   param {
 168 |     lr_mult: 0
 169 |     decay_mult: 0
 170 |   }
 171 |   param {
 172 |     lr_mult: 0
 173 |     decay_mult: 0
 174 |   }
 175 |   param {
 176 |     lr_mult: 0
 177 |     decay_mult: 0
 178 |   }
 179 |   batch_norm_param {
 180 |     use_global_stats: true
 181 |     eps: 1e-5
 182 |   }
 183 | }
 184 | layer {
 185 |   name: "conv2_1/dwise/scale"
 186 |   type: "Scale"
 187 |   bottom: "conv2_1/dwise/bn"
 188 |   top: "conv2_1/dwise/bn"
 189 |   param {
 190 |     lr_mult: 1
 191 |     decay_mult: 0
 192 |   }
 193 |   param {
 194 |     lr_mult: 1
 195 |     decay_mult: 0
 196 |   }
 197 |   scale_param {
 198 |     bias_term: true
 199 |   }
 200 | }
 201 | layer {
 202 |   name: "relu2_1/dwise"
 203 |   type: "ReLU"
 204 |   bottom: "conv2_1/dwise/bn"
 205 |   top: "conv2_1/dwise/bn"
 206 | }
 207 | layer {
 208 |   name: "conv2_1/linear"
 209 |   type: "Convolution"
 210 |   bottom: "conv2_1/dwise/bn"
 211 |   top: "conv2_1/linear"
 212 |   param {
 213 |     lr_mult: 1
 214 |     decay_mult: 1
 215 |   }
 216 |   convolution_param {
 217 |     num_output: 16
 218 |     bias_term: false
 219 |     kernel_size: 1
 220 |     weight_filler {
 221 |       type: "msra"
 222 |     }
 223 |   }
 224 | }
 225 | layer {
 226 |   name: "conv2_1/linear/bn"
 227 |   type: "BatchNorm"
 228 |   bottom: "conv2_1/linear"
 229 |   top: "conv2_1/linear/bn"
 230 |   param {
 231 |     lr_mult: 0
 232 |     decay_mult: 0
 233 |   }
 234 |   param {
 235 |     lr_mult: 0
 236 |     decay_mult: 0
 237 |   }
 238 |   param {
 239 |     lr_mult: 0
 240 |     decay_mult: 0
 241 |   }
 242 |   batch_norm_param {
 243 |     use_global_stats: true
 244 |     eps: 1e-5
 245 |   }
 246 | }
 247 | layer {
 248 |   name: "conv2_1/linear/scale"
 249 |   type: "Scale"
 250 |   bottom: "conv2_1/linear/bn"
 251 |   top: "conv2_1/linear/bn"
 252 |   param {
 253 |     lr_mult: 1
 254 |     decay_mult: 0
 255 |   }
 256 |   param {
 257 |     lr_mult: 1
 258 |     decay_mult: 0
 259 |   }
 260 |   scale_param {
 261 |     bias_term: true
 262 |   }
 263 | }
 264 | layer {
 265 |   name: "conv2_2/expand"
 266 |   type: "Convolution"
 267 |   bottom: "conv2_1/linear/bn"
 268 |   top: "conv2_2/expand"
 269 |   param {
 270 |     lr_mult: 1
 271 |     decay_mult: 1
 272 |   }
 273 |   convolution_param {
 274 |     num_output: 96
 275 |     bias_term: false
 276 |     kernel_size: 1
 277 |     weight_filler {
 278 |       type: "msra"
 279 |     }
 280 |   }
 281 | }
 282 | layer {
 283 |   name: "conv2_2/expand/bn"
 284 |   type: "BatchNorm"
 285 |   bottom: "conv2_2/expand"
 286 |   top: "conv2_2/expand/bn"
 287 |   param {
 288 |     lr_mult: 0
 289 |     decay_mult: 0
 290 |   }
 291 |   param {
 292 |     lr_mult: 0
 293 |     decay_mult: 0
 294 |   }
 295 |   param {
 296 |     lr_mult: 0
 297 |     decay_mult: 0
 298 |   }
 299 |   batch_norm_param {
 300 |     use_global_stats: true
 301 |     eps: 1e-5
 302 |   }
 303 | }
 304 | layer {
 305 |   name: "conv2_2/expand/scale"
 306 |   type: "Scale"
 307 |   bottom: "conv2_2/expand/bn"
 308 |   top: "conv2_2/expand/bn"
 309 |   param {
 310 |     lr_mult: 1
 311 |     decay_mult: 0
 312 |   }
 313 |   param {
 314 |     lr_mult: 1
 315 |     decay_mult: 0
 316 |   }
 317 |   scale_param {
 318 |     bias_term: true
 319 |   }
 320 | }
 321 | layer {
 322 |   name: "relu2_2/expand"
 323 |   type: "ReLU"
 324 |   bottom: "conv2_2/expand/bn"
 325 |   top: "conv2_2/expand/bn"
 326 | }
 327 | layer {
 328 |   name: "conv2_2/dwise"
 329 |   type: "Convolution"
 330 |   bottom: "conv2_2/expand/bn"
 331 |   top: "conv2_2/dwise"
 332 |   param {
 333 |     lr_mult: 1
 334 |     decay_mult: 1
 335 |   }
 336 |   convolution_param {
 337 |     num_output: 96
 338 |     bias_term: false
 339 |     pad: 1
 340 |     kernel_size: 3
 341 |     group: 96
 342 |     stride: 2
 343 |     weight_filler {
 344 |       type: "msra"
 345 |     }
 346 |     engine: CAFFE
 347 |   }
 348 | }
 349 | layer {
 350 |   name: "conv2_2/dwise/bn"
 351 |   type: "BatchNorm"
 352 |   bottom: "conv2_2/dwise"
 353 |   top: "conv2_2/dwise/bn"
 354 |   param {
 355 |     lr_mult: 0
 356 |     decay_mult: 0
 357 |   }
 358 |   param {
 359 |     lr_mult: 0
 360 |     decay_mult: 0
 361 |   }
 362 |   param {
 363 |     lr_mult: 0
 364 |     decay_mult: 0
 365 |   }
 366 |   batch_norm_param {
 367 |     use_global_stats: true
 368 |     eps: 1e-5
 369 |   }
 370 | }
 371 | layer {
 372 |   name: "conv2_2/dwise/scale"
 373 |   type: "Scale"
 374 |   bottom: "conv2_2/dwise/bn"
 375 |   top: "conv2_2/dwise/bn"
 376 |   param {
 377 |     lr_mult: 1
 378 |     decay_mult: 0
 379 |   }
 380 |   param {
 381 |     lr_mult: 1
 382 |     decay_mult: 0
 383 |   }
 384 |   scale_param {
 385 |     bias_term: true
 386 |   }
 387 | }
 388 | layer {
 389 |   name: "relu2_2/dwise"
 390 |   type: "ReLU"
 391 |   bottom: "conv2_2/dwise/bn"
 392 |   top: "conv2_2/dwise/bn"
 393 | }
 394 | layer {
 395 |   name: "conv2_2/linear"
 396 |   type: "Convolution"
 397 |   bottom: "conv2_2/dwise/bn"
 398 |   top: "conv2_2/linear"
 399 |   param {
 400 |     lr_mult: 1
 401 |     decay_mult: 1
 402 |   }
 403 |   convolution_param {
 404 |     num_output: 24
 405 |     bias_term: false
 406 |     kernel_size: 1
 407 |     weight_filler {
 408 |       type: "msra"
 409 |     }
 410 |   }
 411 | }
 412 | layer {
 413 |   name: "conv2_2/linear/bn"
 414 |   type: "BatchNorm"
 415 |   bottom: "conv2_2/linear"
 416 |   top: "conv2_2/linear/bn"
 417 |   param {
 418 |     lr_mult: 0
 419 |     decay_mult: 0
 420 |   }
 421 |   param {
 422 |     lr_mult: 0
 423 |     decay_mult: 0
 424 |   }
 425 |   param {
 426 |     lr_mult: 0
 427 |     decay_mult: 0
 428 |   }
 429 |   batch_norm_param {
 430 |     use_global_stats: true
 431 |     eps: 1e-5
 432 |   }
 433 | }
 434 | layer {
 435 |   name: "conv2_2/linear/scale"
 436 |   type: "Scale"
 437 |   bottom: "conv2_2/linear/bn"
 438 |   top: "conv2_2/linear/bn"
 439 |   param {
 440 |     lr_mult: 1
 441 |     decay_mult: 0
 442 |   }
 443 |   param {
 444 |     lr_mult: 1
 445 |     decay_mult: 0
 446 |   }
 447 |   scale_param {
 448 |     bias_term: true
 449 |   }
 450 | }
 451 | layer {
 452 |   name: "conv3_1/expand"
 453 |   type: "Convolution"
 454 |   bottom: "conv2_2/linear/bn"
 455 |   top: "conv3_1/expand"
 456 |   param {
 457 |     lr_mult: 1
 458 |     decay_mult: 1
 459 |   }
 460 |   convolution_param {
 461 |     num_output: 144
 462 |     bias_term: false
 463 |     kernel_size: 1
 464 |     weight_filler {
 465 |       type: "msra"
 466 |     }
 467 |   }
 468 | }
 469 | layer {
 470 |   name: "conv3_1/expand/bn"
 471 |   type: "BatchNorm"
 472 |   bottom: "conv3_1/expand"
 473 |   top: "conv3_1/expand/bn"
 474 |   param {
 475 |     lr_mult: 0
 476 |     decay_mult: 0
 477 |   }
 478 |   param {
 479 |     lr_mult: 0
 480 |     decay_mult: 0
 481 |   }
 482 |   param {
 483 |     lr_mult: 0
 484 |     decay_mult: 0
 485 |   }
 486 |   batch_norm_param {
 487 |     use_global_stats: true
 488 |     eps: 1e-5
 489 |   }
 490 | }
 491 | layer {
 492 |   name: "conv3_1/expand/scale"
 493 |   type: "Scale"
 494 |   bottom: "conv3_1/expand/bn"
 495 |   top: "conv3_1/expand/bn"
 496 |   param {
 497 |     lr_mult: 1
 498 |     decay_mult: 0
 499 |   }
 500 |   param {
 501 |     lr_mult: 1
 502 |     decay_mult: 0
 503 |   }
 504 |   scale_param {
 505 |     bias_term: true
 506 |   }
 507 | }
 508 | layer {
 509 |   name: "relu3_1/expand"
 510 |   type: "ReLU"
 511 |   bottom: "conv3_1/expand/bn"
 512 |   top: "conv3_1/expand/bn"
 513 | }
 514 | layer {
 515 |   name: "conv3_1/dwise"
 516 |   type: "Convolution"
 517 |   bottom: "conv3_1/expand/bn"
 518 |   top: "conv3_1/dwise"
 519 |   param {
 520 |     lr_mult: 1
 521 |     decay_mult: 1
 522 |   }
 523 |   convolution_param {
 524 |     num_output: 144
 525 |     bias_term: false
 526 |     pad: 1
 527 |     kernel_size: 3
 528 |     group: 144
 529 |     weight_filler {
 530 |       type: "msra"
 531 |     }
 532 |     engine: CAFFE
 533 |   }
 534 | }
 535 | layer {
 536 |   name: "conv3_1/dwise/bn"
 537 |   type: "BatchNorm"
 538 |   bottom: "conv3_1/dwise"
 539 |   top: "conv3_1/dwise/bn"
 540 |   param {
 541 |     lr_mult: 0
 542 |     decay_mult: 0
 543 |   }
 544 |   param {
 545 |     lr_mult: 0
 546 |     decay_mult: 0
 547 |   }
 548 |   param {
 549 |     lr_mult: 0
 550 |     decay_mult: 0
 551 |   }
 552 |   batch_norm_param {
 553 |     use_global_stats: true
 554 |     eps: 1e-5
 555 |   }
 556 | }
 557 | layer {
 558 |   name: "conv3_1/dwise/scale"
 559 |   type: "Scale"
 560 |   bottom: "conv3_1/dwise/bn"
 561 |   top: "conv3_1/dwise/bn"
 562 |   param {
 563 |     lr_mult: 1
 564 |     decay_mult: 0
 565 |   }
 566 |   param {
 567 |     lr_mult: 1
 568 |     decay_mult: 0
 569 |   }
 570 |   scale_param {
 571 |     bias_term: true
 572 |   }
 573 | }
 574 | layer {
 575 |   name: "relu3_1/dwise"
 576 |   type: "ReLU"
 577 |   bottom: "conv3_1/dwise/bn"
 578 |   top: "conv3_1/dwise/bn"
 579 | }
 580 | layer {
 581 |   name: "conv3_1/linear"
 582 |   type: "Convolution"
 583 |   bottom: "conv3_1/dwise/bn"
 584 |   top: "conv3_1/linear"
 585 |   param {
 586 |     lr_mult: 1
 587 |     decay_mult: 1
 588 |   }
 589 |   convolution_param {
 590 |     num_output: 24
 591 |     bias_term: false
 592 |     kernel_size: 1
 593 |     weight_filler {
 594 |       type: "msra"
 595 |     }
 596 |   }
 597 | }
 598 | layer {
 599 |   name: "conv3_1/linear/bn"
 600 |   type: "BatchNorm"
 601 |   bottom: "conv3_1/linear"
 602 |   top: "conv3_1/linear/bn"
 603 |   param {
 604 |     lr_mult: 0
 605 |     decay_mult: 0
 606 |   }
 607 |   param {
 608 |     lr_mult: 0
 609 |     decay_mult: 0
 610 |   }
 611 |   param {
 612 |     lr_mult: 0
 613 |     decay_mult: 0
 614 |   }
 615 |   batch_norm_param {
 616 |     use_global_stats: true
 617 |     eps: 1e-5
 618 |   }
 619 | }
 620 | layer {
 621 |   name: "conv3_1/linear/scale"
 622 |   type: "Scale"
 623 |   bottom: "conv3_1/linear/bn"
 624 |   top: "conv3_1/linear/bn"
 625 |   param {
 626 |     lr_mult: 1
 627 |     decay_mult: 0
 628 |   }
 629 |   param {
 630 |     lr_mult: 1
 631 |     decay_mult: 0
 632 |   }
 633 |   scale_param {
 634 |     bias_term: true
 635 |   }
 636 | }
 637 | layer {
 638 |   name: "block_3_1"
 639 |   type: "Eltwise"
 640 |   bottom: "conv2_2/linear/bn"
 641 |   bottom: "conv3_1/linear/bn"
 642 |   top: "block_3_1"
 643 | }
 644 | layer {
 645 |   name: "conv3_2/expand"
 646 |   type: "Convolution"
 647 |   bottom: "block_3_1"
 648 |   top: "conv3_2/expand"
 649 |   param {
 650 |     lr_mult: 1
 651 |     decay_mult: 1
 652 |   }
 653 |   convolution_param {
 654 |     num_output: 144
 655 |     bias_term: false
 656 |     kernel_size: 1
 657 |     weight_filler {
 658 |       type: "msra"
 659 |     }
 660 |   }
 661 | }
 662 | layer {
 663 |   name: "conv3_2/expand/bn"
 664 |   type: "BatchNorm"
 665 |   bottom: "conv3_2/expand"
 666 |   top: "conv3_2/expand/bn"
 667 |   param {
 668 |     lr_mult: 0
 669 |     decay_mult: 0
 670 |   }
 671 |   param {
 672 |     lr_mult: 0
 673 |     decay_mult: 0
 674 |   }
 675 |   param {
 676 |     lr_mult: 0
 677 |     decay_mult: 0
 678 |   }
 679 |   batch_norm_param {
 680 |     use_global_stats: true
 681 |     eps: 1e-5
 682 |   }
 683 | }
 684 | layer {
 685 |   name: "conv3_2/expand/scale"
 686 |   type: "Scale"
 687 |   bottom: "conv3_2/expand/bn"
 688 |   top: "conv3_2/expand/bn"
 689 |   param {
 690 |     lr_mult: 1
 691 |     decay_mult: 0
 692 |   }
 693 |   param {
 694 |     lr_mult: 1
 695 |     decay_mult: 0
 696 |   }
 697 |   scale_param {
 698 |     bias_term: true
 699 |   }
 700 | }
 701 | layer {
 702 |   name: "relu3_2/expand"
 703 |   type: "ReLU"
 704 |   bottom: "conv3_2/expand/bn"
 705 |   top: "conv3_2/expand/bn"
 706 | }
 707 | layer {
 708 |   name: "conv3_2/dwise"
 709 |   type: "Convolution"
 710 |   bottom: "conv3_2/expand/bn"
 711 |   top: "conv3_2/dwise"
 712 |   param {
 713 |     lr_mult: 1
 714 |     decay_mult: 1
 715 |   }
 716 |   convolution_param {
 717 |     num_output: 144
 718 |     bias_term: false
 719 |     pad: 1
 720 |     kernel_size: 3
 721 |     group: 144
 722 |     stride: 2
 723 |     weight_filler {
 724 |       type: "msra"
 725 |     }
 726 |     engine: CAFFE
 727 |   }
 728 | }
 729 | layer {
 730 |   name: "conv3_2/dwise/bn"
 731 |   type: "BatchNorm"
 732 |   bottom: "conv3_2/dwise"
 733 |   top: "conv3_2/dwise/bn"
 734 |   param {
 735 |     lr_mult: 0
 736 |     decay_mult: 0
 737 |   }
 738 |   param {
 739 |     lr_mult: 0
 740 |     decay_mult: 0
 741 |   }
 742 |   param {
 743 |     lr_mult: 0
 744 |     decay_mult: 0
 745 |   }
 746 |   batch_norm_param {
 747 |     use_global_stats: true
 748 |     eps: 1e-5
 749 |   }
 750 | }
 751 | layer {
 752 |   name: "conv3_2/dwise/scale"
 753 |   type: "Scale"
 754 |   bottom: "conv3_2/dwise/bn"
 755 |   top: "conv3_2/dwise/bn"
 756 |   param {
 757 |     lr_mult: 1
 758 |     decay_mult: 0
 759 |   }
 760 |   param {
 761 |     lr_mult: 1
 762 |     decay_mult: 0
 763 |   }
 764 |   scale_param {
 765 |     bias_term: true
 766 |   }
 767 | }
 768 | layer {
 769 |   name: "relu3_2/dwise"
 770 |   type: "ReLU"
 771 |   bottom: "conv3_2/dwise/bn"
 772 |   top: "conv3_2/dwise/bn"
 773 | }
 774 | layer {
 775 |   name: "conv3_2/linear"
 776 |   type: "Convolution"
 777 |   bottom: "conv3_2/dwise/bn"
 778 |   top: "conv3_2/linear"
 779 |   param {
 780 |     lr_mult: 1
 781 |     decay_mult: 1
 782 |   }
 783 |   convolution_param {
 784 |     num_output: 32
 785 |     bias_term: false
 786 |     kernel_size: 1
 787 |     weight_filler {
 788 |       type: "msra"
 789 |     }
 790 |   }
 791 | }
 792 | layer {
 793 |   name: "conv3_2/linear/bn"
 794 |   type: "BatchNorm"
 795 |   bottom: "conv3_2/linear"
 796 |   top: "conv3_2/linear/bn"
 797 |   param {
 798 |     lr_mult: 0
 799 |     decay_mult: 0
 800 |   }
 801 |   param {
 802 |     lr_mult: 0
 803 |     decay_mult: 0
 804 |   }
 805 |   param {
 806 |     lr_mult: 0
 807 |     decay_mult: 0
 808 |   }
 809 |   batch_norm_param {
 810 |     use_global_stats: true
 811 |     eps: 1e-5
 812 |   }
 813 | }
 814 | layer {
 815 |   name: "conv3_2/linear/scale"
 816 |   type: "Scale"
 817 |   bottom: "conv3_2/linear/bn"
 818 |   top: "conv3_2/linear/bn"
 819 |   param {
 820 |     lr_mult: 1
 821 |     decay_mult: 0
 822 |   }
 823 |   param {
 824 |     lr_mult: 1
 825 |     decay_mult: 0
 826 |   }
 827 |   scale_param {
 828 |     bias_term: true
 829 |   }
 830 | }
 831 | layer {
 832 |   name: "conv4_1/expand"
 833 |   type: "Convolution"
 834 |   bottom: "conv3_2/linear/bn"
 835 |   top: "conv4_1/expand"
 836 |   param {
 837 |     lr_mult: 1
 838 |     decay_mult: 1
 839 |   }
 840 |   convolution_param {
 841 |     num_output: 192
 842 |     bias_term: false
 843 |     kernel_size: 1
 844 |     weight_filler {
 845 |       type: "msra"
 846 |     }
 847 |   }
 848 | }
 849 | layer {
 850 |   name: "conv4_1/expand/bn"
 851 |   type: "BatchNorm"
 852 |   bottom: "conv4_1/expand"
 853 |   top: "conv4_1/expand/bn"
 854 |   param {
 855 |     lr_mult: 0
 856 |     decay_mult: 0
 857 |   }
 858 |   param {
 859 |     lr_mult: 0
 860 |     decay_mult: 0
 861 |   }
 862 |   param {
 863 |     lr_mult: 0
 864 |     decay_mult: 0
 865 |   }
 866 |   batch_norm_param {
 867 |     use_global_stats: true
 868 |     eps: 1e-5
 869 |   }
 870 | }
 871 | layer {
 872 |   name: "conv4_1/expand/scale"
 873 |   type: "Scale"
 874 |   bottom: "conv4_1/expand/bn"
 875 |   top: "conv4_1/expand/bn"
 876 |   param {
 877 |     lr_mult: 1
 878 |     decay_mult: 0
 879 |   }
 880 |   param {
 881 |     lr_mult: 1
 882 |     decay_mult: 0
 883 |   }
 884 |   scale_param {
 885 |     bias_term: true
 886 |   }
 887 | }
 888 | layer {
 889 |   name: "relu4_1/expand"
 890 |   type: "ReLU"
 891 |   bottom: "conv4_1/expand/bn"
 892 |   top: "conv4_1/expand/bn"
 893 | }
 894 | layer {
 895 |   name: "conv4_1/dwise"
 896 |   type: "Convolution"
 897 |   bottom: "conv4_1/expand/bn"
 898 |   top: "conv4_1/dwise"
 899 |   param {
 900 |     lr_mult: 1
 901 |     decay_mult: 1
 902 |   }
 903 |   convolution_param {
 904 |     num_output: 192
 905 |     bias_term: false
 906 |     pad: 1
 907 |     kernel_size: 3
 908 |     group: 192
 909 |     weight_filler {
 910 |       type: "msra"
 911 |     }
 912 |     engine: CAFFE
 913 |   }
 914 | }
 915 | layer {
 916 |   name: "conv4_1/dwise/bn"
 917 |   type: "BatchNorm"
 918 |   bottom: "conv4_1/dwise"
 919 |   top: "conv4_1/dwise/bn"
 920 |   param {
 921 |     lr_mult: 0
 922 |     decay_mult: 0
 923 |   }
 924 |   param {
 925 |     lr_mult: 0
 926 |     decay_mult: 0
 927 |   }
 928 |   param {
 929 |     lr_mult: 0
 930 |     decay_mult: 0
 931 |   }
 932 |   batch_norm_param {
 933 |     use_global_stats: true
 934 |     eps: 1e-5
 935 |   }
 936 | }
 937 | layer {
 938 |   name: "conv4_1/dwise/scale"
 939 |   type: "Scale"
 940 |   bottom: "conv4_1/dwise/bn"
 941 |   top: "conv4_1/dwise/bn"
 942 |   param {
 943 |     lr_mult: 1
 944 |     decay_mult: 0
 945 |   }
 946 |   param {
 947 |     lr_mult: 1
 948 |     decay_mult: 0
 949 |   }
 950 |   scale_param {
 951 |     bias_term: true
 952 |   }
 953 | }
 954 | layer {
 955 |   name: "relu4_1/dwise"
 956 |   type: "ReLU"
 957 |   bottom: "conv4_1/dwise/bn"
 958 |   top: "conv4_1/dwise/bn"
 959 | }
 960 | layer {
 961 |   name: "conv4_1/linear"
 962 |   type: "Convolution"
 963 |   bottom: "conv4_1/dwise/bn"
 964 |   top: "conv4_1/linear"
 965 |   param {
 966 |     lr_mult: 1
 967 |     decay_mult: 1
 968 |   }
 969 |   convolution_param {
 970 |     num_output: 32
 971 |     bias_term: false
 972 |     kernel_size: 1
 973 |     weight_filler {
 974 |       type: "msra"
 975 |     }
 976 |   }
 977 | }
 978 | layer {
 979 |   name: "conv4_1/linear/bn"
 980 |   type: "BatchNorm"
 981 |   bottom: "conv4_1/linear"
 982 |   top: "conv4_1/linear/bn"
 983 |   param {
 984 |     lr_mult: 0
 985 |     decay_mult: 0
 986 |   }
 987 |   param {
 988 |     lr_mult: 0
 989 |     decay_mult: 0
 990 |   }
 991 |   param {
 992 |     lr_mult: 0
 993 |     decay_mult: 0
 994 |   }
 995 |   batch_norm_param {
 996 |     use_global_stats: true
 997 |     eps: 1e-5
 998 |   }
 999 | }
1000 | layer {
1001 |   name: "conv4_1/linear/scale"
1002 |   type: "Scale"
1003 |   bottom: "conv4_1/linear/bn"
1004 |   top: "conv4_1/linear/bn"
1005 |   param {
1006 |     lr_mult: 1
1007 |     decay_mult: 0
1008 |   }
1009 |   param {
1010 |     lr_mult: 1
1011 |     decay_mult: 0
1012 |   }
1013 |   scale_param {
1014 |     bias_term: true
1015 |   }
1016 | }
1017 | layer {
1018 |   name: "block_4_1"
1019 |   type: "Eltwise"
1020 |   bottom: "conv3_2/linear/bn"
1021 |   bottom: "conv4_1/linear/bn"
1022 |   top: "block_4_1"
1023 | }
1024 | layer {
1025 |   name: "conv4_2/expand"
1026 |   type: "Convolution"
1027 |   bottom: "block_4_1"
1028 |   top: "conv4_2/expand"
1029 |   param {
1030 |     lr_mult: 1
1031 |     decay_mult: 1
1032 |   }
1033 |   convolution_param {
1034 |     num_output: 192
1035 |     bias_term: false
1036 |     kernel_size: 1
1037 |     weight_filler {
1038 |       type: "msra"
1039 |     }
1040 |   }
1041 | }
1042 | layer {
1043 |   name: "conv4_2/expand/bn"
1044 |   type: "BatchNorm"
1045 |   bottom: "conv4_2/expand"
1046 |   top: "conv4_2/expand/bn"
1047 |   param {
1048 |     lr_mult: 0
1049 |     decay_mult: 0
1050 |   }
1051 |   param {
1052 |     lr_mult: 0
1053 |     decay_mult: 0
1054 |   }
1055 |   param {
1056 |     lr_mult: 0
1057 |     decay_mult: 0
1058 |   }
1059 |   batch_norm_param {
1060 |     use_global_stats: true
1061 |     eps: 1e-5
1062 |   }
1063 | }
1064 | layer {
1065 |   name: "conv4_2/expand/scale"
1066 |   type: "Scale"
1067 |   bottom: "conv4_2/expand/bn"
1068 |   top: "conv4_2/expand/bn"
1069 |   param {
1070 |     lr_mult: 1
1071 |     decay_mult: 0
1072 |   }
1073 |   param {
1074 |     lr_mult: 1
1075 |     decay_mult: 0
1076 |   }
1077 |   scale_param {
1078 |     bias_term: true
1079 |   }
1080 | }
1081 | layer {
1082 |   name: "relu4_2/expand"
1083 |   type: "ReLU"
1084 |   bottom: "conv4_2/expand/bn"
1085 |   top: "conv4_2/expand/bn"
1086 | }
1087 | layer {
1088 |   name: "conv4_2/dwise"
1089 |   type: "Convolution"
1090 |   bottom: "conv4_2/expand/bn"
1091 |   top: "conv4_2/dwise"
1092 |   param {
1093 |     lr_mult: 1
1094 |     decay_mult: 1
1095 |   }
1096 |   convolution_param {
1097 |     num_output: 192
1098 |     bias_term: false
1099 |     pad: 1
1100 |     kernel_size: 3
1101 |     group: 192
1102 |     weight_filler {
1103 |       type: "msra"
1104 |     }
1105 |     engine: CAFFE
1106 |   }
1107 | }
1108 | layer {
1109 |   name: "conv4_2/dwise/bn"
1110 |   type: "BatchNorm"
1111 |   bottom: "conv4_2/dwise"
1112 |   top: "conv4_2/dwise/bn"
1113 |   param {
1114 |     lr_mult: 0
1115 |     decay_mult: 0
1116 |   }
1117 |   param {
1118 |     lr_mult: 0
1119 |     decay_mult: 0
1120 |   }
1121 |   param {
1122 |     lr_mult: 0
1123 |     decay_mult: 0
1124 |   }
1125 |   batch_norm_param {
1126 |     use_global_stats: true
1127 |     eps: 1e-5
1128 |   }
1129 | }
1130 | layer {
1131 |   name: "conv4_2/dwise/scale"
1132 |   type: "Scale"
1133 |   bottom: "conv4_2/dwise/bn"
1134 |   top: "conv4_2/dwise/bn"
1135 |   param {
1136 |     lr_mult: 1
1137 |     decay_mult: 0
1138 |   }
1139 |   param {
1140 |     lr_mult: 1
1141 |     decay_mult: 0
1142 |   }
1143 |   scale_param {
1144 |     bias_term: true
1145 |   }
1146 | }
1147 | layer {
1148 |   name: "relu4_2/dwise"
1149 |   type: "ReLU"
1150 |   bottom: "conv4_2/dwise/bn"
1151 |   top: "conv4_2/dwise/bn"
1152 | }
1153 | layer {
1154 |   name: "conv4_2/linear"
1155 |   type: "Convolution"
1156 |   bottom: "conv4_2/dwise/bn"
1157 |   top: "conv4_2/linear"
1158 |   param {
1159 |     lr_mult: 1
1160 |     decay_mult: 1
1161 |   }
1162 |   convolution_param {
1163 |     num_output: 32
1164 |     bias_term: false
1165 |     kernel_size: 1
1166 |     weight_filler {
1167 |       type: "msra"
1168 |     }
1169 |   }
1170 | }
1171 | layer {
1172 |   name: "conv4_2/linear/bn"
1173 |   type: "BatchNorm"
1174 |   bottom: "conv4_2/linear"
1175 |   top: "conv4_2/linear/bn"
1176 |   param {
1177 |     lr_mult: 0
1178 |     decay_mult: 0
1179 |   }
1180 |   param {
1181 |     lr_mult: 0
1182 |     decay_mult: 0
1183 |   }
1184 |   param {
1185 |     lr_mult: 0
1186 |     decay_mult: 0
1187 |   }
1188 |   batch_norm_param {
1189 |     use_global_stats: true
1190 |     eps: 1e-5
1191 |   }
1192 | }
1193 | layer {
1194 |   name: "conv4_2/linear/scale"
1195 |   type: "Scale"
1196 |   bottom: "conv4_2/linear/bn"
1197 |   top: "conv4_2/linear/bn"
1198 |   param {
1199 |     lr_mult: 1
1200 |     decay_mult: 0
1201 |   }
1202 |   param {
1203 |     lr_mult: 1
1204 |     decay_mult: 0
1205 |   }
1206 |   scale_param {
1207 |     bias_term: true
1208 |   }
1209 | }
1210 | layer {
1211 |   name: "block_4_2"
1212 |   type: "Eltwise"
1213 |   bottom: "block_4_1"
1214 |   bottom: "conv4_2/linear/bn"
1215 |   top: "block_4_2"
1216 | }
1217 | layer {
1218 |   name: "conv4_3/expand"
1219 |   type: "Convolution"
1220 |   bottom: "block_4_2"
1221 |   top: "conv4_3/expand"
1222 |   param {
1223 |     lr_mult: 1
1224 |     decay_mult: 1
1225 |   }
1226 |   convolution_param {
1227 |     num_output: 192
1228 |     bias_term: false
1229 |     kernel_size: 1
1230 |     weight_filler {
1231 |       type: "msra"
1232 |     }
1233 |   }
1234 | }
1235 | layer {
1236 |   name: "conv4_3/expand/bn"
1237 |   type: "BatchNorm"
1238 |   bottom: "conv4_3/expand"
1239 |   top: "conv4_3/expand/bn"
1240 |   param {
1241 |     lr_mult: 0
1242 |     decay_mult: 0
1243 |   }
1244 |   param {
1245 |     lr_mult: 0
1246 |     decay_mult: 0
1247 |   }
1248 |   param {
1249 |     lr_mult: 0
1250 |     decay_mult: 0
1251 |   }
1252 |   batch_norm_param {
1253 |     use_global_stats: true
1254 |     eps: 1e-5
1255 |   }
1256 | }
1257 | layer {
1258 |   name: "conv4_3/expand/scale"
1259 |   type: "Scale"
1260 |   bottom: "conv4_3/expand/bn"
1261 |   top: "conv4_3/expand/bn"
1262 |   param {
1263 |     lr_mult: 1
1264 |     decay_mult: 0
1265 |   }
1266 |   param {
1267 |     lr_mult: 1
1268 |     decay_mult: 0
1269 |   }
1270 |   scale_param {
1271 |     bias_term: true
1272 |   }
1273 | }
1274 | layer {
1275 |   name: "relu4_3/expand"
1276 |   type: "ReLU"
1277 |   bottom: "conv4_3/expand/bn"
1278 |   top: "conv4_3/expand/bn"
1279 | }
1280 | layer {
1281 |   name: "conv4_3/dwise"
1282 |   type: "Convolution"
1283 |   bottom: "conv4_3/expand/bn"
1284 |   top: "conv4_3/dwise"
1285 |   param {
1286 |     lr_mult: 1
1287 |     decay_mult: 1
1288 |   }
1289 |   convolution_param {
1290 |     num_output: 192
1291 |     bias_term: false
1292 |     pad: 1
1293 |     kernel_size: 3
1294 |     group: 192
1295 |     weight_filler {
1296 |       type: "msra"
1297 |     }
1298 |     engine: CAFFE
1299 |   }
1300 | }
1301 | layer {
1302 |   name: "conv4_3/dwise/bn"
1303 |   type: "BatchNorm"
1304 |   bottom: "conv4_3/dwise"
1305 |   top: "conv4_3/dwise/bn"
1306 |   param {
1307 |     lr_mult: 0
1308 |     decay_mult: 0
1309 |   }
1310 |   param {
1311 |     lr_mult: 0
1312 |     decay_mult: 0
1313 |   }
1314 |   param {
1315 |     lr_mult: 0
1316 |     decay_mult: 0
1317 |   }
1318 |   batch_norm_param {
1319 |     use_global_stats: true
1320 |     eps: 1e-5
1321 |   }
1322 | }
1323 | layer {
1324 |   name: "conv4_3/dwise/scale"
1325 |   type: "Scale"
1326 |   bottom: "conv4_3/dwise/bn"
1327 |   top: "conv4_3/dwise/bn"
1328 |   param {
1329 |     lr_mult: 1
1330 |     decay_mult: 0
1331 |   }
1332 |   param {
1333 |     lr_mult: 1
1334 |     decay_mult: 0
1335 |   }
1336 |   scale_param {
1337 |     bias_term: true
1338 |   }
1339 | }
1340 | layer {
1341 |   name: "relu4_3/dwise"
1342 |   type: "ReLU"
1343 |   bottom: "conv4_3/dwise/bn"
1344 |   top: "conv4_3/dwise/bn"
1345 | }
1346 | layer {
1347 |   name: "conv4_3/linear"
1348 |   type: "Convolution"
1349 |   bottom: "conv4_3/dwise/bn"
1350 |   top: "conv4_3/linear"
1351 |   param {
1352 |     lr_mult: 1
1353 |     decay_mult: 1
1354 |   }
1355 |   convolution_param {
1356 |     num_output: 64
1357 |     bias_term: false
1358 |     kernel_size: 1
1359 |     weight_filler {
1360 |       type: "msra"
1361 |     }
1362 |   }
1363 | }
1364 | layer {
1365 |   name: "conv4_3/linear/bn"
1366 |   type: "BatchNorm"
1367 |   bottom: "conv4_3/linear"
1368 |   top: "conv4_3/linear/bn"
1369 |   param {
1370 |     lr_mult: 0
1371 |     decay_mult: 0
1372 |   }
1373 |   param {
1374 |     lr_mult: 0
1375 |     decay_mult: 0
1376 |   }
1377 |   param {
1378 |     lr_mult: 0
1379 |     decay_mult: 0
1380 |   }
1381 |   batch_norm_param {
1382 |     use_global_stats: true
1383 |     eps: 1e-5
1384 |   }
1385 | }
1386 | layer {
1387 |   name: "conv4_3/linear/scale"
1388 |   type: "Scale"
1389 |   bottom: "conv4_3/linear/bn"
1390 |   top: "conv4_3/linear/bn"
1391 |   param {
1392 |     lr_mult: 1
1393 |     decay_mult: 0
1394 |   }
1395 |   param {
1396 |     lr_mult: 1
1397 |     decay_mult: 0
1398 |   }
1399 |   scale_param {
1400 |     bias_term: true
1401 |   }
1402 | }
1403 | layer {
1404 |   name: "conv4_4/expand"
1405 |   type: "Convolution"
1406 |   bottom: "conv4_3/linear/bn"
1407 |   top: "conv4_4/expand"
1408 |   param {
1409 |     lr_mult: 1
1410 |     decay_mult: 1
1411 |   }
1412 |   convolution_param {
1413 |     num_output: 384
1414 |     bias_term: false
1415 |     kernel_size: 1
1416 |     weight_filler {
1417 |       type: "msra"
1418 |     }
1419 |   }
1420 | }
1421 | layer {
1422 |   name: "conv4_4/expand/bn"
1423 |   type: "BatchNorm"
1424 |   bottom: "conv4_4/expand"
1425 |   top: "conv4_4/expand/bn"
1426 |   param {
1427 |     lr_mult: 0
1428 |     decay_mult: 0
1429 |   }
1430 |   param {
1431 |     lr_mult: 0
1432 |     decay_mult: 0
1433 |   }
1434 |   param {
1435 |     lr_mult: 0
1436 |     decay_mult: 0
1437 |   }
1438 |   batch_norm_param {
1439 |     use_global_stats: true
1440 |     eps: 1e-5
1441 |   }
1442 | }
1443 | layer {
1444 |   name: "conv4_4/expand/scale"
1445 |   type: "Scale"
1446 |   bottom: "conv4_4/expand/bn"
1447 |   top: "conv4_4/expand/bn"
1448 |   param {
1449 |     lr_mult: 1
1450 |     decay_mult: 0
1451 |   }
1452 |   param {
1453 |     lr_mult: 1
1454 |     decay_mult: 0
1455 |   }
1456 |   scale_param {
1457 |     bias_term: true
1458 |   }
1459 | }
1460 | layer {
1461 |   name: "relu4_4/expand"
1462 |   type: "ReLU"
1463 |   bottom: "conv4_4/expand/bn"
1464 |   top: "conv4_4/expand/bn"
1465 | }
1466 | layer {
1467 |   name: "conv4_4/dwise"
1468 |   type: "Convolution"
1469 |   bottom: "conv4_4/expand/bn"
1470 |   top: "conv4_4/dwise"
1471 |   param {
1472 |     lr_mult: 1
1473 |     decay_mult: 1
1474 |   }
1475 |   convolution_param {
1476 |     num_output: 384
1477 |     bias_term: false
1478 |     pad: 1
1479 |     kernel_size: 3
1480 |     group: 384
1481 |     weight_filler {
1482 |       type: "msra"
1483 |     }
1484 |     engine: CAFFE
1485 |   }
1486 | }
1487 | layer {
1488 |   name: "conv4_4/dwise/bn"
1489 |   type: "BatchNorm"
1490 |   bottom: "conv4_4/dwise"
1491 |   top: "conv4_4/dwise/bn"
1492 |   param {
1493 |     lr_mult: 0
1494 |     decay_mult: 0
1495 |   }
1496 |   param {
1497 |     lr_mult: 0
1498 |     decay_mult: 0
1499 |   }
1500 |   param {
1501 |     lr_mult: 0
1502 |     decay_mult: 0
1503 |   }
1504 |   batch_norm_param {
1505 |     use_global_stats: true
1506 |     eps: 1e-5
1507 |   }
1508 | }
1509 | layer {
1510 |   name: "conv4_4/dwise/scale"
1511 |   type: "Scale"
1512 |   bottom: "conv4_4/dwise/bn"
1513 |   top: "conv4_4/dwise/bn"
1514 |   param {
1515 |     lr_mult: 1
1516 |     decay_mult: 0
1517 |   }
1518 |   param {
1519 |     lr_mult: 1
1520 |     decay_mult: 0
1521 |   }
1522 |   scale_param {
1523 |     bias_term: true
1524 |   }
1525 | }
1526 | layer {
1527 |   name: "relu4_4/dwise"
1528 |   type: "ReLU"
1529 |   bottom: "conv4_4/dwise/bn"
1530 |   top: "conv4_4/dwise/bn"
1531 | }
1532 | layer {
1533 |   name: "conv4_4/linear"
1534 |   type: "Convolution"
1535 |   bottom: "conv4_4/dwise/bn"
1536 |   top: "conv4_4/linear"
1537 |   param {
1538 |     lr_mult: 1
1539 |     decay_mult: 1
1540 |   }
1541 |   convolution_param {
1542 |     num_output: 64
1543 |     bias_term: false
1544 |     kernel_size: 1
1545 |     weight_filler {
1546 |       type: "msra"
1547 |     }
1548 |   }
1549 | }
1550 | layer {
1551 |   name: "conv4_4/linear/bn"
1552 |   type: "BatchNorm"
1553 |   bottom: "conv4_4/linear"
1554 |   top: "conv4_4/linear/bn"
1555 |   param {
1556 |     lr_mult: 0
1557 |     decay_mult: 0
1558 |   }
1559 |   param {
1560 |     lr_mult: 0
1561 |     decay_mult: 0
1562 |   }
1563 |   param {
1564 |     lr_mult: 0
1565 |     decay_mult: 0
1566 |   }
1567 |   batch_norm_param {
1568 |     use_global_stats: true
1569 |     eps: 1e-5
1570 |   }
1571 | }
1572 | layer {
1573 |   name: "conv4_4/linear/scale"
1574 |   type: "Scale"
1575 |   bottom: "conv4_4/linear/bn"
1576 |   top: "conv4_4/linear/bn"
1577 |   param {
1578 |     lr_mult: 1
1579 |     decay_mult: 0
1580 |   }
1581 |   param {
1582 |     lr_mult: 1
1583 |     decay_mult: 0
1584 |   }
1585 |   scale_param {
1586 |     bias_term: true
1587 |   }
1588 | }
1589 | layer {
1590 |   name: "block_4_4"
1591 |   type: "Eltwise"
1592 |   bottom: "conv4_3/linear/bn"
1593 |   bottom: "conv4_4/linear/bn"
1594 |   top: "block_4_4"
1595 | }
1596 | layer {
1597 |   name: "conv4_5/expand"
1598 |   type: "Convolution"
1599 |   bottom: "block_4_4"
1600 |   top: "conv4_5/expand"
1601 |   param {
1602 |     lr_mult: 1
1603 |     decay_mult: 1
1604 |   }
1605 |   convolution_param {
1606 |     num_output: 384
1607 |     bias_term: false
1608 |     kernel_size: 1
1609 |     weight_filler {
1610 |       type: "msra"
1611 |     }
1612 |   }
1613 | }
1614 | layer {
1615 |   name: "conv4_5/expand/bn"
1616 |   type: "BatchNorm"
1617 |   bottom: "conv4_5/expand"
1618 |   top: "conv4_5/expand/bn"
1619 |   param {
1620 |     lr_mult: 0
1621 |     decay_mult: 0
1622 |   }
1623 |   param {
1624 |     lr_mult: 0
1625 |     decay_mult: 0
1626 |   }
1627 |   param {
1628 |     lr_mult: 0
1629 |     decay_mult: 0
1630 |   }
1631 |   batch_norm_param {
1632 |     use_global_stats: true
1633 |     eps: 1e-5
1634 |   }
1635 | }
1636 | layer {
1637 |   name: "conv4_5/expand/scale"
1638 |   type: "Scale"
1639 |   bottom: "conv4_5/expand/bn"
1640 |   top: "conv4_5/expand/bn"
1641 |   param {
1642 |     lr_mult: 1
1643 |     decay_mult: 0
1644 |   }
1645 |   param {
1646 |     lr_mult: 1
1647 |     decay_mult: 0
1648 |   }
1649 |   scale_param {
1650 |     bias_term: true
1651 |   }
1652 | }
1653 | layer {
1654 |   name: "relu4_5/expand"
1655 |   type: "ReLU"
1656 |   bottom: "conv4_5/expand/bn"
1657 |   top: "conv4_5/expand/bn"
1658 | }
1659 | layer {
1660 |   name: "conv4_5/dwise"
1661 |   type: "Convolution"
1662 |   bottom: "conv4_5/expand/bn"
1663 |   top: "conv4_5/dwise"
1664 |   param {
1665 |     lr_mult: 1
1666 |     decay_mult: 1
1667 |   }
1668 |   convolution_param {
1669 |     num_output: 384
1670 |     bias_term: false
1671 |     pad: 1
1672 |     kernel_size: 3
1673 |     group: 384
1674 |     weight_filler {
1675 |       type: "msra"
1676 |     }
1677 |     engine: CAFFE
1678 |   }
1679 | }
1680 | layer {
1681 |   name: "conv4_5/dwise/bn"
1682 |   type: "BatchNorm"
1683 |   bottom: "conv4_5/dwise"
1684 |   top: "conv4_5/dwise/bn"
1685 |   param {
1686 |     lr_mult: 0
1687 |     decay_mult: 0
1688 |   }
1689 |   param {
1690 |     lr_mult: 0
1691 |     decay_mult: 0
1692 |   }
1693 |   param {
1694 |     lr_mult: 0
1695 |     decay_mult: 0
1696 |   }
1697 |   batch_norm_param {
1698 |     use_global_stats: true
1699 |     eps: 1e-5
1700 |   }
1701 | }
1702 | layer {
1703 |   name: "conv4_5/dwise/scale"
1704 |   type: "Scale"
1705 |   bottom: "conv4_5/dwise/bn"
1706 |   top: "conv4_5/dwise/bn"
1707 |   param {
1708 |     lr_mult: 1
1709 |     decay_mult: 0
1710 |   }
1711 |   param {
1712 |     lr_mult: 1
1713 |     decay_mult: 0
1714 |   }
1715 |   scale_param {
1716 |     bias_term: true
1717 |   }
1718 | }
1719 | layer {
1720 |   name: "relu4_5/dwise"
1721 |   type: "ReLU"
1722 |   bottom: "conv4_5/dwise/bn"
1723 |   top: "conv4_5/dwise/bn"
1724 | }
1725 | layer {
1726 |   name: "conv4_5/linear"
1727 |   type: "Convolution"
1728 |   bottom: "conv4_5/dwise/bn"
1729 |   top: "conv4_5/linear"
1730 |   param {
1731 |     lr_mult: 1
1732 |     decay_mult: 1
1733 |   }
1734 |   convolution_param {
1735 |     num_output: 64
1736 |     bias_term: false
1737 |     kernel_size: 1
1738 |     weight_filler {
1739 |       type: "msra"
1740 |     }
1741 |   }
1742 | }
1743 | layer {
1744 |   name: "conv4_5/linear/bn"
1745 |   type: "BatchNorm"
1746 |   bottom: "conv4_5/linear"
1747 |   top: "conv4_5/linear/bn"
1748 |   param {
1749 |     lr_mult: 0
1750 |     decay_mult: 0
1751 |   }
1752 |   param {
1753 |     lr_mult: 0
1754 |     decay_mult: 0
1755 |   }
1756 |   param {
1757 |     lr_mult: 0
1758 |     decay_mult: 0
1759 |   }
1760 |   batch_norm_param {
1761 |     use_global_stats: true
1762 |     eps: 1e-5
1763 |   }
1764 | }
1765 | layer {
1766 |   name: "conv4_5/linear/scale"
1767 |   type: "Scale"
1768 |   bottom: "conv4_5/linear/bn"
1769 |   top: "conv4_5/linear/bn"
1770 |   param {
1771 |     lr_mult: 1
1772 |     decay_mult: 0
1773 |   }
1774 |   param {
1775 |     lr_mult: 1
1776 |     decay_mult: 0
1777 |   }
1778 |   scale_param {
1779 |     bias_term: true
1780 |   }
1781 | }
1782 | layer {
1783 |   name: "block_4_5"
1784 |   type: "Eltwise"
1785 |   bottom: "block_4_4"
1786 |   bottom: "conv4_5/linear/bn"
1787 |   top: "block_4_5"
1788 | }
1789 | layer {
1790 |   name: "conv4_6/expand"
1791 |   type: "Convolution"
1792 |   bottom: "block_4_5"
1793 |   top: "conv4_6/expand"
1794 |   param {
1795 |     lr_mult: 1
1796 |     decay_mult: 1
1797 |   }
1798 |   convolution_param {
1799 |     num_output: 384
1800 |     bias_term: false
1801 |     kernel_size: 1
1802 |     weight_filler {
1803 |       type: "msra"
1804 |     }
1805 |   }
1806 | }
1807 | layer {
1808 |   name: "conv4_6/expand/bn"
1809 |   type: "BatchNorm"
1810 |   bottom: "conv4_6/expand"
1811 |   top: "conv4_6/expand/bn"
1812 |   param {
1813 |     lr_mult: 0
1814 |     decay_mult: 0
1815 |   }
1816 |   param {
1817 |     lr_mult: 0
1818 |     decay_mult: 0
1819 |   }
1820 |   param {
1821 |     lr_mult: 0
1822 |     decay_mult: 0
1823 |   }
1824 |   batch_norm_param {
1825 |     use_global_stats: true
1826 |     eps: 1e-5
1827 |   }
1828 | }
1829 | layer {
1830 |   name: "conv4_6/expand/scale"
1831 |   type: "Scale"
1832 |   bottom: "conv4_6/expand/bn"
1833 |   top: "conv4_6/expand/bn"
1834 |   param {
1835 |     lr_mult: 1
1836 |     decay_mult: 0
1837 |   }
1838 |   param {
1839 |     lr_mult: 1
1840 |     decay_mult: 0
1841 |   }
1842 |   scale_param {
1843 |     bias_term: true
1844 |   }
1845 | }
1846 | layer {
1847 |   name: "relu4_6/expand"
1848 |   type: "ReLU"
1849 |   bottom: "conv4_6/expand/bn"
1850 |   top: "conv4_6/expand/bn"
1851 | }
1852 | layer {
1853 |   name: "conv4_6/dwise"
1854 |   type: "Convolution"
1855 |   bottom: "conv4_6/expand/bn"
1856 |   top: "conv4_6/dwise"
1857 |   param {
1858 |     lr_mult: 1
1859 |     decay_mult: 1
1860 |   }
1861 |   convolution_param {
1862 |     num_output: 384
1863 |     bias_term: false
1864 |     pad: 1
1865 |     kernel_size: 3
1866 |     group: 384
1867 |     weight_filler {
1868 |       type: "msra"
1869 |     }
1870 |     engine: CAFFE
1871 |   }
1872 | }
1873 | layer {
1874 |   name: "conv4_6/dwise/bn"
1875 |   type: "BatchNorm"
1876 |   bottom: "conv4_6/dwise"
1877 |   top: "conv4_6/dwise/bn"
1878 |   param {
1879 |     lr_mult: 0
1880 |     decay_mult: 0
1881 |   }
1882 |   param {
1883 |     lr_mult: 0
1884 |     decay_mult: 0
1885 |   }
1886 |   param {
1887 |     lr_mult: 0
1888 |     decay_mult: 0
1889 |   }
1890 |   batch_norm_param {
1891 |     use_global_stats: true
1892 |     eps: 1e-5
1893 |   }
1894 | }
1895 | layer {
1896 |   name: "conv4_6/dwise/scale"
1897 |   type: "Scale"
1898 |   bottom: "conv4_6/dwise/bn"
1899 |   top: "conv4_6/dwise/bn"
1900 |   param {
1901 |     lr_mult: 1
1902 |     decay_mult: 0
1903 |   }
1904 |   param {
1905 |     lr_mult: 1
1906 |     decay_mult: 0
1907 |   }
1908 |   scale_param {
1909 |     bias_term: true
1910 |   }
1911 | }
1912 | layer {
1913 |   name: "relu4_6/dwise"
1914 |   type: "ReLU"
1915 |   bottom: "conv4_6/dwise/bn"
1916 |   top: "conv4_6/dwise/bn"
1917 | }
1918 | layer {
1919 |   name: "conv4_6/linear"
1920 |   type: "Convolution"
1921 |   bottom: "conv4_6/dwise/bn"
1922 |   top: "conv4_6/linear"
1923 |   param {
1924 |     lr_mult: 1
1925 |     decay_mult: 1
1926 |   }
1927 |   convolution_param {
1928 |     num_output: 64
1929 |     bias_term: false
1930 |     kernel_size: 1
1931 |     weight_filler {
1932 |       type: "msra"
1933 |     }
1934 |   }
1935 | }
1936 | layer {
1937 |   name: "conv4_6/linear/bn"
1938 |   type: "BatchNorm"
1939 |   bottom: "conv4_6/linear"
1940 |   top: "conv4_6/linear/bn"
1941 |   param {
1942 |     lr_mult: 0
1943 |     decay_mult: 0
1944 |   }
1945 |   param {
1946 |     lr_mult: 0
1947 |     decay_mult: 0
1948 |   }
1949 |   param {
1950 |     lr_mult: 0
1951 |     decay_mult: 0
1952 |   }
1953 |   batch_norm_param {
1954 |     use_global_stats: true
1955 |     eps: 1e-5
1956 |   }
1957 | }
1958 | layer {
1959 |   name: "conv4_6/linear/scale"
1960 |   type: "Scale"
1961 |   bottom: "conv4_6/linear/bn"
1962 |   top: "conv4_6/linear/bn"
1963 |   param {
1964 |     lr_mult: 1
1965 |     decay_mult: 0
1966 |   }
1967 |   param {
1968 |     lr_mult: 1
1969 |     decay_mult: 0
1970 |   }
1971 |   scale_param {
1972 |     bias_term: true
1973 |   }
1974 | }
1975 | layer {
1976 |   name: "block_4_6"
1977 |   type: "Eltwise"
1978 |   bottom: "block_4_5"
1979 |   bottom: "conv4_6/linear/bn"
1980 |   top: "block_4_6"
1981 | }
1982 | layer {
1983 |   name: "conv4_7/expand"
1984 |   type: "Convolution"
1985 |   bottom: "block_4_6"
1986 |   top: "conv4_7/expand"
1987 |   param {
1988 |     lr_mult: 1
1989 |     decay_mult: 1
1990 |   }
1991 |   convolution_param {
1992 |     num_output: 384
1993 |     bias_term: false
1994 |     kernel_size: 1
1995 |     weight_filler {
1996 |       type: "msra"
1997 |     }
1998 |   }
1999 | }
2000 | layer {
2001 |   name: "conv4_7/expand/bn"
2002 |   type: "BatchNorm"
2003 |   bottom: "conv4_7/expand"
2004 |   top: "conv4_7/expand/bn"
2005 |   param {
2006 |     lr_mult: 0
2007 |     decay_mult: 0
2008 |   }
2009 |   param {
2010 |     lr_mult: 0
2011 |     decay_mult: 0
2012 |   }
2013 |   param {
2014 |     lr_mult: 0
2015 |     decay_mult: 0
2016 |   }
2017 |   batch_norm_param {
2018 |     use_global_stats: true
2019 |     eps: 1e-5
2020 |   }
2021 | }
2022 | layer {
2023 |   name: "conv4_7/expand/scale"
2024 |   type: "Scale"
2025 |   bottom: "conv4_7/expand/bn"
2026 |   top: "conv4_7/expand/bn"
2027 |   param {
2028 |     lr_mult: 1
2029 |     decay_mult: 0
2030 |   }
2031 |   param {
2032 |     lr_mult: 1
2033 |     decay_mult: 0
2034 |   }
2035 |   scale_param {
2036 |     bias_term: true
2037 |   }
2038 | }
2039 | layer {
2040 |   name: "relu4_7/expand"
2041 |   type: "ReLU"
2042 |   bottom: "conv4_7/expand/bn"
2043 |   top: "conv4_7/expand/bn"
2044 | }
2045 | layer {
2046 |   name: "conv4_7/dwise"
2047 |   type: "Convolution"
2048 |   bottom: "conv4_7/expand/bn"
2049 |   top: "conv4_7/dwise"
2050 |   param {
2051 |     lr_mult: 1
2052 |     decay_mult: 1
2053 |   }
2054 |   convolution_param {
2055 |     num_output: 384
2056 |     bias_term: false
2057 |     pad: 1
2058 |     kernel_size: 3
2059 |     group: 384
2060 |     stride: 2
2061 |     weight_filler {
2062 |       type: "msra"
2063 |     }
2064 |     engine: CAFFE
2065 |   }
2066 | }
2067 | layer {
2068 |   name: "conv4_7/dwise/bn"
2069 |   type: "BatchNorm"
2070 |   bottom: "conv4_7/dwise"
2071 |   top: "conv4_7/dwise/bn"
2072 |   param {
2073 |     lr_mult: 0
2074 |     decay_mult: 0
2075 |   }
2076 |   param {
2077 |     lr_mult: 0
2078 |     decay_mult: 0
2079 |   }
2080 |   param {
2081 |     lr_mult: 0
2082 |     decay_mult: 0
2083 |   }
2084 |   batch_norm_param {
2085 |     use_global_stats: true
2086 |     eps: 1e-5
2087 |   }
2088 | }
2089 | layer {
2090 |   name: "conv4_7/dwise/scale"
2091 |   type: "Scale"
2092 |   bottom: "conv4_7/dwise/bn"
2093 |   top: "conv4_7/dwise/bn"
2094 |   param {
2095 |     lr_mult: 1
2096 |     decay_mult: 0
2097 |   }
2098 |   param {
2099 |     lr_mult: 1
2100 |     decay_mult: 0
2101 |   }
2102 |   scale_param {
2103 |     bias_term: true
2104 |   }
2105 | }
2106 | layer {
2107 |   name: "relu4_7/dwise"
2108 |   type: "ReLU"
2109 |   bottom: "conv4_7/dwise/bn"
2110 |   top: "conv4_7/dwise/bn"
2111 | }
2112 | layer {
2113 |   name: "conv4_7/linear"
2114 |   type: "Convolution"
2115 |   bottom: "conv4_7/dwise/bn"
2116 |   top: "conv4_7/linear"
2117 |   param {
2118 |     lr_mult: 1
2119 |     decay_mult: 1
2120 |   }
2121 |   convolution_param {
2122 |     num_output: 96
2123 |     bias_term: false
2124 |     kernel_size: 1
2125 |     weight_filler {
2126 |       type: "msra"
2127 |     }
2128 |   }
2129 | }
2130 | layer {
2131 |   name: "conv4_7/linear/bn"
2132 |   type: "BatchNorm"
2133 |   bottom: "conv4_7/linear"
2134 |   top: "conv4_7/linear/bn"
2135 |   param {
2136 |     lr_mult: 0
2137 |     decay_mult: 0
2138 |   }
2139 |   param {
2140 |     lr_mult: 0
2141 |     decay_mult: 0
2142 |   }
2143 |   param {
2144 |     lr_mult: 0
2145 |     decay_mult: 0
2146 |   }
2147 |   batch_norm_param {
2148 |     use_global_stats: true
2149 |     eps: 1e-5
2150 |   }
2151 | }
2152 | layer {
2153 |   name: "conv4_7/linear/scale"
2154 |   type: "Scale"
2155 |   bottom: "conv4_7/linear/bn"
2156 |   top: "conv4_7/linear/bn"
2157 |   param {
2158 |     lr_mult: 1
2159 |     decay_mult: 0
2160 |   }
2161 |   param {
2162 |     lr_mult: 1
2163 |     decay_mult: 0
2164 |   }
2165 |   scale_param {
2166 |     bias_term: true
2167 |   }
2168 | }
2169 | layer {
2170 |   name: "conv5_1/expand"
2171 |   type: "Convolution"
2172 |   bottom: "conv4_7/linear/bn"
2173 |   top: "conv5_1/expand"
2174 |   param {
2175 |     lr_mult: 1
2176 |     decay_mult: 1
2177 |   }
2178 |   convolution_param {
2179 |     num_output: 576
2180 |     bias_term: false
2181 |     kernel_size: 1
2182 |     weight_filler {
2183 |       type: "msra"
2184 |     }
2185 |   }
2186 | }
2187 | layer {
2188 |   name: "conv5_1/expand/bn"
2189 |   type: "BatchNorm"
2190 |   bottom: "conv5_1/expand"
2191 |   top: "conv5_1/expand/bn"
2192 |   param {
2193 |     lr_mult: 0
2194 |     decay_mult: 0
2195 |   }
2196 |   param {
2197 |     lr_mult: 0
2198 |     decay_mult: 0
2199 |   }
2200 |   param {
2201 |     lr_mult: 0
2202 |     decay_mult: 0
2203 |   }
2204 |   batch_norm_param {
2205 |     use_global_stats: true
2206 |     eps: 1e-5
2207 |   }
2208 | }
2209 | layer {
2210 |   name: "conv5_1/expand/scale"
2211 |   type: "Scale"
2212 |   bottom: "conv5_1/expand/bn"
2213 |   top: "conv5_1/expand/bn"
2214 |   param {
2215 |     lr_mult: 1
2216 |     decay_mult: 0
2217 |   }
2218 |   param {
2219 |     lr_mult: 1
2220 |     decay_mult: 0
2221 |   }
2222 |   scale_param {
2223 |     bias_term: true
2224 |   }
2225 | }
2226 | layer {
2227 |   name: "relu5_1/expand"
2228 |   type: "ReLU"
2229 |   bottom: "conv5_1/expand/bn"
2230 |   top: "conv5_1/expand/bn"
2231 | }
2232 | layer {
2233 |   name: "conv5_1/dwise"
2234 |   type: "Convolution"
2235 |   bottom: "conv5_1/expand/bn"
2236 |   top: "conv5_1/dwise"
2237 |   param {
2238 |     lr_mult: 1
2239 |     decay_mult: 1
2240 |   }
2241 |   convolution_param {
2242 |     num_output: 576
2243 |     bias_term: false
2244 |     pad: 1
2245 |     kernel_size: 3
2246 |     group: 576
2247 |     weight_filler {
2248 |       type: "msra"
2249 |     }
2250 |     engine: CAFFE
2251 |   }
2252 | }
2253 | layer {
2254 |   name: "conv5_1/dwise/bn"
2255 |   type: "BatchNorm"
2256 |   bottom: "conv5_1/dwise"
2257 |   top: "conv5_1/dwise/bn"
2258 |   param {
2259 |     lr_mult: 0
2260 |     decay_mult: 0
2261 |   }
2262 |   param {
2263 |     lr_mult: 0
2264 |     decay_mult: 0
2265 |   }
2266 |   param {
2267 |     lr_mult: 0
2268 |     decay_mult: 0
2269 |   }
2270 |   batch_norm_param {
2271 |     use_global_stats: true
2272 |     eps: 1e-5
2273 |   }
2274 | }
2275 | layer {
2276 |   name: "conv5_1/dwise/scale"
2277 |   type: "Scale"
2278 |   bottom: "conv5_1/dwise/bn"
2279 |   top: "conv5_1/dwise/bn"
2280 |   param {
2281 |     lr_mult: 1
2282 |     decay_mult: 0
2283 |   }
2284 |   param {
2285 |     lr_mult: 1
2286 |     decay_mult: 0
2287 |   }
2288 |   scale_param {
2289 |     bias_term: true
2290 |   }
2291 | }
2292 | layer {
2293 |   name: "relu5_1/dwise"
2294 |   type: "ReLU"
2295 |   bottom: "conv5_1/dwise/bn"
2296 |   top: "conv5_1/dwise/bn"
2297 | }
2298 | layer {
2299 |   name: "conv5_1/linear"
2300 |   type: "Convolution"
2301 |   bottom: "conv5_1/dwise/bn"
2302 |   top: "conv5_1/linear"
2303 |   param {
2304 |     lr_mult: 1
2305 |     decay_mult: 1
2306 |   }
2307 |   convolution_param {
2308 |     num_output: 96
2309 |     bias_term: false
2310 |     kernel_size: 1
2311 |     weight_filler {
2312 |       type: "msra"
2313 |     }
2314 |   }
2315 | }
2316 | layer {
2317 |   name: "conv5_1/linear/bn"
2318 |   type: "BatchNorm"
2319 |   bottom: "conv5_1/linear"
2320 |   top: "conv5_1/linear/bn"
2321 |   param {
2322 |     lr_mult: 0
2323 |     decay_mult: 0
2324 |   }
2325 |   param {
2326 |     lr_mult: 0
2327 |     decay_mult: 0
2328 |   }
2329 |   param {
2330 |     lr_mult: 0
2331 |     decay_mult: 0
2332 |   }
2333 |   batch_norm_param {
2334 |     use_global_stats: true
2335 |     eps: 1e-5
2336 |   }
2337 | }
2338 | layer {
2339 |   name: "conv5_1/linear/scale"
2340 |   type: "Scale"
2341 |   bottom: "conv5_1/linear/bn"
2342 |   top: "conv5_1/linear/bn"
2343 |   param {
2344 |     lr_mult: 1
2345 |     decay_mult: 0
2346 |   }
2347 |   param {
2348 |     lr_mult: 1
2349 |     decay_mult: 0
2350 |   }
2351 |   scale_param {
2352 |     bias_term: true
2353 |   }
2354 | }
2355 | layer {
2356 |   name: "block_5_1"
2357 |   type: "Eltwise"
2358 |   bottom: "conv4_7/linear/bn"
2359 |   bottom: "conv5_1/linear/bn"
2360 |   top: "block_5_1"
2361 | }
2362 | layer {
2363 |   name: "conv5_2/expand"
2364 |   type: "Convolution"
2365 |   bottom: "block_5_1"
2366 |   top: "conv5_2/expand"
2367 |   param {
2368 |     lr_mult: 1
2369 |     decay_mult: 1
2370 |   }
2371 |   convolution_param {
2372 |     num_output: 576
2373 |     bias_term: false
2374 |     kernel_size: 1
2375 |     weight_filler {
2376 |       type: "msra"
2377 |     }
2378 |   }
2379 | }
2380 | layer {
2381 |   name: "conv5_2/expand/bn"
2382 |   type: "BatchNorm"
2383 |   bottom: "conv5_2/expand"
2384 |   top: "conv5_2/expand/bn"
2385 |   param {
2386 |     lr_mult: 0
2387 |     decay_mult: 0
2388 |   }
2389 |   param {
2390 |     lr_mult: 0
2391 |     decay_mult: 0
2392 |   }
2393 |   param {
2394 |     lr_mult: 0
2395 |     decay_mult: 0
2396 |   }
2397 |   batch_norm_param {
2398 |     use_global_stats: true
2399 |     eps: 1e-5
2400 |   }
2401 | }
2402 | layer {
2403 |   name: "conv5_2/expand/scale"
2404 |   type: "Scale"
2405 |   bottom: "conv5_2/expand/bn"
2406 |   top: "conv5_2/expand/bn"
2407 |   param {
2408 |     lr_mult: 1
2409 |     decay_mult: 0
2410 |   }
2411 |   param {
2412 |     lr_mult: 1
2413 |     decay_mult: 0
2414 |   }
2415 |   scale_param {
2416 |     bias_term: true
2417 |   }
2418 | }
2419 | layer {
2420 |   name: "relu5_2/expand"
2421 |   type: "ReLU"
2422 |   bottom: "conv5_2/expand/bn"
2423 |   top: "conv5_2/expand/bn"
2424 | }
2425 | layer {
2426 |   name: "conv5_2/dwise"
2427 |   type: "Convolution"
2428 |   bottom: "conv5_2/expand/bn"
2429 |   top: "conv5_2/dwise"
2430 |   param {
2431 |     lr_mult: 1
2432 |     decay_mult: 1
2433 |   }
2434 |   convolution_param {
2435 |     num_output: 576
2436 |     bias_term: false
2437 |     pad: 1
2438 |     kernel_size: 3
2439 |     group: 576
2440 |     weight_filler {
2441 |       type: "msra"
2442 |     }
2443 |     engine: CAFFE
2444 |   }
2445 | }
2446 | layer {
2447 |   name: "conv5_2/dwise/bn"
2448 |   type: "BatchNorm"
2449 |   bottom: "conv5_2/dwise"
2450 |   top: "conv5_2/dwise/bn"
2451 |   param {
2452 |     lr_mult: 0
2453 |     decay_mult: 0
2454 |   }
2455 |   param {
2456 |     lr_mult: 0
2457 |     decay_mult: 0
2458 |   }
2459 |   param {
2460 |     lr_mult: 0
2461 |     decay_mult: 0
2462 |   }
2463 |   batch_norm_param {
2464 |     use_global_stats: true
2465 |     eps: 1e-5
2466 |   }
2467 | }
2468 | layer {
2469 |   name: "conv5_2/dwise/scale"
2470 |   type: "Scale"
2471 |   bottom: "conv5_2/dwise/bn"
2472 |   top: "conv5_2/dwise/bn"
2473 |   param {
2474 |     lr_mult: 1
2475 |     decay_mult: 0
2476 |   }
2477 |   param {
2478 |     lr_mult: 1
2479 |     decay_mult: 0
2480 |   }
2481 |   scale_param {
2482 |     bias_term: true
2483 |   }
2484 | }
2485 | layer {
2486 |   name: "relu5_2/dwise"
2487 |   type: "ReLU"
2488 |   bottom: "conv5_2/dwise/bn"
2489 |   top: "conv5_2/dwise/bn"
2490 | }
2491 | layer {
2492 |   name: "conv5_2/linear"
2493 |   type: "Convolution"
2494 |   bottom: "conv5_2/dwise/bn"
2495 |   top: "conv5_2/linear"
2496 |   param {
2497 |     lr_mult: 1
2498 |     decay_mult: 1
2499 |   }
2500 |   convolution_param {
2501 |     num_output: 96
2502 |     bias_term: false
2503 |     kernel_size: 1
2504 |     weight_filler {
2505 |       type: "msra"
2506 |     }
2507 |   }
2508 | }
2509 | layer {
2510 |   name: "conv5_2/linear/bn"
2511 |   type: "BatchNorm"
2512 |   bottom: "conv5_2/linear"
2513 |   top: "conv5_2/linear/bn"
2514 |   param {
2515 |     lr_mult: 0
2516 |     decay_mult: 0
2517 |   }
2518 |   param {
2519 |     lr_mult: 0
2520 |     decay_mult: 0
2521 |   }
2522 |   param {
2523 |     lr_mult: 0
2524 |     decay_mult: 0
2525 |   }
2526 |   batch_norm_param {
2527 |     use_global_stats: true
2528 |     eps: 1e-5
2529 |   }
2530 | }
2531 | layer {
2532 |   name: "conv5_2/linear/scale"
2533 |   type: "Scale"
2534 |   bottom: "conv5_2/linear/bn"
2535 |   top: "conv5_2/linear/bn"
2536 |   param {
2537 |     lr_mult: 1
2538 |     decay_mult: 0
2539 |   }
2540 |   param {
2541 |     lr_mult: 1
2542 |     decay_mult: 0
2543 |   }
2544 |   scale_param {
2545 |     bias_term: true
2546 |   }
2547 | }
2548 | layer {
2549 |   name: "block_5_2"
2550 |   type: "Eltwise"
2551 |   bottom: "block_5_1"
2552 |   bottom: "conv5_2/linear/bn"
2553 |   top: "block_5_2"
2554 | }
2555 | layer {
2556 |   name: "conv5_3/expand"
2557 |   type: "Convolution"
2558 |   bottom: "block_5_2"
2559 |   top: "conv5_3/expand"
2560 |   param {
2561 |     lr_mult: 1
2562 |     decay_mult: 1
2563 |   }
2564 |   convolution_param {
2565 |     num_output: 576
2566 |     bias_term: false
2567 |     kernel_size: 1
2568 |     weight_filler {
2569 |       type: "msra"
2570 |     }
2571 |   }
2572 | }
2573 | layer {
2574 |   name: "conv5_3/expand/bn"
2575 |   type: "BatchNorm"
2576 |   bottom: "conv5_3/expand"
2577 |   top: "conv5_3/expand/bn"
2578 |   param {
2579 |     lr_mult: 0
2580 |     decay_mult: 0
2581 |   }
2582 |   param {
2583 |     lr_mult: 0
2584 |     decay_mult: 0
2585 |   }
2586 |   param {
2587 |     lr_mult: 0
2588 |     decay_mult: 0
2589 |   }
2590 |   batch_norm_param {
2591 |     use_global_stats: true
2592 |     eps: 1e-5
2593 |   }
2594 | }
2595 | layer {
2596 |   name: "conv5_3/expand/scale"
2597 |   type: "Scale"
2598 |   bottom: "conv5_3/expand/bn"
2599 |   top: "conv5_3/expand/bn"
2600 |   param {
2601 |     lr_mult: 1
2602 |     decay_mult: 0
2603 |   }
2604 |   param {
2605 |     lr_mult: 1
2606 |     decay_mult: 0
2607 |   }
2608 |   scale_param {
2609 |     bias_term: true
2610 |   }
2611 | }
2612 | layer {
2613 |   name: "relu5_3/expand"
2614 |   type: "ReLU"
2615 |   bottom: "conv5_3/expand/bn"
2616 |   top: "conv5_3/expand/bn"
2617 | }
2618 | layer {
2619 |   name: "conv5_3/dwise"
2620 |   type: "Convolution"
2621 |   bottom: "conv5_3/expand/bn"
2622 |   top: "conv5_3/dwise"
2623 |   param {
2624 |     lr_mult: 1
2625 |     decay_mult: 1
2626 |   }
2627 |   convolution_param {
2628 |     num_output: 576
2629 |     bias_term: false
2630 |     pad: 1
2631 |     kernel_size: 3
2632 |     group: 576
2633 |     stride: 2
2634 |     weight_filler {
2635 |       type: "msra"
2636 |     }
2637 |     engine: CAFFE
2638 |   }
2639 | }
2640 | layer {
2641 |   name: "conv5_3/dwise/bn"
2642 |   type: "BatchNorm"
2643 |   bottom: "conv5_3/dwise"
2644 |   top: "conv5_3/dwise/bn"
2645 |   param {
2646 |     lr_mult: 0
2647 |     decay_mult: 0
2648 |   }
2649 |   param {
2650 |     lr_mult: 0
2651 |     decay_mult: 0
2652 |   }
2653 |   param {
2654 |     lr_mult: 0
2655 |     decay_mult: 0
2656 |   }
2657 |   batch_norm_param {
2658 |     use_global_stats: true
2659 |     eps: 1e-5
2660 |   }
2661 | }
2662 | layer {
2663 |   name: "conv5_3/dwise/scale"
2664 |   type: "Scale"
2665 |   bottom: "conv5_3/dwise/bn"
2666 |   top: "conv5_3/dwise/bn"
2667 |   param {
2668 |     lr_mult: 1
2669 |     decay_mult: 0
2670 |   }
2671 |   param {
2672 |     lr_mult: 1
2673 |     decay_mult: 0
2674 |   }
2675 |   scale_param {
2676 |     bias_term: true
2677 |   }
2678 | }
2679 | layer {
2680 |   name: "relu5_3/dwise"
2681 |   type: "ReLU"
2682 |   bottom: "conv5_3/dwise/bn"
2683 |   top: "conv5_3/dwise/bn"
2684 | }
2685 | layer {
2686 |   name: "conv5_3/linear"
2687 |   type: "Convolution"
2688 |   bottom: "conv5_3/dwise/bn"
2689 |   top: "conv5_3/linear"
2690 |   param {
2691 |     lr_mult: 1
2692 |     decay_mult: 1
2693 |   }
2694 |   convolution_param {
2695 |     num_output: 160
2696 |     bias_term: false
2697 |     kernel_size: 1
2698 |     weight_filler {
2699 |       type: "msra"
2700 |     }
2701 |   }
2702 | }
2703 | layer {
2704 |   name: "conv5_3/linear/bn"
2705 |   type: "BatchNorm"
2706 |   bottom: "conv5_3/linear"
2707 |   top: "conv5_3/linear/bn"
2708 |   param {
2709 |     lr_mult: 0
2710 |     decay_mult: 0
2711 |   }
2712 |   param {
2713 |     lr_mult: 0
2714 |     decay_mult: 0
2715 |   }
2716 |   param {
2717 |     lr_mult: 0
2718 |     decay_mult: 0
2719 |   }
2720 |   batch_norm_param {
2721 |     use_global_stats: true
2722 |     eps: 1e-5
2723 |   }
2724 | }
2725 | layer {
2726 |   name: "conv5_3/linear/scale"
2727 |   type: "Scale"
2728 |   bottom: "conv5_3/linear/bn"
2729 |   top: "conv5_3/linear/bn"
2730 |   param {
2731 |     lr_mult: 1
2732 |     decay_mult: 0
2733 |   }
2734 |   param {
2735 |     lr_mult: 1
2736 |     decay_mult: 0
2737 |   }
2738 |   scale_param {
2739 |     bias_term: true
2740 |   }
2741 | }
2742 | layer {
2743 |   name: "conv6_1/expand"
2744 |   type: "Convolution"
2745 |   bottom: "conv5_3/linear/bn"
2746 |   top: "conv6_1/expand"
2747 |   param {
2748 |     lr_mult: 1
2749 |     decay_mult: 1
2750 |   }
2751 |   convolution_param {
2752 |     num_output: 960
2753 |     bias_term: false
2754 |     kernel_size: 1
2755 |     weight_filler {
2756 |       type: "msra"
2757 |     }
2758 |   }
2759 | }
2760 | layer {
2761 |   name: "conv6_1/expand/bn"
2762 |   type: "BatchNorm"
2763 |   bottom: "conv6_1/expand"
2764 |   top: "conv6_1/expand/bn"
2765 |   param {
2766 |     lr_mult: 0
2767 |     decay_mult: 0
2768 |   }
2769 |   param {
2770 |     lr_mult: 0
2771 |     decay_mult: 0
2772 |   }
2773 |   param {
2774 |     lr_mult: 0
2775 |     decay_mult: 0
2776 |   }
2777 |   batch_norm_param {
2778 |     use_global_stats: true
2779 |     eps: 1e-5
2780 |   }
2781 | }
2782 | layer {
2783 |   name: "conv6_1/expand/scale"
2784 |   type: "Scale"
2785 |   bottom: "conv6_1/expand/bn"
2786 |   top: "conv6_1/expand/bn"
2787 |   param {
2788 |     lr_mult: 1
2789 |     decay_mult: 0
2790 |   }
2791 |   param {
2792 |     lr_mult: 1
2793 |     decay_mult: 0
2794 |   }
2795 |   scale_param {
2796 |     bias_term: true
2797 |   }
2798 | }
2799 | layer {
2800 |   name: "relu6_1/expand"
2801 |   type: "ReLU"
2802 |   bottom: "conv6_1/expand/bn"
2803 |   top: "conv6_1/expand/bn"
2804 | }
2805 | layer {
2806 |   name: "conv6_1/dwise"
2807 |   type: "Convolution"
2808 |   bottom: "conv6_1/expand/bn"
2809 |   top: "conv6_1/dwise"
2810 |   param {
2811 |     lr_mult: 1
2812 |     decay_mult: 1
2813 |   }
2814 |   convolution_param {
2815 |     num_output: 960
2816 |     bias_term: false
2817 |     pad: 1
2818 |     kernel_size: 3
2819 |     group: 960
2820 |     weight_filler {
2821 |       type: "msra"
2822 |     }
2823 |     engine: CAFFE
2824 |   }
2825 | }
2826 | layer {
2827 |   name: "conv6_1/dwise/bn"
2828 |   type: "BatchNorm"
2829 |   bottom: "conv6_1/dwise"
2830 |   top: "conv6_1/dwise/bn"
2831 |   param {
2832 |     lr_mult: 0
2833 |     decay_mult: 0
2834 |   }
2835 |   param {
2836 |     lr_mult: 0
2837 |     decay_mult: 0
2838 |   }
2839 |   param {
2840 |     lr_mult: 0
2841 |     decay_mult: 0
2842 |   }
2843 |   batch_norm_param {
2844 |     use_global_stats: true
2845 |     eps: 1e-5
2846 |   }
2847 | }
2848 | layer {
2849 |   name: "conv6_1/dwise/scale"
2850 |   type: "Scale"
2851 |   bottom: "conv6_1/dwise/bn"
2852 |   top: "conv6_1/dwise/bn"
2853 |   param {
2854 |     lr_mult: 1
2855 |     decay_mult: 0
2856 |   }
2857 |   param {
2858 |     lr_mult: 1
2859 |     decay_mult: 0
2860 |   }
2861 |   scale_param {
2862 |     bias_term: true
2863 |   }
2864 | }
2865 | layer {
2866 |   name: "relu6_1/dwise"
2867 |   type: "ReLU"
2868 |   bottom: "conv6_1/dwise/bn"
2869 |   top: "conv6_1/dwise/bn"
2870 | }
2871 | layer {
2872 |   name: "conv6_1/linear"
2873 |   type: "Convolution"
2874 |   bottom: "conv6_1/dwise/bn"
2875 |   top: "conv6_1/linear"
2876 |   param {
2877 |     lr_mult: 1
2878 |     decay_mult: 1
2879 |   }
2880 |   convolution_param {
2881 |     num_output: 160
2882 |     bias_term: false
2883 |     kernel_size: 1
2884 |     weight_filler {
2885 |       type: "msra"
2886 |     }
2887 |   }
2888 | }
2889 | layer {
2890 |   name: "conv6_1/linear/bn"
2891 |   type: "BatchNorm"
2892 |   bottom: "conv6_1/linear"
2893 |   top: "conv6_1/linear/bn"
2894 |   param {
2895 |     lr_mult: 0
2896 |     decay_mult: 0
2897 |   }
2898 |   param {
2899 |     lr_mult: 0
2900 |     decay_mult: 0
2901 |   }
2902 |   param {
2903 |     lr_mult: 0
2904 |     decay_mult: 0
2905 |   }
2906 |   batch_norm_param {
2907 |     use_global_stats: true
2908 |     eps: 1e-5
2909 |   }
2910 | }
2911 | layer {
2912 |   name: "conv6_1/linear/scale"
2913 |   type: "Scale"
2914 |   bottom: "conv6_1/linear/bn"
2915 |   top: "conv6_1/linear/bn"
2916 |   param {
2917 |     lr_mult: 1
2918 |     decay_mult: 0
2919 |   }
2920 |   param {
2921 |     lr_mult: 1
2922 |     decay_mult: 0
2923 |   }
2924 |   scale_param {
2925 |     bias_term: true
2926 |   }
2927 | }
2928 | layer {
2929 |   name: "block_6_1"
2930 |   type: "Eltwise"
2931 |   bottom: "conv5_3/linear/bn"
2932 |   bottom: "conv6_1/linear/bn"
2933 |   top: "block_6_1"
2934 | }
2935 | layer {
2936 |   name: "conv6_2/expand"
2937 |   type: "Convolution"
2938 |   bottom: "block_6_1"
2939 |   top: "conv6_2/expand"
2940 |   param {
2941 |     lr_mult: 1
2942 |     decay_mult: 1
2943 |   }
2944 |   convolution_param {
2945 |     num_output: 960
2946 |     bias_term: false
2947 |     kernel_size: 1
2948 |     weight_filler {
2949 |       type: "msra"
2950 |     }
2951 |   }
2952 | }
2953 | layer {
2954 |   name: "conv6_2/expand/bn"
2955 |   type: "BatchNorm"
2956 |   bottom: "conv6_2/expand"
2957 |   top: "conv6_2/expand/bn"
2958 |   param {
2959 |     lr_mult: 0
2960 |     decay_mult: 0
2961 |   }
2962 |   param {
2963 |     lr_mult: 0
2964 |     decay_mult: 0
2965 |   }
2966 |   param {
2967 |     lr_mult: 0
2968 |     decay_mult: 0
2969 |   }
2970 |   batch_norm_param {
2971 |     use_global_stats: true
2972 |     eps: 1e-5
2973 |   }
2974 | }
2975 | layer {
2976 |   name: "conv6_2/expand/scale"
2977 |   type: "Scale"
2978 |   bottom: "conv6_2/expand/bn"
2979 |   top: "conv6_2/expand/bn"
2980 |   param {
2981 |     lr_mult: 1
2982 |     decay_mult: 0
2983 |   }
2984 |   param {
2985 |     lr_mult: 1
2986 |     decay_mult: 0
2987 |   }
2988 |   scale_param {
2989 |     bias_term: true
2990 |   }
2991 | }
2992 | layer {
2993 |   name: "relu6_2/expand"
2994 |   type: "ReLU"
2995 |   bottom: "conv6_2/expand/bn"
2996 |   top: "conv6_2/expand/bn"
2997 | }
2998 | layer {
2999 |   name: "conv6_2/dwise"
3000 |   type: "Convolution"
3001 |   bottom: "conv6_2/expand/bn"
3002 |   top: "conv6_2/dwise"
3003 |   param {
3004 |     lr_mult: 1
3005 |     decay_mult: 1
3006 |   }
3007 |   convolution_param {
3008 |     num_output: 960
3009 |     bias_term: false
3010 |     pad: 1
3011 |     kernel_size: 3
3012 |     group: 960
3013 |     weight_filler {
3014 |       type: "msra"
3015 |     }
3016 |     engine: CAFFE
3017 |   }
3018 | }
3019 | layer {
3020 |   name: "conv6_2/dwise/bn"
3021 |   type: "BatchNorm"
3022 |   bottom: "conv6_2/dwise"
3023 |   top: "conv6_2/dwise/bn"
3024 |   param {
3025 |     lr_mult: 0
3026 |     decay_mult: 0
3027 |   }
3028 |   param {
3029 |     lr_mult: 0
3030 |     decay_mult: 0
3031 |   }
3032 |   param {
3033 |     lr_mult: 0
3034 |     decay_mult: 0
3035 |   }
3036 |   batch_norm_param {
3037 |     use_global_stats: true
3038 |     eps: 1e-5
3039 |   }
3040 | }
3041 | layer {
3042 |   name: "conv6_2/dwise/scale"
3043 |   type: "Scale"
3044 |   bottom: "conv6_2/dwise/bn"
3045 |   top: "conv6_2/dwise/bn"
3046 |   param {
3047 |     lr_mult: 1
3048 |     decay_mult: 0
3049 |   }
3050 |   param {
3051 |     lr_mult: 1
3052 |     decay_mult: 0
3053 |   }
3054 |   scale_param {
3055 |     bias_term: true
3056 |   }
3057 | }
3058 | layer {
3059 |   name: "relu6_2/dwise"
3060 |   type: "ReLU"
3061 |   bottom: "conv6_2/dwise/bn"
3062 |   top: "conv6_2/dwise/bn"
3063 | }
3064 | layer {
3065 |   name: "conv6_2/linear"
3066 |   type: "Convolution"
3067 |   bottom: "conv6_2/dwise/bn"
3068 |   top: "conv6_2/linear"
3069 |   param {
3070 |     lr_mult: 1
3071 |     decay_mult: 1
3072 |   }
3073 |   convolution_param {
3074 |     num_output: 160
3075 |     bias_term: false
3076 |     kernel_size: 1
3077 |     weight_filler {
3078 |       type: "msra"
3079 |     }
3080 |   }
3081 | }
3082 | layer {
3083 |   name: "conv6_2/linear/bn"
3084 |   type: "BatchNorm"
3085 |   bottom: "conv6_2/linear"
3086 |   top: "conv6_2/linear/bn"
3087 |   param {
3088 |     lr_mult: 0
3089 |     decay_mult: 0
3090 |   }
3091 |   param {
3092 |     lr_mult: 0
3093 |     decay_mult: 0
3094 |   }
3095 |   param {
3096 |     lr_mult: 0
3097 |     decay_mult: 0
3098 |   }
3099 |   batch_norm_param {
3100 |     use_global_stats: true
3101 |     eps: 1e-5
3102 |   }
3103 | }
3104 | layer {
3105 |   name: "conv6_2/linear/scale"
3106 |   type: "Scale"
3107 |   bottom: "conv6_2/linear/bn"
3108 |   top: "conv6_2/linear/bn"
3109 |   param {
3110 |     lr_mult: 1
3111 |     decay_mult: 0
3112 |   }
3113 |   param {
3114 |     lr_mult: 1
3115 |     decay_mult: 0
3116 |   }
3117 |   scale_param {
3118 |     bias_term: true
3119 |   }
3120 | }
3121 | layer {
3122 |   name: "block_6_2"
3123 |   type: "Eltwise"
3124 |   bottom: "block_6_1"
3125 |   bottom: "conv6_2/linear/bn"
3126 |   top: "block_6_2"
3127 | }
3128 | layer {
3129 |   name: "conv6_3/expand"
3130 |   type: "Convolution"
3131 |   bottom: "block_6_2"
3132 |   top: "conv6_3/expand"
3133 |   param {
3134 |     lr_mult: 1
3135 |     decay_mult: 1
3136 |   }
3137 |   convolution_param {
3138 |     num_output: 960
3139 |     bias_term: false
3140 |     kernel_size: 1
3141 |     weight_filler {
3142 |       type: "msra"
3143 |     }
3144 |   }
3145 | }
3146 | layer {
3147 |   name: "conv6_3/expand/bn"
3148 |   type: "BatchNorm"
3149 |   bottom: "conv6_3/expand"
3150 |   top: "conv6_3/expand/bn"
3151 |   param {
3152 |     lr_mult: 0
3153 |     decay_mult: 0
3154 |   }
3155 |   param {
3156 |     lr_mult: 0
3157 |     decay_mult: 0
3158 |   }
3159 |   param {
3160 |     lr_mult: 0
3161 |     decay_mult: 0
3162 |   }
3163 |   batch_norm_param {
3164 |     use_global_stats: true
3165 |     eps: 1e-5
3166 |   }
3167 | }
3168 | layer {
3169 |   name: "conv6_3/expand/scale"
3170 |   type: "Scale"
3171 |   bottom: "conv6_3/expand/bn"
3172 |   top: "conv6_3/expand/bn"
3173 |   param {
3174 |     lr_mult: 1
3175 |     decay_mult: 0
3176 |   }
3177 |   param {
3178 |     lr_mult: 1
3179 |     decay_mult: 0
3180 |   }
3181 |   scale_param {
3182 |     bias_term: true
3183 |   }
3184 | }
3185 | layer {
3186 |   name: "relu6_3/expand"
3187 |   type: "ReLU"
3188 |   bottom: "conv6_3/expand/bn"
3189 |   top: "conv6_3/expand/bn"
3190 | }
3191 | layer {
3192 |   name: "conv6_3/dwise"
3193 |   type: "Convolution"
3194 |   bottom: "conv6_3/expand/bn"
3195 |   top: "conv6_3/dwise"
3196 |   param {
3197 |     lr_mult: 1
3198 |     decay_mult: 1
3199 |   }
3200 |   convolution_param {
3201 |     num_output: 960
3202 |     bias_term: false
3203 |     pad: 1
3204 |     kernel_size: 3
3205 |     group: 960
3206 |     weight_filler {
3207 |       type: "msra"
3208 |     }
3209 |     engine: CAFFE
3210 |   }
3211 | }
3212 | layer {
3213 |   name: "conv6_3/dwise/bn"
3214 |   type: "BatchNorm"
3215 |   bottom: "conv6_3/dwise"
3216 |   top: "conv6_3/dwise/bn"
3217 |   param {
3218 |     lr_mult: 0
3219 |     decay_mult: 0
3220 |   }
3221 |   param {
3222 |     lr_mult: 0
3223 |     decay_mult: 0
3224 |   }
3225 |   param {
3226 |     lr_mult: 0
3227 |     decay_mult: 0
3228 |   }
3229 |   batch_norm_param {
3230 |     use_global_stats: true
3231 |     eps: 1e-5
3232 |   }
3233 | }
3234 | layer {
3235 |   name: "conv6_3/dwise/scale"
3236 |   type: "Scale"
3237 |   bottom: "conv6_3/dwise/bn"
3238 |   top: "conv6_3/dwise/bn"
3239 |   param {
3240 |     lr_mult: 1
3241 |     decay_mult: 0
3242 |   }
3243 |   param {
3244 |     lr_mult: 1
3245 |     decay_mult: 0
3246 |   }
3247 |   scale_param {
3248 |     bias_term: true
3249 |   }
3250 | }
3251 | layer {
3252 |   name: "relu6_3/dwise"
3253 |   type: "ReLU"
3254 |   bottom: "conv6_3/dwise/bn"
3255 |   top: "conv6_3/dwise/bn"
3256 | }
3257 | layer {
3258 |   name: "conv6_3/linear"
3259 |   type: "Convolution"
3260 |   bottom: "conv6_3/dwise/bn"
3261 |   top: "conv6_3/linear"
3262 |   param {
3263 |     lr_mult: 1
3264 |     decay_mult: 1
3265 |   }
3266 |   convolution_param {
3267 |     num_output: 320
3268 |     bias_term: false
3269 |     kernel_size: 1
3270 |     weight_filler {
3271 |       type: "msra"
3272 |     }
3273 |   }
3274 | }
3275 | layer {
3276 |   name: "conv6_3/linear/bn"
3277 |   type: "BatchNorm"
3278 |   bottom: "conv6_3/linear"
3279 |   top: "conv6_3/linear/bn"
3280 |   param {
3281 |     lr_mult: 0
3282 |     decay_mult: 0
3283 |   }
3284 |   param {
3285 |     lr_mult: 0
3286 |     decay_mult: 0
3287 |   }
3288 |   param {
3289 |     lr_mult: 0
3290 |     decay_mult: 0
3291 |   }
3292 |   batch_norm_param {
3293 |     use_global_stats: true
3294 |     eps: 1e-5
3295 |   }
3296 | }
3297 | layer {
3298 |   name: "conv6_3/linear/scale"
3299 |   type: "Scale"
3300 |   bottom: "conv6_3/linear/bn"
3301 |   top: "conv6_3/linear/bn"
3302 |   param {
3303 |     lr_mult: 1
3304 |     decay_mult: 0
3305 |   }
3306 |   param {
3307 |     lr_mult: 1
3308 |     decay_mult: 0
3309 |   }
3310 |   scale_param {
3311 |     bias_term: true
3312 |   }
3313 | }
3314 | layer {
3315 |   name: "conv6_4"
3316 |   type: "Convolution"
3317 |   bottom: "conv6_3/linear/bn"
3318 |   top: "conv6_4"
3319 |   param {
3320 |     lr_mult: 1
3321 |     decay_mult: 1
3322 |   }
3323 |   convolution_param {
3324 |     num_output: 1280
3325 |     bias_term: false
3326 |     kernel_size: 1
3327 |     weight_filler {
3328 |       type: "msra"
3329 |     }
3330 |   }
3331 | }
3332 | layer {
3333 |   name: "conv6_4/bn"
3334 |   type: "BatchNorm"
3335 |   bottom: "conv6_4"
3336 |   top: "conv6_4/bn"
3337 |   param {
3338 |     lr_mult: 0
3339 |     decay_mult: 0
3340 |   }
3341 |   param {
3342 |     lr_mult: 0
3343 |     decay_mult: 0
3344 |   }
3345 |   param {
3346 |     lr_mult: 0
3347 |     decay_mult: 0
3348 |   }
3349 |   batch_norm_param {
3350 |     use_global_stats: true
3351 |     eps: 1e-5
3352 |   }
3353 | }
3354 | layer {
3355 |   name: "conv6_4/scale"
3356 |   type: "Scale"
3357 |   bottom: "conv6_4/bn"
3358 |   top: "conv6_4/bn"
3359 |   param {
3360 |     lr_mult: 1
3361 |     decay_mult: 0
3362 |   }
3363 |   param {
3364 |     lr_mult: 1
3365 |     decay_mult: 0
3366 |   }
3367 |   scale_param {
3368 |     bias_term: true
3369 |   }
3370 | }
3371 | layer {
3372 |   name: "relu6_4"
3373 |   type: "ReLU"
3374 |   bottom: "conv6_4/bn"
3375 |   top: "conv6_4/bn"
3376 | }
3377 | layer {
3378 |   name: "pool6"
3379 |   type: "Pooling"
3380 |   bottom: "conv6_4/bn"
3381 |   top: "pool6"
3382 |   pooling_param {
3383 |     pool: AVE
3384 |     global_pooling: true
3385 |   }
3386 | }
3387 | layer {
3388 |   name: "fc7"
3389 |   type: "Convolution"
3390 |   bottom: "pool6"
3391 |   top: "fc7"
3392 |   param {
3393 |     lr_mult: 1
3394 |     decay_mult: 1
3395 |   }
3396 |   param {
3397 |     lr_mult: 2
3398 |     decay_mult: 0
3399 |   }
3400 |   convolution_param {
3401 |     num_output: 1000
3402 |     kernel_size: 1
3403 |     weight_filler {
3404 |       type: "msra"
3405 |     }
3406 |     bias_filler {
3407 |       type: "constant"
3408 |       value: 0
3409 |     }
3410 |   }
3411 | }
3412 | layer {
3413 |   name: "prob"
3414 |   type: "Softmax"
3415 |   bottom: "fc7"
3416 |   top: "prob"
3417 | }


--------------------------------------------------------------------------------
/synset.txt:
--------------------------------------------------------------------------------
   1 | 'n01440764 tench, Tinca tinca'
   2 | 'n01443537 goldfish, Carassius auratus'
   3 | 'n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias'
   4 | 'n01491361 tiger shark, Galeocerdo cuvieri'
   5 | 'n01494475 hammerhead, hammerhead shark'
   6 | 'n01496331 electric ray, crampfish, numbfish, torpedo'
   7 | 'n01498041 stingray'
   8 | 'n01514668 cock'
   9 | 'n01514859 hen'
  10 | 'n01518878 ostrich, Struthio camelus'
  11 | 'n01530575 brambling, Fringilla montifringilla'
  12 | 'n01531178 goldfinch, Carduelis carduelis'
  13 | 'n01532829 house finch, linnet, Carpodacus mexicanus'
  14 | 'n01534433 junco, snowbird'
  15 | 'n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea'
  16 | 'n01558993 robin, American robin, Turdus migratorius'
  17 | 'n01560419 bulbul'
  18 | 'n01580077 jay'
  19 | 'n01582220 magpie'
  20 | 'n01592084 chickadee'
  21 | 'n01601694 water ouzel, dipper'
  22 | 'n01608432 kite'
  23 | 'n01614925 bald eagle, American eagle, Haliaeetus leucocephalus'
  24 | 'n01616318 vulture'
  25 | 'n01622779 great grey owl, great gray owl, Strix nebulosa'
  26 | 'n01629819 European fire salamander, Salamandra salamandra'
  27 | 'n01630670 common newt, Triturus vulgaris'
  28 | 'n01631663 eft'
  29 | 'n01632458 spotted salamander, Ambystoma maculatum'
  30 | 'n01632777 axolotl, mud puppy, Ambystoma mexicanum'
  31 | 'n01641577 bullfrog, Rana catesbeiana'
  32 | 'n01644373 tree frog, tree-frog'
  33 | 'n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui'
  34 | 'n01664065 loggerhead, loggerhead turtle, Caretta caretta'
  35 | 'n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea'
  36 | 'n01667114 mud turtle'
  37 | 'n01667778 terrapin'
  38 | 'n01669191 box turtle, box tortoise'
  39 | 'n01675722 banded gecko'
  40 | 'n01677366 common iguana, iguana, Iguana iguana'
  41 | 'n01682714 American chameleon, anole, Anolis carolinensis'
  42 | 'n01685808 whiptail, whiptail lizard'
  43 | 'n01687978 agama'
  44 | 'n01688243 frilled lizard, Chlamydosaurus kingi'
  45 | 'n01689811 alligator lizard'
  46 | 'n01692333 Gila monster, Heloderma suspectum'
  47 | 'n01693334 green lizard, Lacerta viridis'
  48 | 'n01694178 African chameleon, Chamaeleo chamaeleon'
  49 | 'n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis'
  50 | 'n01697457 African crocodile, Nile crocodile, Crocodylus niloticus'
  51 | 'n01698640 American alligator, Alligator mississipiensis'
  52 | 'n01704323 triceratops'
  53 | 'n01728572 thunder snake, worm snake, Carphophis amoenus'
  54 | 'n01728920 ringneck snake, ring-necked snake, ring snake'
  55 | 'n01729322 hognose snake, puff adder, sand viper'
  56 | 'n01729977 green snake, grass snake'
  57 | 'n01734418 king snake, kingsnake'
  58 | 'n01735189 garter snake, grass snake'
  59 | 'n01737021 water snake'
  60 | 'n01739381 vine snake'
  61 | 'n01740131 night snake, Hypsiglena torquata'
  62 | 'n01742172 boa constrictor, Constrictor constrictor'
  63 | 'n01744401 rock python, rock snake, Python sebae'
  64 | 'n01748264 Indian cobra, Naja naja'
  65 | 'n01749939 green mamba'
  66 | 'n01751748 sea snake'
  67 | 'n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus'
  68 | 'n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus'
  69 | 'n01756291 sidewinder, horned rattlesnake, Crotalus cerastes'
  70 | 'n01768244 trilobite'
  71 | 'n01770081 harvestman, daddy longlegs, Phalangium opilio'
  72 | 'n01770393 scorpion'
  73 | 'n01773157 black and gold garden spider, Argiope aurantia'
  74 | 'n01773549 barn spider, Araneus cavaticus'
  75 | 'n01773797 garden spider, Aranea diademata'
  76 | 'n01774384 black widow, Latrodectus mactans'
  77 | 'n01774750 tarantula'
  78 | 'n01775062 wolf spider, hunting spider'
  79 | 'n01776313 tick'
  80 | 'n01784675 centipede'
  81 | 'n01795545 black grouse'
  82 | 'n01796340 ptarmigan'
  83 | 'n01797886 ruffed grouse, partridge, Bonasa umbellus'
  84 | 'n01798484 prairie chicken, prairie grouse, prairie fowl'
  85 | 'n01806143 peacock'
  86 | 'n01806567 quail'
  87 | 'n01807496 partridge'
  88 | 'n01817953 African grey, African gray, Psittacus erithacus'
  89 | 'n01818515 macaw'
  90 | 'n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita'
  91 | 'n01820546 lorikeet'
  92 | 'n01824575 coucal'
  93 | 'n01828970 bee eater'
  94 | 'n01829413 hornbill'
  95 | 'n01833805 hummingbird'
  96 | 'n01843065 jacamar'
  97 | 'n01843383 toucan'
  98 | 'n01847000 drake'
  99 | 'n01855032 red-breasted merganser, Mergus serrator'
 100 | 'n01855672 goose'
 101 | 'n01860187 black swan, Cygnus atratus'
 102 | 'n01871265 tusker'
 103 | 'n01872401 echidna, spiny anteater, anteater'
 104 | 'n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus'
 105 | 'n01877812 wallaby, brush kangaroo'
 106 | 'n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus'
 107 | 'n01883070 wombat'
 108 | 'n01910747 jellyfish'
 109 | 'n01914609 sea anemone, anemone'
 110 | 'n01917289 brain coral'
 111 | 'n01924916 flatworm, platyhelminth'
 112 | 'n01930112 nematode, nematode worm, roundworm'
 113 | 'n01943899 conch'
 114 | 'n01944390 snail'
 115 | 'n01945685 slug'
 116 | 'n01950731 sea slug, nudibranch'
 117 | 'n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore'
 118 | 'n01968897 chambered nautilus, pearly nautilus, nautilus'
 119 | 'n01978287 Dungeness crab, Cancer magister'
 120 | 'n01978455 rock crab, Cancer irroratus'
 121 | 'n01980166 fiddler crab'
 122 | 'n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica'
 123 | 'n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus'
 124 | 'n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish'
 125 | 'n01985128 crayfish, crawfish, crawdad, crawdaddy'
 126 | 'n01986214 hermit crab'
 127 | 'n01990800 isopod'
 128 | 'n02002556 white stork, Ciconia ciconia'
 129 | 'n02002724 black stork, Ciconia nigra'
 130 | 'n02006656 spoonbill'
 131 | 'n02007558 flamingo'
 132 | 'n02009229 little blue heron, Egretta caerulea'
 133 | 'n02009912 American egret, great white heron, Egretta albus'
 134 | 'n02011460 bittern'
 135 | 'n02012849 crane'
 136 | 'n02013706 limpkin, Aramus pictus'
 137 | 'n02017213 European gallinule, Porphyrio porphyrio'
 138 | 'n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana'
 139 | 'n02018795 bustard'
 140 | 'n02025239 ruddy turnstone, Arenaria interpres'
 141 | 'n02027492 red-backed sandpiper, dunlin, Erolia alpina'
 142 | 'n02028035 redshank, Tringa totanus'
 143 | 'n02033041 dowitcher'
 144 | 'n02037110 oystercatcher, oyster catcher'
 145 | 'n02051845 pelican'
 146 | 'n02056570 king penguin, Aptenodytes patagonica'
 147 | 'n02058221 albatross, mollymawk'
 148 | 'n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus'
 149 | 'n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca'
 150 | 'n02074367 dugong, Dugong dugon'
 151 | 'n02077923 sea lion'
 152 | 'n02085620 Chihuahua'
 153 | 'n02085782 Japanese spaniel'
 154 | 'n02085936 Maltese dog, Maltese terrier, Maltese'
 155 | 'n02086079 Pekinese, Pekingese, Peke'
 156 | 'n02086240 Shih-Tzu'
 157 | 'n02086646 Blenheim spaniel'
 158 | 'n02086910 papillon'
 159 | 'n02087046 toy terrier'
 160 | 'n02087394 Rhodesian ridgeback'
 161 | 'n02088094 Afghan hound, Afghan'
 162 | 'n02088238 basset, basset hound'
 163 | 'n02088364 beagle'
 164 | 'n02088466 bloodhound, sleuthhound'
 165 | 'n02088632 bluetick'
 166 | 'n02089078 black-and-tan coonhound'
 167 | 'n02089867 Walker hound, Walker foxhound'
 168 | 'n02089973 English foxhound'
 169 | 'n02090379 redbone'
 170 | 'n02090622 borzoi, Russian wolfhound'
 171 | 'n02090721 Irish wolfhound'
 172 | 'n02091032 Italian greyhound'
 173 | 'n02091134 whippet'
 174 | 'n02091244 Ibizan hound, Ibizan Podenco'
 175 | 'n02091467 Norwegian elkhound, elkhound'
 176 | 'n02091635 otterhound, otter hound'
 177 | 'n02091831 Saluki, gazelle hound'
 178 | 'n02092002 Scottish deerhound, deerhound'
 179 | 'n02092339 Weimaraner'
 180 | 'n02093256 Staffordshire bullterrier, Staffordshire bull terrier'
 181 | 'n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier'
 182 | 'n02093647 Bedlington terrier'
 183 | 'n02093754 Border terrier'
 184 | 'n02093859 Kerry blue terrier'
 185 | 'n02093991 Irish terrier'
 186 | 'n02094114 Norfolk terrier'
 187 | 'n02094258 Norwich terrier'
 188 | 'n02094433 Yorkshire terrier'
 189 | 'n02095314 wire-haired fox terrier'
 190 | 'n02095570 Lakeland terrier'
 191 | 'n02095889 Sealyham terrier, Sealyham'
 192 | 'n02096051 Airedale, Airedale terrier'
 193 | 'n02096177 cairn, cairn terrier'
 194 | 'n02096294 Australian terrier'
 195 | 'n02096437 Dandie Dinmont, Dandie Dinmont terrier'
 196 | 'n02096585 Boston bull, Boston terrier'
 197 | 'n02097047 miniature schnauzer'
 198 | 'n02097130 giant schnauzer'
 199 | 'n02097209 standard schnauzer'
 200 | 'n02097298 Scotch terrier, Scottish terrier, Scottie'
 201 | 'n02097474 Tibetan terrier, chrysanthemum dog'
 202 | 'n02097658 silky terrier, Sydney silky'
 203 | 'n02098105 soft-coated wheaten terrier'
 204 | 'n02098286 West Highland white terrier'
 205 | 'n02098413 Lhasa, Lhasa apso'
 206 | 'n02099267 flat-coated retriever'
 207 | 'n02099429 curly-coated retriever'
 208 | 'n02099601 golden retriever'
 209 | 'n02099712 Labrador retriever'
 210 | 'n02099849 Chesapeake Bay retriever'
 211 | 'n02100236 German short-haired pointer'
 212 | 'n02100583 vizsla, Hungarian pointer'
 213 | 'n02100735 English setter'
 214 | 'n02100877 Irish setter, red setter'
 215 | 'n02101006 Gordon setter'
 216 | 'n02101388 Brittany spaniel'
 217 | 'n02101556 clumber, clumber spaniel'
 218 | 'n02102040 English springer, English springer spaniel'
 219 | 'n02102177 Welsh springer spaniel'
 220 | 'n02102318 cocker spaniel, English cocker spaniel, cocker'
 221 | 'n02102480 Sussex spaniel'
 222 | 'n02102973 Irish water spaniel'
 223 | 'n02104029 kuvasz'
 224 | 'n02104365 schipperke'
 225 | 'n02105056 groenendael'
 226 | 'n02105162 malinois'
 227 | 'n02105251 briard'
 228 | 'n02105412 kelpie'
 229 | 'n02105505 komondor'
 230 | 'n02105641 Old English sheepdog, bobtail'
 231 | 'n02105855 Shetland sheepdog, Shetland sheep dog, Shetland'
 232 | 'n02106030 collie'
 233 | 'n02106166 Border collie'
 234 | 'n02106382 Bouvier des Flandres, Bouviers des Flandres'
 235 | 'n02106550 Rottweiler'
 236 | 'n02106662 German shepherd, German shepherd dog, German police dog, alsatian'
 237 | 'n02107142 Doberman, Doberman pinscher'
 238 | 'n02107312 miniature pinscher'
 239 | 'n02107574 Greater Swiss Mountain dog'
 240 | 'n02107683 Bernese mountain dog'
 241 | 'n02107908 Appenzeller'
 242 | 'n02108000 EntleBucher'
 243 | 'n02108089 boxer'
 244 | 'n02108422 bull mastiff'
 245 | 'n02108551 Tibetan mastiff'
 246 | 'n02108915 French bulldog'
 247 | 'n02109047 Great Dane'
 248 | 'n02109525 Saint Bernard, St Bernard'
 249 | 'n02109961 Eskimo dog, husky'
 250 | 'n02110063 malamute, malemute, Alaskan malamute'
 251 | 'n02110185 Siberian husky'
 252 | 'n02110341 dalmatian, coach dog, carriage dog'
 253 | 'n02110627 affenpinscher, monkey pinscher, monkey dog'
 254 | 'n02110806 basenji'
 255 | 'n02110958 pug, pug-dog'
 256 | 'n02111129 Leonberg'
 257 | 'n02111277 Newfoundland, Newfoundland dog'
 258 | 'n02111500 Great Pyrenees'
 259 | 'n02111889 Samoyed, Samoyede'
 260 | 'n02112018 Pomeranian'
 261 | 'n02112137 chow, chow chow'
 262 | 'n02112350 keeshond'
 263 | 'n02112706 Brabancon griffon'
 264 | 'n02113023 Pembroke, Pembroke Welsh corgi'
 265 | 'n02113186 Cardigan, Cardigan Welsh corgi'
 266 | 'n02113624 toy poodle'
 267 | 'n02113712 miniature poodle'
 268 | 'n02113799 standard poodle'
 269 | 'n02113978 Mexican hairless'
 270 | 'n02114367 timber wolf, grey wolf, gray wolf, Canis lupus'
 271 | 'n02114548 white wolf, Arctic wolf, Canis lupus tundrarum'
 272 | 'n02114712 red wolf, maned wolf, Canis rufus, Canis niger'
 273 | 'n02114855 coyote, prairie wolf, brush wolf, Canis latrans'
 274 | 'n02115641 dingo, warrigal, warragal, Canis dingo'
 275 | 'n02115913 dhole, Cuon alpinus'
 276 | 'n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus'
 277 | 'n02117135 hyena, hyaena'
 278 | 'n02119022 red fox, Vulpes vulpes'
 279 | 'n02119789 kit fox, Vulpes macrotis'
 280 | 'n02120079 Arctic fox, white fox, Alopex lagopus'
 281 | 'n02120505 grey fox, gray fox, Urocyon cinereoargenteus'
 282 | 'n02123045 tabby, tabby cat'
 283 | 'n02123159 tiger cat'
 284 | 'n02123394 Persian cat'
 285 | 'n02123597 Siamese cat, Siamese'
 286 | 'n02124075 Egyptian cat'
 287 | 'n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'
 288 | 'n02127052 lynx, catamount'
 289 | 'n02128385 leopard, Panthera pardus'
 290 | 'n02128757 snow leopard, ounce, Panthera uncia'
 291 | 'n02128925 jaguar, panther, Panthera onca, Felis onca'
 292 | 'n02129165 lion, king of beasts, Panthera leo'
 293 | 'n02129604 tiger, Panthera tigris'
 294 | 'n02130308 cheetah, chetah, Acinonyx jubatus'
 295 | 'n02132136 brown bear, bruin, Ursus arctos'
 296 | 'n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus'
 297 | 'n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus'
 298 | 'n02134418 sloth bear, Melursus ursinus, Ursus ursinus'
 299 | 'n02137549 mongoose'
 300 | 'n02138441 meerkat, mierkat'
 301 | 'n02165105 tiger beetle'
 302 | 'n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle'
 303 | 'n02167151 ground beetle, carabid beetle'
 304 | 'n02168699 long-horned beetle, longicorn, longicorn beetle'
 305 | 'n02169497 leaf beetle, chrysomelid'
 306 | 'n02172182 dung beetle'
 307 | 'n02174001 rhinoceros beetle'
 308 | 'n02177972 weevil'
 309 | 'n02190166 fly'
 310 | 'n02206856 bee'
 311 | 'n02219486 ant, emmet, pismire'
 312 | 'n02226429 grasshopper, hopper'
 313 | 'n02229544 cricket'
 314 | 'n02231487 walking stick, walkingstick, stick insect'
 315 | 'n02233338 cockroach, roach'
 316 | 'n02236044 mantis, mantid'
 317 | 'n02256656 cicada, cicala'
 318 | 'n02259212 leafhopper'
 319 | 'n02264363 lacewing, lacewing fly'
 320 | 'n02268443 dragonfly, darning needle, devil''s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk'
 321 | 'n02268853 damselfly'
 322 | 'n02276258 admiral'
 323 | 'n02277742 ringlet, ringlet butterfly'
 324 | 'n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus'
 325 | 'n02280649 cabbage butterfly'
 326 | 'n02281406 sulphur butterfly, sulfur butterfly'
 327 | 'n02281787 lycaenid, lycaenid butterfly'
 328 | 'n02317335 starfish, sea star'
 329 | 'n02319095 sea urchin'
 330 | 'n02321529 sea cucumber, holothurian'
 331 | 'n02325366 wood rabbit, cottontail, cottontail rabbit'
 332 | 'n02326432 hare'
 333 | 'n02328150 Angora, Angora rabbit'
 334 | 'n02342885 hamster'
 335 | 'n02346627 porcupine, hedgehog'
 336 | 'n02356798 fox squirrel, eastern fox squirrel, Sciurus niger'
 337 | 'n02361337 marmot'
 338 | 'n02363005 beaver'
 339 | 'n02364673 guinea pig, Cavia cobaya'
 340 | 'n02389026 sorrel'
 341 | 'n02391049 zebra'
 342 | 'n02395406 hog, pig, grunter, squealer, Sus scrofa'
 343 | 'n02396427 wild boar, boar, Sus scrofa'
 344 | 'n02397096 warthog'
 345 | 'n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius'
 346 | 'n02403003 ox'
 347 | 'n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis'
 348 | 'n02410509 bison'
 349 | 'n02412080 ram, tup'
 350 | 'n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis'
 351 | 'n02417914 ibex, Capra ibex'
 352 | 'n02422106 hartebeest'
 353 | 'n02422699 impala, Aepyceros melampus'
 354 | 'n02423022 gazelle'
 355 | 'n02437312 Arabian camel, dromedary, Camelus dromedarius'
 356 | 'n02437616 llama'
 357 | 'n02441942 weasel'
 358 | 'n02442845 mink'
 359 | 'n02443114 polecat, fitch, foulmart, foumart, Mustela putorius'
 360 | 'n02443484 black-footed ferret, ferret, Mustela nigripes'
 361 | 'n02444819 otter'
 362 | 'n02445715 skunk, polecat, wood pussy'
 363 | 'n02447366 badger'
 364 | 'n02454379 armadillo'
 365 | 'n02457408 three-toed sloth, ai, Bradypus tridactylus'
 366 | 'n02480495 orangutan, orang, orangutang, Pongo pygmaeus'
 367 | 'n02480855 gorilla, Gorilla gorilla'
 368 | 'n02481823 chimpanzee, chimp, Pan troglodytes'
 369 | 'n02483362 gibbon, Hylobates lar'
 370 | 'n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus'
 371 | 'n02484975 guenon, guenon monkey'
 372 | 'n02486261 patas, hussar monkey, Erythrocebus patas'
 373 | 'n02486410 baboon'
 374 | 'n02487347 macaque'
 375 | 'n02488291 langur'
 376 | 'n02488702 colobus, colobus monkey'
 377 | 'n02489166 proboscis monkey, Nasalis larvatus'
 378 | 'n02490219 marmoset'
 379 | 'n02492035 capuchin, ringtail, Cebus capucinus'
 380 | 'n02492660 howler monkey, howler'
 381 | 'n02493509 titi, titi monkey'
 382 | 'n02493793 spider monkey, Ateles geoffroyi'
 383 | 'n02494079 squirrel monkey, Saimiri sciureus'
 384 | 'n02497673 Madagascar cat, ring-tailed lemur, Lemur catta'
 385 | 'n02500267 indri, indris, Indri indri, Indri brevicaudatus'
 386 | 'n02504013 Indian elephant, Elephas maximus'
 387 | 'n02504458 African elephant, Loxodonta africana'
 388 | 'n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens'
 389 | 'n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca'
 390 | 'n02514041 barracouta, snoek'
 391 | 'n02526121 eel'
 392 | 'n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch'
 393 | 'n02606052 rock beauty, Holocanthus tricolor'
 394 | 'n02607072 anemone fish'
 395 | 'n02640242 sturgeon'
 396 | 'n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus'
 397 | 'n02643566 lionfish'
 398 | 'n02655020 puffer, pufferfish, blowfish, globefish'
 399 | 'n02666196 abacus'
 400 | 'n02667093 abaya'
 401 | 'n02669723 academic gown, academic robe, judge''s robe'
 402 | 'n02672831 accordion, piano accordion, squeeze box'
 403 | 'n02676566 acoustic guitar'
 404 | 'n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier'
 405 | 'n02690373 airliner'
 406 | 'n02692877 airship, dirigible'
 407 | 'n02699494 altar'
 408 | 'n02701002 ambulance'
 409 | 'n02704792 amphibian, amphibious vehicle'
 410 | 'n02708093 analog clock'
 411 | 'n02727426 apiary, bee house'
 412 | 'n02730930 apron'
 413 | 'n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin'
 414 | 'n02749479 assault rifle, assault gun'
 415 | 'n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack'
 416 | 'n02776631 bakery, bakeshop, bakehouse'
 417 | 'n02777292 balance beam, beam'
 418 | 'n02782093 balloon'
 419 | 'n02783161 ballpoint, ballpoint pen, ballpen, Biro'
 420 | 'n02786058 Band Aid'
 421 | 'n02787622 banjo'
 422 | 'n02788148 bannister, banister, balustrade, balusters, handrail'
 423 | 'n02790996 barbell'
 424 | 'n02791124 barber chair'
 425 | 'n02791270 barbershop'
 426 | 'n02793495 barn'
 427 | 'n02794156 barometer'
 428 | 'n02795169 barrel, cask'
 429 | 'n02797295 barrow, garden cart, lawn cart, wheelbarrow'
 430 | 'n02799071 baseball'
 431 | 'n02802426 basketball'
 432 | 'n02804414 bassinet'
 433 | 'n02804610 bassoon'
 434 | 'n02807133 bathing cap, swimming cap'
 435 | 'n02808304 bath towel'
 436 | 'n02808440 bathtub, bathing tub, bath, tub'
 437 | 'n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon'
 438 | 'n02814860 beacon, lighthouse, beacon light, pharos'
 439 | 'n02815834 beaker'
 440 | 'n02817516 bearskin, busby, shako'
 441 | 'n02823428 beer bottle'
 442 | 'n02823750 beer glass'
 443 | 'n02825657 bell cote, bell cot'
 444 | 'n02834397 bib'
 445 | 'n02835271 bicycle-built-for-two, tandem bicycle, tandem'
 446 | 'n02837789 bikini, two-piece'
 447 | 'n02840245 binder, ring-binder'
 448 | 'n02841315 binoculars, field glasses, opera glasses'
 449 | 'n02843684 birdhouse'
 450 | 'n02859443 boathouse'
 451 | 'n02860847 bobsled, bobsleigh, bob'
 452 | 'n02865351 bolo tie, bolo, bola tie, bola'
 453 | 'n02869837 bonnet, poke bonnet'
 454 | 'n02870880 bookcase'
 455 | 'n02871525 bookshop, bookstore, bookstall'
 456 | 'n02877765 bottlecap'
 457 | 'n02879718 bow'
 458 | 'n02883205 bow tie, bow-tie, bowtie'
 459 | 'n02892201 brass, memorial tablet, plaque'
 460 | 'n02892767 brassiere, bra, bandeau'
 461 | 'n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty'
 462 | 'n02895154 breastplate, aegis, egis'
 463 | 'n02906734 broom'
 464 | 'n02909870 bucket, pail'
 465 | 'n02910353 buckle'
 466 | 'n02916936 bulletproof vest'
 467 | 'n02917067 bullet train, bullet'
 468 | 'n02927161 butcher shop, meat market'
 469 | 'n02930766 cab, hack, taxi, taxicab'
 470 | 'n02939185 caldron, cauldron'
 471 | 'n02948072 candle, taper, wax light'
 472 | 'n02950826 cannon'
 473 | 'n02951358 canoe'
 474 | 'n02951585 can opener, tin opener'
 475 | 'n02963159 cardigan'
 476 | 'n02965783 car mirror'
 477 | 'n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig'
 478 | 'n02966687 carpenter''s kit, tool kit'
 479 | 'n02971356 carton'
 480 | 'n02974003 car wheel'
 481 | 'n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM'
 482 | 'n02978881 cassette'
 483 | 'n02979186 cassette player'
 484 | 'n02980441 castle'
 485 | 'n02981792 catamaran'
 486 | 'n02988304 CD player'
 487 | 'n02992211 cello, violoncello'
 488 | 'n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone'
 489 | 'n02999410 chain'
 490 | 'n03000134 chainlink fence'
 491 | 'n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour'
 492 | 'n03000684 chain saw, chainsaw'
 493 | 'n03014705 chest'
 494 | 'n03016953 chiffonier, commode'
 495 | 'n03017168 chime, bell, gong'
 496 | 'n03018349 china cabinet, china closet'
 497 | 'n03026506 Christmas stocking'
 498 | 'n03028079 church, church building'
 499 | 'n03032252 cinema, movie theater, movie theatre, movie house, picture palace'
 500 | 'n03041632 cleaver, meat cleaver, chopper'
 501 | 'n03042490 cliff dwelling'
 502 | 'n03045698 cloak'
 503 | 'n03047690 clog, geta, patten, sabot'
 504 | 'n03062245 cocktail shaker'
 505 | 'n03063599 coffee mug'
 506 | 'n03063689 coffeepot'
 507 | 'n03065424 coil, spiral, volute, whorl, helix'
 508 | 'n03075370 combination lock'
 509 | 'n03085013 computer keyboard, keypad'
 510 | 'n03089624 confectionery, confectionary, candy store'
 511 | 'n03095699 container ship, containership, container vessel'
 512 | 'n03100240 convertible'
 513 | 'n03109150 corkscrew, bottle screw'
 514 | 'n03110669 cornet, horn, trumpet, trump'
 515 | 'n03124043 cowboy boot'
 516 | 'n03124170 cowboy hat, ten-gallon hat'
 517 | 'n03125729 cradle'
 518 | 'n03126707 crane'
 519 | 'n03127747 crash helmet'
 520 | 'n03127925 crate'
 521 | 'n03131574 crib, cot'
 522 | 'n03133878 Crock Pot'
 523 | 'n03134739 croquet ball'
 524 | 'n03141823 crutch'
 525 | 'n03146219 cuirass'
 526 | 'n03160309 dam, dike, dyke'
 527 | 'n03179701 desk'
 528 | 'n03180011 desktop computer'
 529 | 'n03187595 dial telephone, dial phone'
 530 | 'n03188531 diaper, nappy, napkin'
 531 | 'n03196217 digital clock'
 532 | 'n03197337 digital watch'
 533 | 'n03201208 dining table, board'
 534 | 'n03207743 dishrag, dishcloth'
 535 | 'n03207941 dishwasher, dish washer, dishwashing machine'
 536 | 'n03208938 disk brake, disc brake'
 537 | 'n03216828 dock, dockage, docking facility'
 538 | 'n03218198 dogsled, dog sled, dog sleigh'
 539 | 'n03220513 dome'
 540 | 'n03223299 doormat, welcome mat'
 541 | 'n03240683 drilling platform, offshore rig'
 542 | 'n03249569 drum, membranophone, tympan'
 543 | 'n03250847 drumstick'
 544 | 'n03255030 dumbbell'
 545 | 'n03259280 Dutch oven'
 546 | 'n03271574 electric fan, blower'
 547 | 'n03272010 electric guitar'
 548 | 'n03272562 electric locomotive'
 549 | 'n03290653 entertainment center'
 550 | 'n03291819 envelope'
 551 | 'n03297495 espresso maker'
 552 | 'n03314780 face powder'
 553 | 'n03325584 feather boa, boa'
 554 | 'n03337140 file, file cabinet, filing cabinet'
 555 | 'n03344393 fireboat'
 556 | 'n03345487 fire engine, fire truck'
 557 | 'n03347037 fire screen, fireguard'
 558 | 'n03355925 flagpole, flagstaff'
 559 | 'n03372029 flute, transverse flute'
 560 | 'n03376595 folding chair'
 561 | 'n03379051 football helmet'
 562 | 'n03384352 forklift'
 563 | 'n03388043 fountain'
 564 | 'n03388183 fountain pen'
 565 | 'n03388549 four-poster'
 566 | 'n03393912 freight car'
 567 | 'n03394916 French horn, horn'
 568 | 'n03400231 frying pan, frypan, skillet'
 569 | 'n03404251 fur coat'
 570 | 'n03417042 garbage truck, dustcart'
 571 | 'n03424325 gasmask, respirator, gas helmet'
 572 | 'n03425413 gas pump, gasoline pump, petrol pump, island dispenser'
 573 | 'n03443371 goblet'
 574 | 'n03444034 go-kart'
 575 | 'n03445777 golf ball'
 576 | 'n03445924 golfcart, golf cart'
 577 | 'n03447447 gondola'
 578 | 'n03447721 gong, tam-tam'
 579 | 'n03450230 gown'
 580 | 'n03452741 grand piano, grand'
 581 | 'n03457902 greenhouse, nursery, glasshouse'
 582 | 'n03459775 grille, radiator grille'
 583 | 'n03461385 grocery store, grocery, food market, market'
 584 | 'n03467068 guillotine'
 585 | 'n03476684 hair slide'
 586 | 'n03476991 hair spray'
 587 | 'n03478589 half track'
 588 | 'n03481172 hammer'
 589 | 'n03482405 hamper'
 590 | 'n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier'
 591 | 'n03485407 hand-held computer, hand-held microcomputer'
 592 | 'n03485794 handkerchief, hankie, hanky, hankey'
 593 | 'n03492542 hard disc, hard disk, fixed disk'
 594 | 'n03494278 harmonica, mouth organ, harp, mouth harp'
 595 | 'n03495258 harp'
 596 | 'n03496892 harvester, reaper'
 597 | 'n03498962 hatchet'
 598 | 'n03527444 holster'
 599 | 'n03529860 home theater, home theatre'
 600 | 'n03530642 honeycomb'
 601 | 'n03532672 hook, claw'
 602 | 'n03534580 hoopskirt, crinoline'
 603 | 'n03535780 horizontal bar, high bar'
 604 | 'n03538406 horse cart, horse-cart'
 605 | 'n03544143 hourglass'
 606 | 'n03584254 iPod'
 607 | 'n03584829 iron, smoothing iron'
 608 | 'n03590841 jack-o''-lantern'
 609 | 'n03594734 jean, blue jean, denim'
 610 | 'n03594945 jeep, landrover'
 611 | 'n03595614 jersey, T-shirt, tee shirt'
 612 | 'n03598930 jigsaw puzzle'
 613 | 'n03599486 jinrikisha, ricksha, rickshaw'
 614 | 'n03602883 joystick'
 615 | 'n03617480 kimono'
 616 | 'n03623198 knee pad'
 617 | 'n03627232 knot'
 618 | 'n03630383 lab coat, laboratory coat'
 619 | 'n03633091 ladle'
 620 | 'n03637318 lampshade, lamp shade'
 621 | 'n03642806 laptop, laptop computer'
 622 | 'n03649909 lawn mower, mower'
 623 | 'n03657121 lens cap, lens cover'
 624 | 'n03658185 letter opener, paper knife, paperknife'
 625 | 'n03661043 library'
 626 | 'n03662601 lifeboat'
 627 | 'n03666591 lighter, light, igniter, ignitor'
 628 | 'n03670208 limousine, limo'
 629 | 'n03673027 liner, ocean liner'
 630 | 'n03676483 lipstick, lip rouge'
 631 | 'n03680355 Loafer'
 632 | 'n03690938 lotion'
 633 | 'n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system'
 634 | 'n03692522 loupe, jeweler''s loupe'
 635 | 'n03697007 lumbermill, sawmill'
 636 | 'n03706229 magnetic compass'
 637 | 'n03709823 mailbag, postbag'
 638 | 'n03710193 mailbox, letter box'
 639 | 'n03710637 maillot'
 640 | 'n03710721 maillot, tank suit'
 641 | 'n03717622 manhole cover'
 642 | 'n03720891 maraca'
 643 | 'n03721384 marimba, xylophone'
 644 | 'n03724870 mask'
 645 | 'n03729826 matchstick'
 646 | 'n03733131 maypole'
 647 | 'n03733281 maze, labyrinth'
 648 | 'n03733805 measuring cup'
 649 | 'n03742115 medicine chest, medicine cabinet'
 650 | 'n03743016 megalith, megalithic structure'
 651 | 'n03759954 microphone, mike'
 652 | 'n03761084 microwave, microwave oven'
 653 | 'n03763968 military uniform'
 654 | 'n03764736 milk can'
 655 | 'n03769881 minibus'
 656 | 'n03770439 miniskirt, mini'
 657 | 'n03770679 minivan'
 658 | 'n03773504 missile'
 659 | 'n03775071 mitten'
 660 | 'n03775546 mixing bowl'
 661 | 'n03776460 mobile home, manufactured home'
 662 | 'n03777568 Model T'
 663 | 'n03777754 modem'
 664 | 'n03781244 monastery'
 665 | 'n03782006 monitor'
 666 | 'n03785016 moped'
 667 | 'n03786901 mortar'
 668 | 'n03787032 mortarboard'
 669 | 'n03788195 mosque'
 670 | 'n03788365 mosquito net'
 671 | 'n03791053 motor scooter, scooter'
 672 | 'n03792782 mountain bike, all-terrain bike, off-roader'
 673 | 'n03792972 mountain tent'
 674 | 'n03793489 mouse, computer mouse'
 675 | 'n03794056 mousetrap'
 676 | 'n03796401 moving van'
 677 | 'n03803284 muzzle'
 678 | 'n03804744 nail'
 679 | 'n03814639 neck brace'
 680 | 'n03814906 necklace'
 681 | 'n03825788 nipple'
 682 | 'n03832673 notebook, notebook computer'
 683 | 'n03837869 obelisk'
 684 | 'n03838899 oboe, hautboy, hautbois'
 685 | 'n03840681 ocarina, sweet potato'
 686 | 'n03841143 odometer, hodometer, mileometer, milometer'
 687 | 'n03843555 oil filter'
 688 | 'n03854065 organ, pipe organ'
 689 | 'n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO'
 690 | 'n03866082 overskirt'
 691 | 'n03868242 oxcart'
 692 | 'n03868863 oxygen mask'
 693 | 'n03871628 packet'
 694 | 'n03873416 paddle, boat paddle'
 695 | 'n03874293 paddlewheel, paddle wheel'
 696 | 'n03874599 padlock'
 697 | 'n03876231 paintbrush'
 698 | 'n03877472 pajama, pyjama, pj''s, jammies'
 699 | 'n03877845 palace'
 700 | 'n03884397 panpipe, pandean pipe, syrinx'
 701 | 'n03887697 paper towel'
 702 | 'n03888257 parachute, chute'
 703 | 'n03888605 parallel bars, bars'
 704 | 'n03891251 park bench'
 705 | 'n03891332 parking meter'
 706 | 'n03895866 passenger car, coach, carriage'
 707 | 'n03899768 patio, terrace'
 708 | 'n03902125 pay-phone, pay-station'
 709 | 'n03903868 pedestal, plinth, footstall'
 710 | 'n03908618 pencil box, pencil case'
 711 | 'n03908714 pencil sharpener'
 712 | 'n03916031 perfume, essence'
 713 | 'n03920288 Petri dish'
 714 | 'n03924679 photocopier'
 715 | 'n03929660 pick, plectrum, plectron'
 716 | 'n03929855 pickelhaube'
 717 | 'n03930313 picket fence, paling'
 718 | 'n03930630 pickup, pickup truck'
 719 | 'n03933933 pier'
 720 | 'n03935335 piggy bank, penny bank'
 721 | 'n03937543 pill bottle'
 722 | 'n03938244 pillow'
 723 | 'n03942813 ping-pong ball'
 724 | 'n03944341 pinwheel'
 725 | 'n03947888 pirate, pirate ship'
 726 | 'n03950228 pitcher, ewer'
 727 | 'n03954731 plane, carpenter''s plane, woodworking plane'
 728 | 'n03956157 planetarium'
 729 | 'n03958227 plastic bag'
 730 | 'n03961711 plate rack'
 731 | 'n03967562 plow, plough'
 732 | 'n03970156 plunger, plumber''s helper'
 733 | 'n03976467 Polaroid camera, Polaroid Land camera'
 734 | 'n03976657 pole'
 735 | 'n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria'
 736 | 'n03980874 poncho'
 737 | 'n03982430 pool table, billiard table, snooker table'
 738 | 'n03983396 pop bottle, soda bottle'
 739 | 'n03991062 pot, flowerpot'
 740 | 'n03992509 potter''s wheel'
 741 | 'n03995372 power drill'
 742 | 'n03998194 prayer rug, prayer mat'
 743 | 'n04004767 printer'
 744 | 'n04005630 prison, prison house'
 745 | 'n04008634 projectile, missile'
 746 | 'n04009552 projector'
 747 | 'n04019541 puck, hockey puck'
 748 | 'n04023962 punching bag, punch bag, punching ball, punchball'
 749 | 'n04026417 purse'
 750 | 'n04033901 quill, quill pen'
 751 | 'n04033995 quilt, comforter, comfort, puff'
 752 | 'n04037443 racer, race car, racing car'
 753 | 'n04039381 racket, racquet'
 754 | 'n04040759 radiator'
 755 | 'n04041544 radio, wireless'
 756 | 'n04044716 radio telescope, radio reflector'
 757 | 'n04049303 rain barrel'
 758 | 'n04065272 recreational vehicle, RV, R.V.'
 759 | 'n04067472 reel'
 760 | 'n04069434 reflex camera'
 761 | 'n04070727 refrigerator, icebox'
 762 | 'n04074963 remote control, remote'
 763 | 'n04081281 restaurant, eating house, eating place, eatery'
 764 | 'n04086273 revolver, six-gun, six-shooter'
 765 | 'n04090263 rifle'
 766 | 'n04099969 rocking chair, rocker'
 767 | 'n04111531 rotisserie'
 768 | 'n04116512 rubber eraser, rubber, pencil eraser'
 769 | 'n04118538 rugby ball'
 770 | 'n04118776 rule, ruler'
 771 | 'n04120489 running shoe'
 772 | 'n04125021 safe'
 773 | 'n04127249 safety pin'
 774 | 'n04131690 saltshaker, salt shaker'
 775 | 'n04133789 sandal'
 776 | 'n04136333 sarong'
 777 | 'n04141076 sax, saxophone'
 778 | 'n04141327 scabbard'
 779 | 'n04141975 scale, weighing machine'
 780 | 'n04146614 school bus'
 781 | 'n04147183 schooner'
 782 | 'n04149813 scoreboard'
 783 | 'n04152593 screen, CRT screen'
 784 | 'n04153751 screw'
 785 | 'n04154565 screwdriver'
 786 | 'n04162706 seat belt, seatbelt'
 787 | 'n04179913 sewing machine'
 788 | 'n04192698 shield, buckler'
 789 | 'n04200800 shoe shop, shoe-shop, shoe store'
 790 | 'n04201297 shoji'
 791 | 'n04204238 shopping basket'
 792 | 'n04204347 shopping cart'
 793 | 'n04208210 shovel'
 794 | 'n04209133 shower cap'
 795 | 'n04209239 shower curtain'
 796 | 'n04228054 ski'
 797 | 'n04229816 ski mask'
 798 | 'n04235860 sleeping bag'
 799 | 'n04238763 slide rule, slipstick'
 800 | 'n04239074 sliding door'
 801 | 'n04243546 slot, one-armed bandit'
 802 | 'n04251144 snorkel'
 803 | 'n04252077 snowmobile'
 804 | 'n04252225 snowplow, snowplough'
 805 | 'n04254120 soap dispenser'
 806 | 'n04254680 soccer ball'
 807 | 'n04254777 sock'
 808 | 'n04258138 solar dish, solar collector, solar furnace'
 809 | 'n04259630 sombrero'
 810 | 'n04263257 soup bowl'
 811 | 'n04264628 space bar'
 812 | 'n04265275 space heater'
 813 | 'n04266014 space shuttle'
 814 | 'n04270147 spatula'
 815 | 'n04273569 speedboat'
 816 | 'n04275548 spider web, spider''s web'
 817 | 'n04277352 spindle'
 818 | 'n04285008 sports car, sport car'
 819 | 'n04286575 spotlight, spot'
 820 | 'n04296562 stage'
 821 | 'n04310018 steam locomotive'
 822 | 'n04311004 steel arch bridge'
 823 | 'n04311174 steel drum'
 824 | 'n04317175 stethoscope'
 825 | 'n04325704 stole'
 826 | 'n04326547 stone wall'
 827 | 'n04328186 stopwatch, stop watch'
 828 | 'n04330267 stove'
 829 | 'n04332243 strainer'
 830 | 'n04335435 streetcar, tram, tramcar, trolley, trolley car'
 831 | 'n04336792 stretcher'
 832 | 'n04344873 studio couch, day bed'
 833 | 'n04346328 stupa, tope'
 834 | 'n04347754 submarine, pigboat, sub, U-boat'
 835 | 'n04350905 suit, suit of clothes'
 836 | 'n04355338 sundial'
 837 | 'n04355933 sunglass'
 838 | 'n04356056 sunglasses, dark glasses, shades'
 839 | 'n04357314 sunscreen, sunblock, sun blocker'
 840 | 'n04366367 suspension bridge'
 841 | 'n04367480 swab, swob, mop'
 842 | 'n04370456 sweatshirt'
 843 | 'n04371430 swimming trunks, bathing trunks'
 844 | 'n04371774 swing'
 845 | 'n04372370 switch, electric switch, electrical switch'
 846 | 'n04376876 syringe'
 847 | 'n04380533 table lamp'
 848 | 'n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle'
 849 | 'n04392985 tape player'
 850 | 'n04398044 teapot'
 851 | 'n04399382 teddy, teddy bear'
 852 | 'n04404412 television, television system'
 853 | 'n04409515 tennis ball'
 854 | 'n04417672 thatch, thatched roof'
 855 | 'n04418357 theater curtain, theatre curtain'
 856 | 'n04423845 thimble'
 857 | 'n04428191 thresher, thrasher, threshing machine'
 858 | 'n04429376 throne'
 859 | 'n04435653 tile roof'
 860 | 'n04442312 toaster'
 861 | 'n04443257 tobacco shop, tobacconist shop, tobacconist'
 862 | 'n04447861 toilet seat'
 863 | 'n04456115 torch'
 864 | 'n04458633 totem pole'
 865 | 'n04461696 tow truck, tow car, wrecker'
 866 | 'n04462240 toyshop'
 867 | 'n04465501 tractor'
 868 | 'n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi'
 869 | 'n04476259 tray'
 870 | 'n04479046 trench coat'
 871 | 'n04482393 tricycle, trike, velocipede'
 872 | 'n04483307 trimaran'
 873 | 'n04485082 tripod'
 874 | 'n04486054 triumphal arch'
 875 | 'n04487081 trolleybus, trolley coach, trackless trolley'
 876 | 'n04487394 trombone'
 877 | 'n04493381 tub, vat'
 878 | 'n04501370 turnstile'
 879 | 'n04505470 typewriter keyboard'
 880 | 'n04507155 umbrella'
 881 | 'n04509417 unicycle, monocycle'
 882 | 'n04515003 upright, upright piano'
 883 | 'n04517823 vacuum, vacuum cleaner'
 884 | 'n04522168 vase'
 885 | 'n04523525 vault'
 886 | 'n04525038 velvet'
 887 | 'n04525305 vending machine'
 888 | 'n04532106 vestment'
 889 | 'n04532670 viaduct'
 890 | 'n04536866 violin, fiddle'
 891 | 'n04540053 volleyball'
 892 | 'n04542943 waffle iron'
 893 | 'n04548280 wall clock'
 894 | 'n04548362 wallet, billfold, notecase, pocketbook'
 895 | 'n04550184 wardrobe, closet, press'
 896 | 'n04552348 warplane, military plane'
 897 | 'n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin'
 898 | 'n04554684 washer, automatic washer, washing machine'
 899 | 'n04557648 water bottle'
 900 | 'n04560804 water jug'
 901 | 'n04562935 water tower'
 902 | 'n04579145 whiskey jug'
 903 | 'n04579432 whistle'
 904 | 'n04584207 wig'
 905 | 'n04589890 window screen'
 906 | 'n04590129 window shade'
 907 | 'n04591157 Windsor tie'
 908 | 'n04591713 wine bottle'
 909 | 'n04592741 wing'
 910 | 'n04596742 wok'
 911 | 'n04597913 wooden spoon'
 912 | 'n04599235 wool, woolen, woollen'
 913 | 'n04604644 worm fence, snake fence, snake-rail fence, Virginia fence'
 914 | 'n04606251 wreck'
 915 | 'n04612504 yawl'
 916 | 'n04613696 yurt'
 917 | 'n06359193 web site, website, internet site, site'
 918 | 'n06596364 comic book'
 919 | 'n06785654 crossword puzzle, crossword'
 920 | 'n06794110 street sign'
 921 | 'n06874185 traffic light, traffic signal, stoplight'
 922 | 'n07248320 book jacket, dust cover, dust jacket, dust wrapper'
 923 | 'n07565083 menu'
 924 | 'n07579787 plate'
 925 | 'n07583066 guacamole'
 926 | 'n07584110 consomme'
 927 | 'n07590611 hot pot, hotpot'
 928 | 'n07613480 trifle'
 929 | 'n07614500 ice cream, icecream'
 930 | 'n07615774 ice lolly, lolly, lollipop, popsicle'
 931 | 'n07684084 French loaf'
 932 | 'n07693725 bagel, beigel'
 933 | 'n07695742 pretzel'
 934 | 'n07697313 cheeseburger'
 935 | 'n07697537 hotdog, hot dog, red hot'
 936 | 'n07711569 mashed potato'
 937 | 'n07714571 head cabbage'
 938 | 'n07714990 broccoli'
 939 | 'n07715103 cauliflower'
 940 | 'n07716358 zucchini, courgette'
 941 | 'n07716906 spaghetti squash'
 942 | 'n07717410 acorn squash'
 943 | 'n07717556 butternut squash'
 944 | 'n07718472 cucumber, cuke'
 945 | 'n07718747 artichoke, globe artichoke'
 946 | 'n07720875 bell pepper'
 947 | 'n07730033 cardoon'
 948 | 'n07734744 mushroom'
 949 | 'n07742313 Granny Smith'
 950 | 'n07745940 strawberry'
 951 | 'n07747607 orange'
 952 | 'n07749582 lemon'
 953 | 'n07753113 fig'
 954 | 'n07753275 pineapple, ananas'
 955 | 'n07753592 banana'
 956 | 'n07754684 jackfruit, jak, jack'
 957 | 'n07760859 custard apple'
 958 | 'n07768694 pomegranate'
 959 | 'n07802026 hay'
 960 | 'n07831146 carbonara'
 961 | 'n07836838 chocolate sauce, chocolate syrup'
 962 | 'n07860988 dough'
 963 | 'n07871810 meat loaf, meatloaf'
 964 | 'n07873807 pizza, pizza pie'
 965 | 'n07875152 potpie'
 966 | 'n07880968 burrito'
 967 | 'n07892512 red wine'
 968 | 'n07920052 espresso'
 969 | 'n07930864 cup'
 970 | 'n07932039 eggnog'
 971 | 'n09193705 alp'
 972 | 'n09229709 bubble'
 973 | 'n09246464 cliff, drop, drop-off'
 974 | 'n09256479 coral reef'
 975 | 'n09288635 geyser'
 976 | 'n09332890 lakeside, lakeshore'
 977 | 'n09399592 promontory, headland, head, foreland'
 978 | 'n09421951 sandbar, sand bar'
 979 | 'n09428293 seashore, coast, seacoast, sea-coast'
 980 | 'n09468604 valley, vale'
 981 | 'n09472597 volcano'
 982 | 'n09835506 ballplayer, baseball player'
 983 | 'n10148035 groom, bridegroom'
 984 | 'n10565667 scuba diver'
 985 | 'n11879895 rapeseed'
 986 | 'n11939491 daisy'
 987 | 'n12057211 yellow lady''s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum'
 988 | 'n12144580 corn'
 989 | 'n12267677 acorn'
 990 | 'n12620546 hip, rose hip, rosehip'
 991 | 'n12768682 buckeye, horse chestnut, conker'
 992 | 'n12985857 coral fungus'
 993 | 'n12998815 agaric'
 994 | 'n13037406 gyromitra'
 995 | 'n13040303 stinkhorn, carrion fungus'
 996 | 'n13044778 earthstar'
 997 | 'n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa'
 998 | 'n13054560 bolete'
 999 | 'n13133613 ear, spike, capitulum'
1000 | 'n15075141 toilet tissue, toilet paper, bathroom tissue'


--------------------------------------------------------------------------------