source: internals/2016/aptoideimagesdetector/trunk/explicit_content_detector/API/Explicit_detector/Illustration2Vector/illustration2vec_master/illust2vec_tag.prototxt @ 16448

Last change on this file since 16448 was 16448, checked in by dferreira, 3 years ago

File organization totally changed

File size: 4.0 KB
Line 
1name: "CaffeNet"
2input: "data"
3input_dim: 50
4input_dim: 3
5input_dim: 224
6input_dim: 224
7layer {
8  name: "conv1_1"
9  type: "Convolution"
10  bottom: "data"
11  top: "conv1_1"
12  convolution_param {
13    num_output: 64
14    kernel_size: 3
15    stride: 1
16    pad: 1
17  }
18}
19layer {
20  name: "relu1_1"
21  type: "ReLU"
22  bottom: "conv1_1"
23  top: "conv1_1"
24}
25layer {
26  name: "pool1"
27  type: "Pooling"
28  bottom: "conv1_1"
29  top: "pool1"
30  pooling_param {
31    pool: MAX
32    kernel_size: 2
33    stride: 2
34  }
35}
36layer {
37  name: "conv2_1"
38  type: "Convolution"
39  bottom: "pool1"
40  top: "conv2_1"
41  convolution_param {
42    num_output: 128
43    kernel_size: 3
44    stride: 1
45    pad: 1
46  }
47}
48layer {
49  name: "relu2_1"
50  type: "ReLU"
51  bottom: "conv2_1"
52  top: "conv2_1"
53}
54layer {
55  name: "pool2"
56  type: "Pooling"
57  bottom: "conv2_1"
58  top: "pool2"
59  pooling_param {
60    pool: MAX
61    kernel_size: 2
62    stride: 2
63  }
64}
65layer {
66  name: "conv3_1"
67  type: "Convolution"
68  bottom: "pool2"
69  top: "conv3_1"
70  convolution_param {
71    num_output: 256
72    kernel_size: 3
73    stride: 1
74    pad: 1
75  }
76}
77layer {
78  name: "relu3_1"
79  type: "ReLU"
80  bottom: "conv3_1"
81  top: "conv3_1"
82}
83layer {
84  name: "conv3_2"
85  type: "Convolution"
86  bottom: "conv3_1"
87  top: "conv3_2"
88  convolution_param {
89    num_output: 256
90    kernel_size: 3
91    stride: 1
92    pad: 1
93  }
94}
95layer {
96  name: "relu3_2"
97  type: "ReLU"
98  bottom: "conv3_2"
99  top: "conv3_2"
100}
101layer {
102  name: "pool3"
103  type: "Pooling"
104  bottom: "conv3_2"
105  top: "pool3"
106  pooling_param {
107    pool: MAX
108    kernel_size: 2
109    stride: 2
110  }
111}
112layer {
113  name: "conv4_1"
114  type: "Convolution"
115  bottom: "pool3"
116  top: "conv4_1"
117  convolution_param {
118    num_output: 512
119    kernel_size: 3
120    stride: 1
121    pad: 1
122  }
123}
124layer {
125  name: "relu4_1"
126  type: "ReLU"
127  bottom: "conv4_1"
128  top: "conv4_1"
129}
130layer {
131  name: "conv4_2"
132  type: "Convolution"
133  bottom: "conv4_1"
134  top: "conv4_2"
135  convolution_param {
136    num_output: 512
137    kernel_size: 3
138    stride: 1
139    pad: 1
140  }
141}
142layer {
143  name: "relu4_2"
144  type: "ReLU"
145  bottom: "conv4_2"
146  top: "conv4_2"
147}
148layer {
149  name: "pool4"
150  type: "Pooling"
151  bottom: "conv4_2"
152  top: "pool4"
153  pooling_param {
154    pool: MAX
155    kernel_size: 2
156    stride: 2
157  }
158}
159layer {
160  name: "conv5_1"
161  type: "Convolution"
162  bottom: "pool4"
163  top: "conv5_1"
164  convolution_param {
165    num_output: 512
166    kernel_size: 3
167    stride: 1
168    pad: 1
169  }
170}
171layer {
172  name: "relu5_1"
173  type: "ReLU"
174  bottom: "conv5_1"
175  top: "conv5_1"
176}
177layer {
178  name: "conv5_2"
179  type: "Convolution"
180  bottom: "conv5_1"
181  top: "conv5_2"
182  convolution_param {
183    num_output: 512
184    kernel_size: 3
185    stride: 1
186    pad: 1
187  }
188}
189layer {
190  name: "relu5_2"
191  type: "ReLU"
192  bottom: "conv5_2"
193  top: "conv5_2"
194}
195layer {
196  name: "pool5"
197  type: "Pooling"
198  bottom: "conv5_2"
199  top: "pool5"
200  pooling_param {
201    pool: MAX
202    kernel_size: 2
203    stride: 2
204  }
205}
206layer {
207  name: "conv6_1"
208  type: "Convolution"
209  bottom: "pool5"
210  top: "conv6_1"
211  convolution_param {
212    num_output: 1024
213    kernel_size: 3
214    stride: 1
215    pad: 1
216  }
217}
218layer {
219  name: "relu6_1"
220  type: "ReLU"
221  bottom: "conv6_1"
222  top: "conv6_1"
223}
224layer {
225  name: "conv6_2"
226  type: "Convolution"
227  bottom: "conv6_1"
228  top: "conv6_2"
229  convolution_param {
230    num_output: 1024
231    kernel_size: 3
232    stride: 1
233    pad: 1
234  }
235}
236layer {
237  name: "relu6_2"
238  type: "ReLU"
239  bottom: "conv6_2"
240  top: "conv6_2"
241}
242layer {
243  name: "conv6_3"
244  type: "Convolution"
245  bottom: "conv6_2"
246  top: "conv6_3"
247  convolution_param {
248    num_output: 1024
249    kernel_size: 3
250    stride: 1
251    pad: 1
252  }
253}
254layer {
255  name: "relu6_3"
256  type: "ReLU"
257  bottom: "conv6_3"
258  top: "conv6_3"
259}
260layer {
261  name: "conv6_4"
262  type: "Convolution"
263  bottom: "conv6_3"
264  top: "conv6_4"
265  convolution_param {
266    num_output: 1539
267    kernel_size: 3
268    stride: 1
269    pad: 1
270  }
271}
272layer {
273  name: "pool6"
274  type: "Pooling"
275  bottom: "conv6_4"
276  top: "pool6"
277  pooling_param {
278    pool: AVE
279    kernel_size: 7
280    stride: 1
281  }
282}
283layer {
284  name: "prob"
285  type: "Sigmoid"
286  bottom: "pool6"
287  top: "prob"
288}
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