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Upload 8 files (#1)
Browse files- Upload 8 files (5cd9e210da38c87e28d551422b899a1f9c9adce8)
Co-authored-by: Dohee Kang <asrs777@users.noreply.huggingface.co>
- .gitattributes +1 -0
- app.py +68 -5
- examples/9_peoples.jpg +0 -0
- examples/angry.jpg +0 -0
- examples/sad.jpg +0 -0
- examples/sad.webp +0 -0
- examples/surprise.jpg +0 -0
- face_detector/deploy.prototxt +1789 -0
- face_detector/res10_300x300_ssd_iter_140000.caffemodel +3 -0
.gitattributes
CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
face_detector/res10_300x300_ssd_iter_140000.caffemodel filter=lfs diff=lfs merge=lfs -text
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app.py
CHANGED
@@ -4,13 +4,60 @@ import torch
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from transformers import AutoModelForImageClassification
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import shutil
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from optimum.pipelines import pipeline
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-
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device = 1 if torch.cuda.is_available() else "cpu"
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chk_point = "kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176"
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model = AutoModelForImageClassification.from_pretrained(chk_point)
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emotion_dict = {
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'neutral': '0',
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'happy': '1',
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@@ -69,14 +116,30 @@ except NotImplementedError:
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# )
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# demo.launch(enable_queue=True)
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def predict(image):
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gr.Interface(
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predict,
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inputs=gr.inputs.Image(label="Upload image"
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outputs=gr.outputs.Label(num_top_classes=7),
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title="FER trained on DiffusionFER dataset",
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).launch()
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from transformers import AutoModelForImageClassification
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import shutil
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from optimum.pipelines import pipeline
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import os, cv2, numpy as np
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from PIL import Image
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import itertools
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device = 1 if torch.cuda.is_available() else "cpu"
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chk_point = "kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176"
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model = AutoModelForImageClassification.from_pretrained(chk_point)
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##Add face detector
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protoPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
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modelPath = os.path.sep.join(["face_detector",
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"res10_300x300_ssd_iter_140000.caffemodel"])
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net = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
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def draw_results_ssd(detected, input_img, ad):
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img_w, img_h, _ = np.shape(input_img)
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# loop over the detections
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cropped_output = []
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if detected.shape[2] > 0:
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print("detected face: {}".format(detected.shape[2]))
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for i in range(0, detected.shape[2]):
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# extract the confidence (i.e., probability) associated with the
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# prediction
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confidence = detected[0, 0, i, 2]
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# filter out weak detections
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if confidence > 0.5:
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# compute the (x, y)-coordinates of the bounding box for
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# the face and extract the face ROI
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(h0, w0) = input_img.shape[:2]
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box = detected[0, 0, i, 3:7] * np.array([w0, h0, w0, h0])
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(startX, startY, endX, endY) = box.astype("int")
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# print((startX, startY, endX, endY))
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x1 = startX
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y1 = startY
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w = endX - startX
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h = endY - startY
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x2 = x1 + w
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y2 = y1 + h
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xw1 = max(int(x1 - ad * w), 0)
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yw1 = max(int(y1 - ad * h), 0)
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xw2 = min(int(x2 + ad * w), img_w - 1)
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yw2 = min(int(y2 + ad * h), img_h - 1)
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face_crop = input_img[yw1:yw2, xw1:xw2]
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if face_crop.size != 0:
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cropped_output.append(face_crop)
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return cropped_output
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emotion_dict = {
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'neutral': '0',
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'happy': '1',
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# )
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# demo.launch(enable_queue=True)
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def face_detector(input_img):
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img_h, img_w, _ = np.shape(input_img)
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blob = cv2.dnn.blobFromImage(cv2.resize(input_img, (300, 300)), 1.0,
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(300, 300), (104.0, 177.0, 123.0))
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net.setInput(blob)
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detected = net.forward() # For example, (1, 1, 200, 7)
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cropped_list = draw_results_ssd(detected, input_img, 0.1 ) # 128
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return cropped_list
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def predict(image):
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print(type(image))
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cropped_list = face_detector(image)
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print(cropped_list)
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output = [None for _ in range(20)]
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for i in range(len(cropped_list)):
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predictions = pipe(Image.fromarray(cropped_list[i]))
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output[int(i*2)] = cropped_list[i]
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output[int(i*2+1)] = {p["label"]: p["score"] for p in predictions}
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return output
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gr.Interface(
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predict,
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inputs=gr.inputs.Image(label="Upload image"),
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outputs=list(itertools.chain(*[[gr.outputs.Image(label="image {}".format(ind)), gr.outputs.Label(num_top_classes=7, label="Result {}".format(ind))] for ind in range(10)])),
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examples=[["examples/9_peoples.jpg"], ["examples/sad.jpg"], ["examples/angry.jpg"], ["examples/surprise.jpg"]],
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title="FER trained on DiffusionFER dataset",
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).launch()
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examples/9_peoples.jpg
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examples/angry.jpg
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examples/sad.jpg
ADDED
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examples/sad.webp
ADDED
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examples/surprise.jpg
ADDED
![]() |
face_detector/deploy.prototxt
ADDED
@@ -0,0 +1,1789 @@
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|
1 |
+
input: "data"
|
2 |
+
input_shape {
|
3 |
+
dim: 1
|
4 |
+
dim: 3
|
5 |
+
dim: 300
|
6 |
+
dim: 300
|
7 |
+
}
|
8 |
+
|
9 |
+
layer {
|
10 |
+
name: "data_bn"
|
11 |
+
type: "BatchNorm"
|
12 |
+
bottom: "data"
|
13 |
+
top: "data_bn"
|
14 |
+
param {
|
15 |
+
lr_mult: 0.0
|
16 |
+
}
|
17 |
+
param {
|
18 |
+
lr_mult: 0.0
|
19 |
+
}
|
20 |
+
param {
|
21 |
+
lr_mult: 0.0
|
22 |
+
}
|
23 |
+
}
|
24 |
+
layer {
|
25 |
+
name: "data_scale"
|
26 |
+
type: "Scale"
|
27 |
+
bottom: "data_bn"
|
28 |
+
top: "data_bn"
|
29 |
+
param {
|
30 |
+
lr_mult: 1.0
|
31 |
+
decay_mult: 1.0
|
32 |
+
}
|
33 |
+
param {
|
34 |
+
lr_mult: 2.0
|
35 |
+
decay_mult: 1.0
|
36 |
+
}
|
37 |
+
scale_param {
|
38 |
+
bias_term: true
|
39 |
+
}
|
40 |
+
}
|
41 |
+
layer {
|
42 |
+
name: "conv1_h"
|
43 |
+
type: "Convolution"
|
44 |
+
bottom: "data_bn"
|
45 |
+
top: "conv1_h"
|
46 |
+
param {
|
47 |
+
lr_mult: 1.0
|
48 |
+
decay_mult: 1.0
|
49 |
+
}
|
50 |
+
param {
|
51 |
+
lr_mult: 2.0
|
52 |
+
decay_mult: 1.0
|
53 |
+
}
|
54 |
+
convolution_param {
|
55 |
+
num_output: 32
|
56 |
+
pad: 3
|
57 |
+
kernel_size: 7
|
58 |
+
stride: 2
|
59 |
+
weight_filler {
|
60 |
+
type: "msra"
|
61 |
+
variance_norm: FAN_OUT
|
62 |
+
}
|
63 |
+
bias_filler {
|
64 |
+
type: "constant"
|
65 |
+
value: 0.0
|
66 |
+
}
|
67 |
+
}
|
68 |
+
}
|
69 |
+
layer {
|
70 |
+
name: "conv1_bn_h"
|
71 |
+
type: "BatchNorm"
|
72 |
+
bottom: "conv1_h"
|
73 |
+
top: "conv1_h"
|
74 |
+
param {
|
75 |
+
lr_mult: 0.0
|
76 |
+
}
|
77 |
+
param {
|
78 |
+
lr_mult: 0.0
|
79 |
+
}
|
80 |
+
param {
|
81 |
+
lr_mult: 0.0
|
82 |
+
}
|
83 |
+
}
|
84 |
+
layer {
|
85 |
+
name: "conv1_scale_h"
|
86 |
+
type: "Scale"
|
87 |
+
bottom: "conv1_h"
|
88 |
+
top: "conv1_h"
|
89 |
+
param {
|
90 |
+
lr_mult: 1.0
|
91 |
+
decay_mult: 1.0
|
92 |
+
}
|
93 |
+
param {
|
94 |
+
lr_mult: 2.0
|
95 |
+
decay_mult: 1.0
|
96 |
+
}
|
97 |
+
scale_param {
|
98 |
+
bias_term: true
|
99 |
+
}
|
100 |
+
}
|
101 |
+
layer {
|
102 |
+
name: "conv1_relu"
|
103 |
+
type: "ReLU"
|
104 |
+
bottom: "conv1_h"
|
105 |
+
top: "conv1_h"
|
106 |
+
}
|
107 |
+
layer {
|
108 |
+
name: "conv1_pool"
|
109 |
+
type: "Pooling"
|
110 |
+
bottom: "conv1_h"
|
111 |
+
top: "conv1_pool"
|
112 |
+
pooling_param {
|
113 |
+
kernel_size: 3
|
114 |
+
stride: 2
|
115 |
+
}
|
116 |
+
}
|
117 |
+
layer {
|
118 |
+
name: "layer_64_1_conv1_h"
|
119 |
+
type: "Convolution"
|
120 |
+
bottom: "conv1_pool"
|
121 |
+
top: "layer_64_1_conv1_h"
|
122 |
+
param {
|
123 |
+
lr_mult: 1.0
|
124 |
+
decay_mult: 1.0
|
125 |
+
}
|
126 |
+
convolution_param {
|
127 |
+
num_output: 32
|
128 |
+
bias_term: false
|
129 |
+
pad: 1
|
130 |
+
kernel_size: 3
|
131 |
+
stride: 1
|
132 |
+
weight_filler {
|
133 |
+
type: "msra"
|
134 |
+
}
|
135 |
+
bias_filler {
|
136 |
+
type: "constant"
|
137 |
+
value: 0.0
|
138 |
+
}
|
139 |
+
}
|
140 |
+
}
|
141 |
+
layer {
|
142 |
+
name: "layer_64_1_bn2_h"
|
143 |
+
type: "BatchNorm"
|
144 |
+
bottom: "layer_64_1_conv1_h"
|
145 |
+
top: "layer_64_1_conv1_h"
|
146 |
+
param {
|
147 |
+
lr_mult: 0.0
|
148 |
+
}
|
149 |
+
param {
|
150 |
+
lr_mult: 0.0
|
151 |
+
}
|
152 |
+
param {
|
153 |
+
lr_mult: 0.0
|
154 |
+
}
|
155 |
+
}
|
156 |
+
layer {
|
157 |
+
name: "layer_64_1_scale2_h"
|
158 |
+
type: "Scale"
|
159 |
+
bottom: "layer_64_1_conv1_h"
|
160 |
+
top: "layer_64_1_conv1_h"
|
161 |
+
param {
|
162 |
+
lr_mult: 1.0
|
163 |
+
decay_mult: 1.0
|
164 |
+
}
|
165 |
+
param {
|
166 |
+
lr_mult: 2.0
|
167 |
+
decay_mult: 1.0
|
168 |
+
}
|
169 |
+
scale_param {
|
170 |
+
bias_term: true
|
171 |
+
}
|
172 |
+
}
|
173 |
+
layer {
|
174 |
+
name: "layer_64_1_relu2"
|
175 |
+
type: "ReLU"
|
176 |
+
bottom: "layer_64_1_conv1_h"
|
177 |
+
top: "layer_64_1_conv1_h"
|
178 |
+
}
|
179 |
+
layer {
|
180 |
+
name: "layer_64_1_conv2_h"
|
181 |
+
type: "Convolution"
|
182 |
+
bottom: "layer_64_1_conv1_h"
|
183 |
+
top: "layer_64_1_conv2_h"
|
184 |
+
param {
|
185 |
+
lr_mult: 1.0
|
186 |
+
decay_mult: 1.0
|
187 |
+
}
|
188 |
+
convolution_param {
|
189 |
+
num_output: 32
|
190 |
+
bias_term: false
|
191 |
+
pad: 1
|
192 |
+
kernel_size: 3
|
193 |
+
stride: 1
|
194 |
+
weight_filler {
|
195 |
+
type: "msra"
|
196 |
+
}
|
197 |
+
bias_filler {
|
198 |
+
type: "constant"
|
199 |
+
value: 0.0
|
200 |
+
}
|
201 |
+
}
|
202 |
+
}
|
203 |
+
layer {
|
204 |
+
name: "layer_64_1_sum"
|
205 |
+
type: "Eltwise"
|
206 |
+
bottom: "layer_64_1_conv2_h"
|
207 |
+
bottom: "conv1_pool"
|
208 |
+
top: "layer_64_1_sum"
|
209 |
+
}
|
210 |
+
layer {
|
211 |
+
name: "layer_128_1_bn1_h"
|
212 |
+
type: "BatchNorm"
|
213 |
+
bottom: "layer_64_1_sum"
|
214 |
+
top: "layer_128_1_bn1_h"
|
215 |
+
param {
|
216 |
+
lr_mult: 0.0
|
217 |
+
}
|
218 |
+
param {
|
219 |
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lr_mult: 0.0
|
220 |
+
}
|
221 |
+
param {
|
222 |
+
lr_mult: 0.0
|
223 |
+
}
|
224 |
+
}
|
225 |
+
layer {
|
226 |
+
name: "layer_128_1_scale1_h"
|
227 |
+
type: "Scale"
|
228 |
+
bottom: "layer_128_1_bn1_h"
|
229 |
+
top: "layer_128_1_bn1_h"
|
230 |
+
param {
|
231 |
+
lr_mult: 1.0
|
232 |
+
decay_mult: 1.0
|
233 |
+
}
|
234 |
+
param {
|
235 |
+
lr_mult: 2.0
|
236 |
+
decay_mult: 1.0
|
237 |
+
}
|
238 |
+
scale_param {
|
239 |
+
bias_term: true
|
240 |
+
}
|
241 |
+
}
|
242 |
+
layer {
|
243 |
+
name: "layer_128_1_relu1"
|
244 |
+
type: "ReLU"
|
245 |
+
bottom: "layer_128_1_bn1_h"
|
246 |
+
top: "layer_128_1_bn1_h"
|
247 |
+
}
|
248 |
+
layer {
|
249 |
+
name: "layer_128_1_conv1_h"
|
250 |
+
type: "Convolution"
|
251 |
+
bottom: "layer_128_1_bn1_h"
|
252 |
+
top: "layer_128_1_conv1_h"
|
253 |
+
param {
|
254 |
+
lr_mult: 1.0
|
255 |
+
decay_mult: 1.0
|
256 |
+
}
|
257 |
+
convolution_param {
|
258 |
+
num_output: 128
|
259 |
+
bias_term: false
|
260 |
+
pad: 1
|
261 |
+
kernel_size: 3
|
262 |
+
stride: 2
|
263 |
+
weight_filler {
|
264 |
+
type: "msra"
|
265 |
+
}
|
266 |
+
bias_filler {
|
267 |
+
type: "constant"
|
268 |
+
value: 0.0
|
269 |
+
}
|
270 |
+
}
|
271 |
+
}
|
272 |
+
layer {
|
273 |
+
name: "layer_128_1_bn2"
|
274 |
+
type: "BatchNorm"
|
275 |
+
bottom: "layer_128_1_conv1_h"
|
276 |
+
top: "layer_128_1_conv1_h"
|
277 |
+
param {
|
278 |
+
lr_mult: 0.0
|
279 |
+
}
|
280 |
+
param {
|
281 |
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lr_mult: 0.0
|
282 |
+
}
|
283 |
+
param {
|
284 |
+
lr_mult: 0.0
|
285 |
+
}
|
286 |
+
}
|
287 |
+
layer {
|
288 |
+
name: "layer_128_1_scale2"
|
289 |
+
type: "Scale"
|
290 |
+
bottom: "layer_128_1_conv1_h"
|
291 |
+
top: "layer_128_1_conv1_h"
|
292 |
+
param {
|
293 |
+
lr_mult: 1.0
|
294 |
+
decay_mult: 1.0
|
295 |
+
}
|
296 |
+
param {
|
297 |
+
lr_mult: 2.0
|
298 |
+
decay_mult: 1.0
|
299 |
+
}
|
300 |
+
scale_param {
|
301 |
+
bias_term: true
|
302 |
+
}
|
303 |
+
}
|
304 |
+
layer {
|
305 |
+
name: "layer_128_1_relu2"
|
306 |
+
type: "ReLU"
|
307 |
+
bottom: "layer_128_1_conv1_h"
|
308 |
+
top: "layer_128_1_conv1_h"
|
309 |
+
}
|
310 |
+
layer {
|
311 |
+
name: "layer_128_1_conv2"
|
312 |
+
type: "Convolution"
|
313 |
+
bottom: "layer_128_1_conv1_h"
|
314 |
+
top: "layer_128_1_conv2"
|
315 |
+
param {
|
316 |
+
lr_mult: 1.0
|
317 |
+
decay_mult: 1.0
|
318 |
+
}
|
319 |
+
convolution_param {
|
320 |
+
num_output: 128
|
321 |
+
bias_term: false
|
322 |
+
pad: 1
|
323 |
+
kernel_size: 3
|
324 |
+
stride: 1
|
325 |
+
weight_filler {
|
326 |
+
type: "msra"
|
327 |
+
}
|
328 |
+
bias_filler {
|
329 |
+
type: "constant"
|
330 |
+
value: 0.0
|
331 |
+
}
|
332 |
+
}
|
333 |
+
}
|
334 |
+
layer {
|
335 |
+
name: "layer_128_1_conv_expand_h"
|
336 |
+
type: "Convolution"
|
337 |
+
bottom: "layer_128_1_bn1_h"
|
338 |
+
top: "layer_128_1_conv_expand_h"
|
339 |
+
param {
|
340 |
+
lr_mult: 1.0
|
341 |
+
decay_mult: 1.0
|
342 |
+
}
|
343 |
+
convolution_param {
|
344 |
+
num_output: 128
|
345 |
+
bias_term: false
|
346 |
+
pad: 0
|
347 |
+
kernel_size: 1
|
348 |
+
stride: 2
|
349 |
+
weight_filler {
|
350 |
+
type: "msra"
|
351 |
+
}
|
352 |
+
bias_filler {
|
353 |
+
type: "constant"
|
354 |
+
value: 0.0
|
355 |
+
}
|
356 |
+
}
|
357 |
+
}
|
358 |
+
layer {
|
359 |
+
name: "layer_128_1_sum"
|
360 |
+
type: "Eltwise"
|
361 |
+
bottom: "layer_128_1_conv2"
|
362 |
+
bottom: "layer_128_1_conv_expand_h"
|
363 |
+
top: "layer_128_1_sum"
|
364 |
+
}
|
365 |
+
layer {
|
366 |
+
name: "layer_256_1_bn1"
|
367 |
+
type: "BatchNorm"
|
368 |
+
bottom: "layer_128_1_sum"
|
369 |
+
top: "layer_256_1_bn1"
|
370 |
+
param {
|
371 |
+
lr_mult: 0.0
|
372 |
+
}
|
373 |
+
param {
|
374 |
+
lr_mult: 0.0
|
375 |
+
}
|
376 |
+
param {
|
377 |
+
lr_mult: 0.0
|
378 |
+
}
|
379 |
+
}
|
380 |
+
layer {
|
381 |
+
name: "layer_256_1_scale1"
|
382 |
+
type: "Scale"
|
383 |
+
bottom: "layer_256_1_bn1"
|
384 |
+
top: "layer_256_1_bn1"
|
385 |
+
param {
|
386 |
+
lr_mult: 1.0
|
387 |
+
decay_mult: 1.0
|
388 |
+
}
|
389 |
+
param {
|
390 |
+
lr_mult: 2.0
|
391 |
+
decay_mult: 1.0
|
392 |
+
}
|
393 |
+
scale_param {
|
394 |
+
bias_term: true
|
395 |
+
}
|
396 |
+
}
|
397 |
+
layer {
|
398 |
+
name: "layer_256_1_relu1"
|
399 |
+
type: "ReLU"
|
400 |
+
bottom: "layer_256_1_bn1"
|
401 |
+
top: "layer_256_1_bn1"
|
402 |
+
}
|
403 |
+
layer {
|
404 |
+
name: "layer_256_1_conv1"
|
405 |
+
type: "Convolution"
|
406 |
+
bottom: "layer_256_1_bn1"
|
407 |
+
top: "layer_256_1_conv1"
|
408 |
+
param {
|
409 |
+
lr_mult: 1.0
|
410 |
+
decay_mult: 1.0
|
411 |
+
}
|
412 |
+
convolution_param {
|
413 |
+
num_output: 256
|
414 |
+
bias_term: false
|
415 |
+
pad: 1
|
416 |
+
kernel_size: 3
|
417 |
+
stride: 2
|
418 |
+
weight_filler {
|
419 |
+
type: "msra"
|
420 |
+
}
|
421 |
+
bias_filler {
|
422 |
+
type: "constant"
|
423 |
+
value: 0.0
|
424 |
+
}
|
425 |
+
}
|
426 |
+
}
|
427 |
+
layer {
|
428 |
+
name: "layer_256_1_bn2"
|
429 |
+
type: "BatchNorm"
|
430 |
+
bottom: "layer_256_1_conv1"
|
431 |
+
top: "layer_256_1_conv1"
|
432 |
+
param {
|
433 |
+
lr_mult: 0.0
|
434 |
+
}
|
435 |
+
param {
|
436 |
+
lr_mult: 0.0
|
437 |
+
}
|
438 |
+
param {
|
439 |
+
lr_mult: 0.0
|
440 |
+
}
|
441 |
+
}
|
442 |
+
layer {
|
443 |
+
name: "layer_256_1_scale2"
|
444 |
+
type: "Scale"
|
445 |
+
bottom: "layer_256_1_conv1"
|
446 |
+
top: "layer_256_1_conv1"
|
447 |
+
param {
|
448 |
+
lr_mult: 1.0
|
449 |
+
decay_mult: 1.0
|
450 |
+
}
|
451 |
+
param {
|
452 |
+
lr_mult: 2.0
|
453 |
+
decay_mult: 1.0
|
454 |
+
}
|
455 |
+
scale_param {
|
456 |
+
bias_term: true
|
457 |
+
}
|
458 |
+
}
|
459 |
+
layer {
|
460 |
+
name: "layer_256_1_relu2"
|
461 |
+
type: "ReLU"
|
462 |
+
bottom: "layer_256_1_conv1"
|
463 |
+
top: "layer_256_1_conv1"
|
464 |
+
}
|
465 |
+
layer {
|
466 |
+
name: "layer_256_1_conv2"
|
467 |
+
type: "Convolution"
|
468 |
+
bottom: "layer_256_1_conv1"
|
469 |
+
top: "layer_256_1_conv2"
|
470 |
+
param {
|
471 |
+
lr_mult: 1.0
|
472 |
+
decay_mult: 1.0
|
473 |
+
}
|
474 |
+
convolution_param {
|
475 |
+
num_output: 256
|
476 |
+
bias_term: false
|
477 |
+
pad: 1
|
478 |
+
kernel_size: 3
|
479 |
+
stride: 1
|
480 |
+
weight_filler {
|
481 |
+
type: "msra"
|
482 |
+
}
|
483 |
+
bias_filler {
|
484 |
+
type: "constant"
|
485 |
+
value: 0.0
|
486 |
+
}
|
487 |
+
}
|
488 |
+
}
|
489 |
+
layer {
|
490 |
+
name: "layer_256_1_conv_expand"
|
491 |
+
type: "Convolution"
|
492 |
+
bottom: "layer_256_1_bn1"
|
493 |
+
top: "layer_256_1_conv_expand"
|
494 |
+
param {
|
495 |
+
lr_mult: 1.0
|
496 |
+
decay_mult: 1.0
|
497 |
+
}
|
498 |
+
convolution_param {
|
499 |
+
num_output: 256
|
500 |
+
bias_term: false
|
501 |
+
pad: 0
|
502 |
+
kernel_size: 1
|
503 |
+
stride: 2
|
504 |
+
weight_filler {
|
505 |
+
type: "msra"
|
506 |
+
}
|
507 |
+
bias_filler {
|
508 |
+
type: "constant"
|
509 |
+
value: 0.0
|
510 |
+
}
|
511 |
+
}
|
512 |
+
}
|
513 |
+
layer {
|
514 |
+
name: "layer_256_1_sum"
|
515 |
+
type: "Eltwise"
|
516 |
+
bottom: "layer_256_1_conv2"
|
517 |
+
bottom: "layer_256_1_conv_expand"
|
518 |
+
top: "layer_256_1_sum"
|
519 |
+
}
|
520 |
+
layer {
|
521 |
+
name: "layer_512_1_bn1"
|
522 |
+
type: "BatchNorm"
|
523 |
+
bottom: "layer_256_1_sum"
|
524 |
+
top: "layer_512_1_bn1"
|
525 |
+
param {
|
526 |
+
lr_mult: 0.0
|
527 |
+
}
|
528 |
+
param {
|
529 |
+
lr_mult: 0.0
|
530 |
+
}
|
531 |
+
param {
|
532 |
+
lr_mult: 0.0
|
533 |
+
}
|
534 |
+
}
|
535 |
+
layer {
|
536 |
+
name: "layer_512_1_scale1"
|
537 |
+
type: "Scale"
|
538 |
+
bottom: "layer_512_1_bn1"
|
539 |
+
top: "layer_512_1_bn1"
|
540 |
+
param {
|
541 |
+
lr_mult: 1.0
|
542 |
+
decay_mult: 1.0
|
543 |
+
}
|
544 |
+
param {
|
545 |
+
lr_mult: 2.0
|
546 |
+
decay_mult: 1.0
|
547 |
+
}
|
548 |
+
scale_param {
|
549 |
+
bias_term: true
|
550 |
+
}
|
551 |
+
}
|
552 |
+
layer {
|
553 |
+
name: "layer_512_1_relu1"
|
554 |
+
type: "ReLU"
|
555 |
+
bottom: "layer_512_1_bn1"
|
556 |
+
top: "layer_512_1_bn1"
|
557 |
+
}
|
558 |
+
layer {
|
559 |
+
name: "layer_512_1_conv1_h"
|
560 |
+
type: "Convolution"
|
561 |
+
bottom: "layer_512_1_bn1"
|
562 |
+
top: "layer_512_1_conv1_h"
|
563 |
+
param {
|
564 |
+
lr_mult: 1.0
|
565 |
+
decay_mult: 1.0
|
566 |
+
}
|
567 |
+
convolution_param {
|
568 |
+
num_output: 128
|
569 |
+
bias_term: false
|
570 |
+
pad: 1
|
571 |
+
kernel_size: 3
|
572 |
+
stride: 1 # 2
|
573 |
+
weight_filler {
|
574 |
+
type: "msra"
|
575 |
+
}
|
576 |
+
bias_filler {
|
577 |
+
type: "constant"
|
578 |
+
value: 0.0
|
579 |
+
}
|
580 |
+
}
|
581 |
+
}
|
582 |
+
layer {
|
583 |
+
name: "layer_512_1_bn2_h"
|
584 |
+
type: "BatchNorm"
|
585 |
+
bottom: "layer_512_1_conv1_h"
|
586 |
+
top: "layer_512_1_conv1_h"
|
587 |
+
param {
|
588 |
+
lr_mult: 0.0
|
589 |
+
}
|
590 |
+
param {
|
591 |
+
lr_mult: 0.0
|
592 |
+
}
|
593 |
+
param {
|
594 |
+
lr_mult: 0.0
|
595 |
+
}
|
596 |
+
}
|
597 |
+
layer {
|
598 |
+
name: "layer_512_1_scale2_h"
|
599 |
+
type: "Scale"
|
600 |
+
bottom: "layer_512_1_conv1_h"
|
601 |
+
top: "layer_512_1_conv1_h"
|
602 |
+
param {
|
603 |
+
lr_mult: 1.0
|
604 |
+
decay_mult: 1.0
|
605 |
+
}
|
606 |
+
param {
|
607 |
+
lr_mult: 2.0
|
608 |
+
decay_mult: 1.0
|
609 |
+
}
|
610 |
+
scale_param {
|
611 |
+
bias_term: true
|
612 |
+
}
|
613 |
+
}
|
614 |
+
layer {
|
615 |
+
name: "layer_512_1_relu2"
|
616 |
+
type: "ReLU"
|
617 |
+
bottom: "layer_512_1_conv1_h"
|
618 |
+
top: "layer_512_1_conv1_h"
|
619 |
+
}
|
620 |
+
layer {
|
621 |
+
name: "layer_512_1_conv2_h"
|
622 |
+
type: "Convolution"
|
623 |
+
bottom: "layer_512_1_conv1_h"
|
624 |
+
top: "layer_512_1_conv2_h"
|
625 |
+
param {
|
626 |
+
lr_mult: 1.0
|
627 |
+
decay_mult: 1.0
|
628 |
+
}
|
629 |
+
convolution_param {
|
630 |
+
num_output: 256
|
631 |
+
bias_term: false
|
632 |
+
pad: 2 # 1
|
633 |
+
kernel_size: 3
|
634 |
+
stride: 1
|
635 |
+
dilation: 2
|
636 |
+
weight_filler {
|
637 |
+
type: "msra"
|
638 |
+
}
|
639 |
+
bias_filler {
|
640 |
+
type: "constant"
|
641 |
+
value: 0.0
|
642 |
+
}
|
643 |
+
}
|
644 |
+
}
|
645 |
+
layer {
|
646 |
+
name: "layer_512_1_conv_expand_h"
|
647 |
+
type: "Convolution"
|
648 |
+
bottom: "layer_512_1_bn1"
|
649 |
+
top: "layer_512_1_conv_expand_h"
|
650 |
+
param {
|
651 |
+
lr_mult: 1.0
|
652 |
+
decay_mult: 1.0
|
653 |
+
}
|
654 |
+
convolution_param {
|
655 |
+
num_output: 256
|
656 |
+
bias_term: false
|
657 |
+
pad: 0
|
658 |
+
kernel_size: 1
|
659 |
+
stride: 1 # 2
|
660 |
+
weight_filler {
|
661 |
+
type: "msra"
|
662 |
+
}
|
663 |
+
bias_filler {
|
664 |
+
type: "constant"
|
665 |
+
value: 0.0
|
666 |
+
}
|
667 |
+
}
|
668 |
+
}
|
669 |
+
layer {
|
670 |
+
name: "layer_512_1_sum"
|
671 |
+
type: "Eltwise"
|
672 |
+
bottom: "layer_512_1_conv2_h"
|
673 |
+
bottom: "layer_512_1_conv_expand_h"
|
674 |
+
top: "layer_512_1_sum"
|
675 |
+
}
|
676 |
+
layer {
|
677 |
+
name: "last_bn_h"
|
678 |
+
type: "BatchNorm"
|
679 |
+
bottom: "layer_512_1_sum"
|
680 |
+
top: "layer_512_1_sum"
|
681 |
+
param {
|
682 |
+
lr_mult: 0.0
|
683 |
+
}
|
684 |
+
param {
|
685 |
+
lr_mult: 0.0
|
686 |
+
}
|
687 |
+
param {
|
688 |
+
lr_mult: 0.0
|
689 |
+
}
|
690 |
+
}
|
691 |
+
layer {
|
692 |
+
name: "last_scale_h"
|
693 |
+
type: "Scale"
|
694 |
+
bottom: "layer_512_1_sum"
|
695 |
+
top: "layer_512_1_sum"
|
696 |
+
param {
|
697 |
+
lr_mult: 1.0
|
698 |
+
decay_mult: 1.0
|
699 |
+
}
|
700 |
+
param {
|
701 |
+
lr_mult: 2.0
|
702 |
+
decay_mult: 1.0
|
703 |
+
}
|
704 |
+
scale_param {
|
705 |
+
bias_term: true
|
706 |
+
}
|
707 |
+
}
|
708 |
+
layer {
|
709 |
+
name: "last_relu"
|
710 |
+
type: "ReLU"
|
711 |
+
bottom: "layer_512_1_sum"
|
712 |
+
top: "fc7"
|
713 |
+
}
|
714 |
+
|
715 |
+
layer {
|
716 |
+
name: "conv6_1_h"
|
717 |
+
type: "Convolution"
|
718 |
+
bottom: "fc7"
|
719 |
+
top: "conv6_1_h"
|
720 |
+
param {
|
721 |
+
lr_mult: 1
|
722 |
+
decay_mult: 1
|
723 |
+
}
|
724 |
+
param {
|
725 |
+
lr_mult: 2
|
726 |
+
decay_mult: 0
|
727 |
+
}
|
728 |
+
convolution_param {
|
729 |
+
num_output: 128
|
730 |
+
pad: 0
|
731 |
+
kernel_size: 1
|
732 |
+
stride: 1
|
733 |
+
weight_filler {
|
734 |
+
type: "xavier"
|
735 |
+
}
|
736 |
+
bias_filler {
|
737 |
+
type: "constant"
|
738 |
+
value: 0
|
739 |
+
}
|
740 |
+
}
|
741 |
+
}
|
742 |
+
layer {
|
743 |
+
name: "conv6_1_relu"
|
744 |
+
type: "ReLU"
|
745 |
+
bottom: "conv6_1_h"
|
746 |
+
top: "conv6_1_h"
|
747 |
+
}
|
748 |
+
layer {
|
749 |
+
name: "conv6_2_h"
|
750 |
+
type: "Convolution"
|
751 |
+
bottom: "conv6_1_h"
|
752 |
+
top: "conv6_2_h"
|
753 |
+
param {
|
754 |
+
lr_mult: 1
|
755 |
+
decay_mult: 1
|
756 |
+
}
|
757 |
+
param {
|
758 |
+
lr_mult: 2
|
759 |
+
decay_mult: 0
|
760 |
+
}
|
761 |
+
convolution_param {
|
762 |
+
num_output: 256
|
763 |
+
pad: 1
|
764 |
+
kernel_size: 3
|
765 |
+
stride: 2
|
766 |
+
weight_filler {
|
767 |
+
type: "xavier"
|
768 |
+
}
|
769 |
+
bias_filler {
|
770 |
+
type: "constant"
|
771 |
+
value: 0
|
772 |
+
}
|
773 |
+
}
|
774 |
+
}
|
775 |
+
layer {
|
776 |
+
name: "conv6_2_relu"
|
777 |
+
type: "ReLU"
|
778 |
+
bottom: "conv6_2_h"
|
779 |
+
top: "conv6_2_h"
|
780 |
+
}
|
781 |
+
layer {
|
782 |
+
name: "conv7_1_h"
|
783 |
+
type: "Convolution"
|
784 |
+
bottom: "conv6_2_h"
|
785 |
+
top: "conv7_1_h"
|
786 |
+
param {
|
787 |
+
lr_mult: 1
|
788 |
+
decay_mult: 1
|
789 |
+
}
|
790 |
+
param {
|
791 |
+
lr_mult: 2
|
792 |
+
decay_mult: 0
|
793 |
+
}
|
794 |
+
convolution_param {
|
795 |
+
num_output: 64
|
796 |
+
pad: 0
|
797 |
+
kernel_size: 1
|
798 |
+
stride: 1
|
799 |
+
weight_filler {
|
800 |
+
type: "xavier"
|
801 |
+
}
|
802 |
+
bias_filler {
|
803 |
+
type: "constant"
|
804 |
+
value: 0
|
805 |
+
}
|
806 |
+
}
|
807 |
+
}
|
808 |
+
layer {
|
809 |
+
name: "conv7_1_relu"
|
810 |
+
type: "ReLU"
|
811 |
+
bottom: "conv7_1_h"
|
812 |
+
top: "conv7_1_h"
|
813 |
+
}
|
814 |
+
layer {
|
815 |
+
name: "conv7_2_h"
|
816 |
+
type: "Convolution"
|
817 |
+
bottom: "conv7_1_h"
|
818 |
+
top: "conv7_2_h"
|
819 |
+
param {
|
820 |
+
lr_mult: 1
|
821 |
+
decay_mult: 1
|
822 |
+
}
|
823 |
+
param {
|
824 |
+
lr_mult: 2
|
825 |
+
decay_mult: 0
|
826 |
+
}
|
827 |
+
convolution_param {
|
828 |
+
num_output: 128
|
829 |
+
pad: 1
|
830 |
+
kernel_size: 3
|
831 |
+
stride: 2
|
832 |
+
weight_filler {
|
833 |
+
type: "xavier"
|
834 |
+
}
|
835 |
+
bias_filler {
|
836 |
+
type: "constant"
|
837 |
+
value: 0
|
838 |
+
}
|
839 |
+
}
|
840 |
+
}
|
841 |
+
layer {
|
842 |
+
name: "conv7_2_relu"
|
843 |
+
type: "ReLU"
|
844 |
+
bottom: "conv7_2_h"
|
845 |
+
top: "conv7_2_h"
|
846 |
+
}
|
847 |
+
layer {
|
848 |
+
name: "conv8_1_h"
|
849 |
+
type: "Convolution"
|
850 |
+
bottom: "conv7_2_h"
|
851 |
+
top: "conv8_1_h"
|
852 |
+
param {
|
853 |
+
lr_mult: 1
|
854 |
+
decay_mult: 1
|
855 |
+
}
|
856 |
+
param {
|
857 |
+
lr_mult: 2
|
858 |
+
decay_mult: 0
|
859 |
+
}
|
860 |
+
convolution_param {
|
861 |
+
num_output: 64
|
862 |
+
pad: 0
|
863 |
+
kernel_size: 1
|
864 |
+
stride: 1
|
865 |
+
weight_filler {
|
866 |
+
type: "xavier"
|
867 |
+
}
|
868 |
+
bias_filler {
|
869 |
+
type: "constant"
|
870 |
+
value: 0
|
871 |
+
}
|
872 |
+
}
|
873 |
+
}
|
874 |
+
layer {
|
875 |
+
name: "conv8_1_relu"
|
876 |
+
type: "ReLU"
|
877 |
+
bottom: "conv8_1_h"
|
878 |
+
top: "conv8_1_h"
|
879 |
+
}
|
880 |
+
layer {
|
881 |
+
name: "conv8_2_h"
|
882 |
+
type: "Convolution"
|
883 |
+
bottom: "conv8_1_h"
|
884 |
+
top: "conv8_2_h"
|
885 |
+
param {
|
886 |
+
lr_mult: 1
|
887 |
+
decay_mult: 1
|
888 |
+
}
|
889 |
+
param {
|
890 |
+
lr_mult: 2
|
891 |
+
decay_mult: 0
|
892 |
+
}
|
893 |
+
convolution_param {
|
894 |
+
num_output: 128
|
895 |
+
pad: 1
|
896 |
+
kernel_size: 3
|
897 |
+
stride: 1
|
898 |
+
weight_filler {
|
899 |
+
type: "xavier"
|
900 |
+
}
|
901 |
+
bias_filler {
|
902 |
+
type: "constant"
|
903 |
+
value: 0
|
904 |
+
}
|
905 |
+
}
|
906 |
+
}
|
907 |
+
layer {
|
908 |
+
name: "conv8_2_relu"
|
909 |
+
type: "ReLU"
|
910 |
+
bottom: "conv8_2_h"
|
911 |
+
top: "conv8_2_h"
|
912 |
+
}
|
913 |
+
layer {
|
914 |
+
name: "conv9_1_h"
|
915 |
+
type: "Convolution"
|
916 |
+
bottom: "conv8_2_h"
|
917 |
+
top: "conv9_1_h"
|
918 |
+
param {
|
919 |
+
lr_mult: 1
|
920 |
+
decay_mult: 1
|
921 |
+
}
|
922 |
+
param {
|
923 |
+
lr_mult: 2
|
924 |
+
decay_mult: 0
|
925 |
+
}
|
926 |
+
convolution_param {
|
927 |
+
num_output: 64
|
928 |
+
pad: 0
|
929 |
+
kernel_size: 1
|
930 |
+
stride: 1
|
931 |
+
weight_filler {
|
932 |
+
type: "xavier"
|
933 |
+
}
|
934 |
+
bias_filler {
|
935 |
+
type: "constant"
|
936 |
+
value: 0
|
937 |
+
}
|
938 |
+
}
|
939 |
+
}
|
940 |
+
layer {
|
941 |
+
name: "conv9_1_relu"
|
942 |
+
type: "ReLU"
|
943 |
+
bottom: "conv9_1_h"
|
944 |
+
top: "conv9_1_h"
|
945 |
+
}
|
946 |
+
layer {
|
947 |
+
name: "conv9_2_h"
|
948 |
+
type: "Convolution"
|
949 |
+
bottom: "conv9_1_h"
|
950 |
+
top: "conv9_2_h"
|
951 |
+
param {
|
952 |
+
lr_mult: 1
|
953 |
+
decay_mult: 1
|
954 |
+
}
|
955 |
+
param {
|
956 |
+
lr_mult: 2
|
957 |
+
decay_mult: 0
|
958 |
+
}
|
959 |
+
convolution_param {
|
960 |
+
num_output: 128
|
961 |
+
pad: 1
|
962 |
+
kernel_size: 3
|
963 |
+
stride: 1
|
964 |
+
weight_filler {
|
965 |
+
type: "xavier"
|
966 |
+
}
|
967 |
+
bias_filler {
|
968 |
+
type: "constant"
|
969 |
+
value: 0
|
970 |
+
}
|
971 |
+
}
|
972 |
+
}
|
973 |
+
layer {
|
974 |
+
name: "conv9_2_relu"
|
975 |
+
type: "ReLU"
|
976 |
+
bottom: "conv9_2_h"
|
977 |
+
top: "conv9_2_h"
|
978 |
+
}
|
979 |
+
layer {
|
980 |
+
name: "conv4_3_norm"
|
981 |
+
type: "Normalize"
|
982 |
+
bottom: "layer_256_1_bn1"
|
983 |
+
top: "conv4_3_norm"
|
984 |
+
norm_param {
|
985 |
+
across_spatial: false
|
986 |
+
scale_filler {
|
987 |
+
type: "constant"
|
988 |
+
value: 20
|
989 |
+
}
|
990 |
+
channel_shared: false
|
991 |
+
}
|
992 |
+
}
|
993 |
+
layer {
|
994 |
+
name: "conv4_3_norm_mbox_loc"
|
995 |
+
type: "Convolution"
|
996 |
+
bottom: "conv4_3_norm"
|
997 |
+
top: "conv4_3_norm_mbox_loc"
|
998 |
+
param {
|
999 |
+
lr_mult: 1
|
1000 |
+
decay_mult: 1
|
1001 |
+
}
|
1002 |
+
param {
|
1003 |
+
lr_mult: 2
|
1004 |
+
decay_mult: 0
|
1005 |
+
}
|
1006 |
+
convolution_param {
|
1007 |
+
num_output: 16
|
1008 |
+
pad: 1
|
1009 |
+
kernel_size: 3
|
1010 |
+
stride: 1
|
1011 |
+
weight_filler {
|
1012 |
+
type: "xavier"
|
1013 |
+
}
|
1014 |
+
bias_filler {
|
1015 |
+
type: "constant"
|
1016 |
+
value: 0
|
1017 |
+
}
|
1018 |
+
}
|
1019 |
+
}
|
1020 |
+
layer {
|
1021 |
+
name: "conv4_3_norm_mbox_loc_perm"
|
1022 |
+
type: "Permute"
|
1023 |
+
bottom: "conv4_3_norm_mbox_loc"
|
1024 |
+
top: "conv4_3_norm_mbox_loc_perm"
|
1025 |
+
permute_param {
|
1026 |
+
order: 0
|
1027 |
+
order: 2
|
1028 |
+
order: 3
|
1029 |
+
order: 1
|
1030 |
+
}
|
1031 |
+
}
|
1032 |
+
layer {
|
1033 |
+
name: "conv4_3_norm_mbox_loc_flat"
|
1034 |
+
type: "Flatten"
|
1035 |
+
bottom: "conv4_3_norm_mbox_loc_perm"
|
1036 |
+
top: "conv4_3_norm_mbox_loc_flat"
|
1037 |
+
flatten_param {
|
1038 |
+
axis: 1
|
1039 |
+
}
|
1040 |
+
}
|
1041 |
+
layer {
|
1042 |
+
name: "conv4_3_norm_mbox_conf"
|
1043 |
+
type: "Convolution"
|
1044 |
+
bottom: "conv4_3_norm"
|
1045 |
+
top: "conv4_3_norm_mbox_conf"
|
1046 |
+
param {
|
1047 |
+
lr_mult: 1
|
1048 |
+
decay_mult: 1
|
1049 |
+
}
|
1050 |
+
param {
|
1051 |
+
lr_mult: 2
|
1052 |
+
decay_mult: 0
|
1053 |
+
}
|
1054 |
+
convolution_param {
|
1055 |
+
num_output: 8 # 84
|
1056 |
+
pad: 1
|
1057 |
+
kernel_size: 3
|
1058 |
+
stride: 1
|
1059 |
+
weight_filler {
|
1060 |
+
type: "xavier"
|
1061 |
+
}
|
1062 |
+
bias_filler {
|
1063 |
+
type: "constant"
|
1064 |
+
value: 0
|
1065 |
+
}
|
1066 |
+
}
|
1067 |
+
}
|
1068 |
+
layer {
|
1069 |
+
name: "conv4_3_norm_mbox_conf_perm"
|
1070 |
+
type: "Permute"
|
1071 |
+
bottom: "conv4_3_norm_mbox_conf"
|
1072 |
+
top: "conv4_3_norm_mbox_conf_perm"
|
1073 |
+
permute_param {
|
1074 |
+
order: 0
|
1075 |
+
order: 2
|
1076 |
+
order: 3
|
1077 |
+
order: 1
|
1078 |
+
}
|
1079 |
+
}
|
1080 |
+
layer {
|
1081 |
+
name: "conv4_3_norm_mbox_conf_flat"
|
1082 |
+
type: "Flatten"
|
1083 |
+
bottom: "conv4_3_norm_mbox_conf_perm"
|
1084 |
+
top: "conv4_3_norm_mbox_conf_flat"
|
1085 |
+
flatten_param {
|
1086 |
+
axis: 1
|
1087 |
+
}
|
1088 |
+
}
|
1089 |
+
layer {
|
1090 |
+
name: "conv4_3_norm_mbox_priorbox"
|
1091 |
+
type: "PriorBox"
|
1092 |
+
bottom: "conv4_3_norm"
|
1093 |
+
bottom: "data"
|
1094 |
+
top: "conv4_3_norm_mbox_priorbox"
|
1095 |
+
prior_box_param {
|
1096 |
+
min_size: 30.0
|
1097 |
+
max_size: 60.0
|
1098 |
+
aspect_ratio: 2
|
1099 |
+
flip: true
|
1100 |
+
clip: false
|
1101 |
+
variance: 0.1
|
1102 |
+
variance: 0.1
|
1103 |
+
variance: 0.2
|
1104 |
+
variance: 0.2
|
1105 |
+
step: 8
|
1106 |
+
offset: 0.5
|
1107 |
+
}
|
1108 |
+
}
|
1109 |
+
layer {
|
1110 |
+
name: "fc7_mbox_loc"
|
1111 |
+
type: "Convolution"
|
1112 |
+
bottom: "fc7"
|
1113 |
+
top: "fc7_mbox_loc"
|
1114 |
+
param {
|
1115 |
+
lr_mult: 1
|
1116 |
+
decay_mult: 1
|
1117 |
+
}
|
1118 |
+
param {
|
1119 |
+
lr_mult: 2
|
1120 |
+
decay_mult: 0
|
1121 |
+
}
|
1122 |
+
convolution_param {
|
1123 |
+
num_output: 24
|
1124 |
+
pad: 1
|
1125 |
+
kernel_size: 3
|
1126 |
+
stride: 1
|
1127 |
+
weight_filler {
|
1128 |
+
type: "xavier"
|
1129 |
+
}
|
1130 |
+
bias_filler {
|
1131 |
+
type: "constant"
|
1132 |
+
value: 0
|
1133 |
+
}
|
1134 |
+
}
|
1135 |
+
}
|
1136 |
+
layer {
|
1137 |
+
name: "fc7_mbox_loc_perm"
|
1138 |
+
type: "Permute"
|
1139 |
+
bottom: "fc7_mbox_loc"
|
1140 |
+
top: "fc7_mbox_loc_perm"
|
1141 |
+
permute_param {
|
1142 |
+
order: 0
|
1143 |
+
order: 2
|
1144 |
+
order: 3
|
1145 |
+
order: 1
|
1146 |
+
}
|
1147 |
+
}
|
1148 |
+
layer {
|
1149 |
+
name: "fc7_mbox_loc_flat"
|
1150 |
+
type: "Flatten"
|
1151 |
+
bottom: "fc7_mbox_loc_perm"
|
1152 |
+
top: "fc7_mbox_loc_flat"
|
1153 |
+
flatten_param {
|
1154 |
+
axis: 1
|
1155 |
+
}
|
1156 |
+
}
|
1157 |
+
layer {
|
1158 |
+
name: "fc7_mbox_conf"
|
1159 |
+
type: "Convolution"
|
1160 |
+
bottom: "fc7"
|
1161 |
+
top: "fc7_mbox_conf"
|
1162 |
+
param {
|
1163 |
+
lr_mult: 1
|
1164 |
+
decay_mult: 1
|
1165 |
+
}
|
1166 |
+
param {
|
1167 |
+
lr_mult: 2
|
1168 |
+
decay_mult: 0
|
1169 |
+
}
|
1170 |
+
convolution_param {
|
1171 |
+
num_output: 12 # 126
|
1172 |
+
pad: 1
|
1173 |
+
kernel_size: 3
|
1174 |
+
stride: 1
|
1175 |
+
weight_filler {
|
1176 |
+
type: "xavier"
|
1177 |
+
}
|
1178 |
+
bias_filler {
|
1179 |
+
type: "constant"
|
1180 |
+
value: 0
|
1181 |
+
}
|
1182 |
+
}
|
1183 |
+
}
|
1184 |
+
layer {
|
1185 |
+
name: "fc7_mbox_conf_perm"
|
1186 |
+
type: "Permute"
|
1187 |
+
bottom: "fc7_mbox_conf"
|
1188 |
+
top: "fc7_mbox_conf_perm"
|
1189 |
+
permute_param {
|
1190 |
+
order: 0
|
1191 |
+
order: 2
|
1192 |
+
order: 3
|
1193 |
+
order: 1
|
1194 |
+
}
|
1195 |
+
}
|
1196 |
+
layer {
|
1197 |
+
name: "fc7_mbox_conf_flat"
|
1198 |
+
type: "Flatten"
|
1199 |
+
bottom: "fc7_mbox_conf_perm"
|
1200 |
+
top: "fc7_mbox_conf_flat"
|
1201 |
+
flatten_param {
|
1202 |
+
axis: 1
|
1203 |
+
}
|
1204 |
+
}
|
1205 |
+
layer {
|
1206 |
+
name: "fc7_mbox_priorbox"
|
1207 |
+
type: "PriorBox"
|
1208 |
+
bottom: "fc7"
|
1209 |
+
bottom: "data"
|
1210 |
+
top: "fc7_mbox_priorbox"
|
1211 |
+
prior_box_param {
|
1212 |
+
min_size: 60.0
|
1213 |
+
max_size: 111.0
|
1214 |
+
aspect_ratio: 2
|
1215 |
+
aspect_ratio: 3
|
1216 |
+
flip: true
|
1217 |
+
clip: false
|
1218 |
+
variance: 0.1
|
1219 |
+
variance: 0.1
|
1220 |
+
variance: 0.2
|
1221 |
+
variance: 0.2
|
1222 |
+
step: 16
|
1223 |
+
offset: 0.5
|
1224 |
+
}
|
1225 |
+
}
|
1226 |
+
layer {
|
1227 |
+
name: "conv6_2_mbox_loc"
|
1228 |
+
type: "Convolution"
|
1229 |
+
bottom: "conv6_2_h"
|
1230 |
+
top: "conv6_2_mbox_loc"
|
1231 |
+
param {
|
1232 |
+
lr_mult: 1
|
1233 |
+
decay_mult: 1
|
1234 |
+
}
|
1235 |
+
param {
|
1236 |
+
lr_mult: 2
|
1237 |
+
decay_mult: 0
|
1238 |
+
}
|
1239 |
+
convolution_param {
|
1240 |
+
num_output: 24
|
1241 |
+
pad: 1
|
1242 |
+
kernel_size: 3
|
1243 |
+
stride: 1
|
1244 |
+
weight_filler {
|
1245 |
+
type: "xavier"
|
1246 |
+
}
|
1247 |
+
bias_filler {
|
1248 |
+
type: "constant"
|
1249 |
+
value: 0
|
1250 |
+
}
|
1251 |
+
}
|
1252 |
+
}
|
1253 |
+
layer {
|
1254 |
+
name: "conv6_2_mbox_loc_perm"
|
1255 |
+
type: "Permute"
|
1256 |
+
bottom: "conv6_2_mbox_loc"
|
1257 |
+
top: "conv6_2_mbox_loc_perm"
|
1258 |
+
permute_param {
|
1259 |
+
order: 0
|
1260 |
+
order: 2
|
1261 |
+
order: 3
|
1262 |
+
order: 1
|
1263 |
+
}
|
1264 |
+
}
|
1265 |
+
layer {
|
1266 |
+
name: "conv6_2_mbox_loc_flat"
|
1267 |
+
type: "Flatten"
|
1268 |
+
bottom: "conv6_2_mbox_loc_perm"
|
1269 |
+
top: "conv6_2_mbox_loc_flat"
|
1270 |
+
flatten_param {
|
1271 |
+
axis: 1
|
1272 |
+
}
|
1273 |
+
}
|
1274 |
+
layer {
|
1275 |
+
name: "conv6_2_mbox_conf"
|
1276 |
+
type: "Convolution"
|
1277 |
+
bottom: "conv6_2_h"
|
1278 |
+
top: "conv6_2_mbox_conf"
|
1279 |
+
param {
|
1280 |
+
lr_mult: 1
|
1281 |
+
decay_mult: 1
|
1282 |
+
}
|
1283 |
+
param {
|
1284 |
+
lr_mult: 2
|
1285 |
+
decay_mult: 0
|
1286 |
+
}
|
1287 |
+
convolution_param {
|
1288 |
+
num_output: 12 # 126
|
1289 |
+
pad: 1
|
1290 |
+
kernel_size: 3
|
1291 |
+
stride: 1
|
1292 |
+
weight_filler {
|
1293 |
+
type: "xavier"
|
1294 |
+
}
|
1295 |
+
bias_filler {
|
1296 |
+
type: "constant"
|
1297 |
+
value: 0
|
1298 |
+
}
|
1299 |
+
}
|
1300 |
+
}
|
1301 |
+
layer {
|
1302 |
+
name: "conv6_2_mbox_conf_perm"
|
1303 |
+
type: "Permute"
|
1304 |
+
bottom: "conv6_2_mbox_conf"
|
1305 |
+
top: "conv6_2_mbox_conf_perm"
|
1306 |
+
permute_param {
|
1307 |
+
order: 0
|
1308 |
+
order: 2
|
1309 |
+
order: 3
|
1310 |
+
order: 1
|
1311 |
+
}
|
1312 |
+
}
|
1313 |
+
layer {
|
1314 |
+
name: "conv6_2_mbox_conf_flat"
|
1315 |
+
type: "Flatten"
|
1316 |
+
bottom: "conv6_2_mbox_conf_perm"
|
1317 |
+
top: "conv6_2_mbox_conf_flat"
|
1318 |
+
flatten_param {
|
1319 |
+
axis: 1
|
1320 |
+
}
|
1321 |
+
}
|
1322 |
+
layer {
|
1323 |
+
name: "conv6_2_mbox_priorbox"
|
1324 |
+
type: "PriorBox"
|
1325 |
+
bottom: "conv6_2_h"
|
1326 |
+
bottom: "data"
|
1327 |
+
top: "conv6_2_mbox_priorbox"
|
1328 |
+
prior_box_param {
|
1329 |
+
min_size: 111.0
|
1330 |
+
max_size: 162.0
|
1331 |
+
aspect_ratio: 2
|
1332 |
+
aspect_ratio: 3
|
1333 |
+
flip: true
|
1334 |
+
clip: false
|
1335 |
+
variance: 0.1
|
1336 |
+
variance: 0.1
|
1337 |
+
variance: 0.2
|
1338 |
+
variance: 0.2
|
1339 |
+
step: 32
|
1340 |
+
offset: 0.5
|
1341 |
+
}
|
1342 |
+
}
|
1343 |
+
layer {
|
1344 |
+
name: "conv7_2_mbox_loc"
|
1345 |
+
type: "Convolution"
|
1346 |
+
bottom: "conv7_2_h"
|
1347 |
+
top: "conv7_2_mbox_loc"
|
1348 |
+
param {
|
1349 |
+
lr_mult: 1
|
1350 |
+
decay_mult: 1
|
1351 |
+
}
|
1352 |
+
param {
|
1353 |
+
lr_mult: 2
|
1354 |
+
decay_mult: 0
|
1355 |
+
}
|
1356 |
+
convolution_param {
|
1357 |
+
num_output: 24
|
1358 |
+
pad: 1
|
1359 |
+
kernel_size: 3
|
1360 |
+
stride: 1
|
1361 |
+
weight_filler {
|
1362 |
+
type: "xavier"
|
1363 |
+
}
|
1364 |
+
bias_filler {
|
1365 |
+
type: "constant"
|
1366 |
+
value: 0
|
1367 |
+
}
|
1368 |
+
}
|
1369 |
+
}
|
1370 |
+
layer {
|
1371 |
+
name: "conv7_2_mbox_loc_perm"
|
1372 |
+
type: "Permute"
|
1373 |
+
bottom: "conv7_2_mbox_loc"
|
1374 |
+
top: "conv7_2_mbox_loc_perm"
|
1375 |
+
permute_param {
|
1376 |
+
order: 0
|
1377 |
+
order: 2
|
1378 |
+
order: 3
|
1379 |
+
order: 1
|
1380 |
+
}
|
1381 |
+
}
|
1382 |
+
layer {
|
1383 |
+
name: "conv7_2_mbox_loc_flat"
|
1384 |
+
type: "Flatten"
|
1385 |
+
bottom: "conv7_2_mbox_loc_perm"
|
1386 |
+
top: "conv7_2_mbox_loc_flat"
|
1387 |
+
flatten_param {
|
1388 |
+
axis: 1
|
1389 |
+
}
|
1390 |
+
}
|
1391 |
+
layer {
|
1392 |
+
name: "conv7_2_mbox_conf"
|
1393 |
+
type: "Convolution"
|
1394 |
+
bottom: "conv7_2_h"
|
1395 |
+
top: "conv7_2_mbox_conf"
|
1396 |
+
param {
|
1397 |
+
lr_mult: 1
|
1398 |
+
decay_mult: 1
|
1399 |
+
}
|
1400 |
+
param {
|
1401 |
+
lr_mult: 2
|
1402 |
+
decay_mult: 0
|
1403 |
+
}
|
1404 |
+
convolution_param {
|
1405 |
+
num_output: 12 # 126
|
1406 |
+
pad: 1
|
1407 |
+
kernel_size: 3
|
1408 |
+
stride: 1
|
1409 |
+
weight_filler {
|
1410 |
+
type: "xavier"
|
1411 |
+
}
|
1412 |
+
bias_filler {
|
1413 |
+
type: "constant"
|
1414 |
+
value: 0
|
1415 |
+
}
|
1416 |
+
}
|
1417 |
+
}
|
1418 |
+
layer {
|
1419 |
+
name: "conv7_2_mbox_conf_perm"
|
1420 |
+
type: "Permute"
|
1421 |
+
bottom: "conv7_2_mbox_conf"
|
1422 |
+
top: "conv7_2_mbox_conf_perm"
|
1423 |
+
permute_param {
|
1424 |
+
order: 0
|
1425 |
+
order: 2
|
1426 |
+
order: 3
|
1427 |
+
order: 1
|
1428 |
+
}
|
1429 |
+
}
|
1430 |
+
layer {
|
1431 |
+
name: "conv7_2_mbox_conf_flat"
|
1432 |
+
type: "Flatten"
|
1433 |
+
bottom: "conv7_2_mbox_conf_perm"
|
1434 |
+
top: "conv7_2_mbox_conf_flat"
|
1435 |
+
flatten_param {
|
1436 |
+
axis: 1
|
1437 |
+
}
|
1438 |
+
}
|
1439 |
+
layer {
|
1440 |
+
name: "conv7_2_mbox_priorbox"
|
1441 |
+
type: "PriorBox"
|
1442 |
+
bottom: "conv7_2_h"
|
1443 |
+
bottom: "data"
|
1444 |
+
top: "conv7_2_mbox_priorbox"
|
1445 |
+
prior_box_param {
|
1446 |
+
min_size: 162.0
|
1447 |
+
max_size: 213.0
|
1448 |
+
aspect_ratio: 2
|
1449 |
+
aspect_ratio: 3
|
1450 |
+
flip: true
|
1451 |
+
clip: false
|
1452 |
+
variance: 0.1
|
1453 |
+
variance: 0.1
|
1454 |
+
variance: 0.2
|
1455 |
+
variance: 0.2
|
1456 |
+
step: 64
|
1457 |
+
offset: 0.5
|
1458 |
+
}
|
1459 |
+
}
|
1460 |
+
layer {
|
1461 |
+
name: "conv8_2_mbox_loc"
|
1462 |
+
type: "Convolution"
|
1463 |
+
bottom: "conv8_2_h"
|
1464 |
+
top: "conv8_2_mbox_loc"
|
1465 |
+
param {
|
1466 |
+
lr_mult: 1
|
1467 |
+
decay_mult: 1
|
1468 |
+
}
|
1469 |
+
param {
|
1470 |
+
lr_mult: 2
|
1471 |
+
decay_mult: 0
|
1472 |
+
}
|
1473 |
+
convolution_param {
|
1474 |
+
num_output: 16
|
1475 |
+
pad: 1
|
1476 |
+
kernel_size: 3
|
1477 |
+
stride: 1
|
1478 |
+
weight_filler {
|
1479 |
+
type: "xavier"
|
1480 |
+
}
|
1481 |
+
bias_filler {
|
1482 |
+
type: "constant"
|
1483 |
+
value: 0
|
1484 |
+
}
|
1485 |
+
}
|
1486 |
+
}
|
1487 |
+
layer {
|
1488 |
+
name: "conv8_2_mbox_loc_perm"
|
1489 |
+
type: "Permute"
|
1490 |
+
bottom: "conv8_2_mbox_loc"
|
1491 |
+
top: "conv8_2_mbox_loc_perm"
|
1492 |
+
permute_param {
|
1493 |
+
order: 0
|
1494 |
+
order: 2
|
1495 |
+
order: 3
|
1496 |
+
order: 1
|
1497 |
+
}
|
1498 |
+
}
|
1499 |
+
layer {
|
1500 |
+
name: "conv8_2_mbox_loc_flat"
|
1501 |
+
type: "Flatten"
|
1502 |
+
bottom: "conv8_2_mbox_loc_perm"
|
1503 |
+
top: "conv8_2_mbox_loc_flat"
|
1504 |
+
flatten_param {
|
1505 |
+
axis: 1
|
1506 |
+
}
|
1507 |
+
}
|
1508 |
+
layer {
|
1509 |
+
name: "conv8_2_mbox_conf"
|
1510 |
+
type: "Convolution"
|
1511 |
+
bottom: "conv8_2_h"
|
1512 |
+
top: "conv8_2_mbox_conf"
|
1513 |
+
param {
|
1514 |
+
lr_mult: 1
|
1515 |
+
decay_mult: 1
|
1516 |
+
}
|
1517 |
+
param {
|
1518 |
+
lr_mult: 2
|
1519 |
+
decay_mult: 0
|
1520 |
+
}
|
1521 |
+
convolution_param {
|
1522 |
+
num_output: 8 # 84
|
1523 |
+
pad: 1
|
1524 |
+
kernel_size: 3
|
1525 |
+
stride: 1
|
1526 |
+
weight_filler {
|
1527 |
+
type: "xavier"
|
1528 |
+
}
|
1529 |
+
bias_filler {
|
1530 |
+
type: "constant"
|
1531 |
+
value: 0
|
1532 |
+
}
|
1533 |
+
}
|
1534 |
+
}
|
1535 |
+
layer {
|
1536 |
+
name: "conv8_2_mbox_conf_perm"
|
1537 |
+
type: "Permute"
|
1538 |
+
bottom: "conv8_2_mbox_conf"
|
1539 |
+
top: "conv8_2_mbox_conf_perm"
|
1540 |
+
permute_param {
|
1541 |
+
order: 0
|
1542 |
+
order: 2
|
1543 |
+
order: 3
|
1544 |
+
order: 1
|
1545 |
+
}
|
1546 |
+
}
|
1547 |
+
layer {
|
1548 |
+
name: "conv8_2_mbox_conf_flat"
|
1549 |
+
type: "Flatten"
|
1550 |
+
bottom: "conv8_2_mbox_conf_perm"
|
1551 |
+
top: "conv8_2_mbox_conf_flat"
|
1552 |
+
flatten_param {
|
1553 |
+
axis: 1
|
1554 |
+
}
|
1555 |
+
}
|
1556 |
+
layer {
|
1557 |
+
name: "conv8_2_mbox_priorbox"
|
1558 |
+
type: "PriorBox"
|
1559 |
+
bottom: "conv8_2_h"
|
1560 |
+
bottom: "data"
|
1561 |
+
top: "conv8_2_mbox_priorbox"
|
1562 |
+
prior_box_param {
|
1563 |
+
min_size: 213.0
|
1564 |
+
max_size: 264.0
|
1565 |
+
aspect_ratio: 2
|
1566 |
+
flip: true
|
1567 |
+
clip: false
|
1568 |
+
variance: 0.1
|
1569 |
+
variance: 0.1
|
1570 |
+
variance: 0.2
|
1571 |
+
variance: 0.2
|
1572 |
+
step: 100
|
1573 |
+
offset: 0.5
|
1574 |
+
}
|
1575 |
+
}
|
1576 |
+
layer {
|
1577 |
+
name: "conv9_2_mbox_loc"
|
1578 |
+
type: "Convolution"
|
1579 |
+
bottom: "conv9_2_h"
|
1580 |
+
top: "conv9_2_mbox_loc"
|
1581 |
+
param {
|
1582 |
+
lr_mult: 1
|
1583 |
+
decay_mult: 1
|
1584 |
+
}
|
1585 |
+
param {
|
1586 |
+
lr_mult: 2
|
1587 |
+
decay_mult: 0
|
1588 |
+
}
|
1589 |
+
convolution_param {
|
1590 |
+
num_output: 16
|
1591 |
+
pad: 1
|
1592 |
+
kernel_size: 3
|
1593 |
+
stride: 1
|
1594 |
+
weight_filler {
|
1595 |
+
type: "xavier"
|
1596 |
+
}
|
1597 |
+
bias_filler {
|
1598 |
+
type: "constant"
|
1599 |
+
value: 0
|
1600 |
+
}
|
1601 |
+
}
|
1602 |
+
}
|
1603 |
+
layer {
|
1604 |
+
name: "conv9_2_mbox_loc_perm"
|
1605 |
+
type: "Permute"
|
1606 |
+
bottom: "conv9_2_mbox_loc"
|
1607 |
+
top: "conv9_2_mbox_loc_perm"
|
1608 |
+
permute_param {
|
1609 |
+
order: 0
|
1610 |
+
order: 2
|
1611 |
+
order: 3
|
1612 |
+
order: 1
|
1613 |
+
}
|
1614 |
+
}
|
1615 |
+
layer {
|
1616 |
+
name: "conv9_2_mbox_loc_flat"
|
1617 |
+
type: "Flatten"
|
1618 |
+
bottom: "conv9_2_mbox_loc_perm"
|
1619 |
+
top: "conv9_2_mbox_loc_flat"
|
1620 |
+
flatten_param {
|
1621 |
+
axis: 1
|
1622 |
+
}
|
1623 |
+
}
|
1624 |
+
layer {
|
1625 |
+
name: "conv9_2_mbox_conf"
|
1626 |
+
type: "Convolution"
|
1627 |
+
bottom: "conv9_2_h"
|
1628 |
+
top: "conv9_2_mbox_conf"
|
1629 |
+
param {
|
1630 |
+
lr_mult: 1
|
1631 |
+
decay_mult: 1
|
1632 |
+
}
|
1633 |
+
param {
|
1634 |
+
lr_mult: 2
|
1635 |
+
decay_mult: 0
|
1636 |
+
}
|
1637 |
+
convolution_param {
|
1638 |
+
num_output: 8 # 84
|
1639 |
+
pad: 1
|
1640 |
+
kernel_size: 3
|
1641 |
+
stride: 1
|
1642 |
+
weight_filler {
|
1643 |
+
type: "xavier"
|
1644 |
+
}
|
1645 |
+
bias_filler {
|
1646 |
+
type: "constant"
|
1647 |
+
value: 0
|
1648 |
+
}
|
1649 |
+
}
|
1650 |
+
}
|
1651 |
+
layer {
|
1652 |
+
name: "conv9_2_mbox_conf_perm"
|
1653 |
+
type: "Permute"
|
1654 |
+
bottom: "conv9_2_mbox_conf"
|
1655 |
+
top: "conv9_2_mbox_conf_perm"
|
1656 |
+
permute_param {
|
1657 |
+
order: 0
|
1658 |
+
order: 2
|
1659 |
+
order: 3
|
1660 |
+
order: 1
|
1661 |
+
}
|
1662 |
+
}
|
1663 |
+
layer {
|
1664 |
+
name: "conv9_2_mbox_conf_flat"
|
1665 |
+
type: "Flatten"
|
1666 |
+
bottom: "conv9_2_mbox_conf_perm"
|
1667 |
+
top: "conv9_2_mbox_conf_flat"
|
1668 |
+
flatten_param {
|
1669 |
+
axis: 1
|
1670 |
+
}
|
1671 |
+
}
|
1672 |
+
layer {
|
1673 |
+
name: "conv9_2_mbox_priorbox"
|
1674 |
+
type: "PriorBox"
|
1675 |
+
bottom: "conv9_2_h"
|
1676 |
+
bottom: "data"
|
1677 |
+
top: "conv9_2_mbox_priorbox"
|
1678 |
+
prior_box_param {
|
1679 |
+
min_size: 264.0
|
1680 |
+
max_size: 315.0
|
1681 |
+
aspect_ratio: 2
|
1682 |
+
flip: true
|
1683 |
+
clip: false
|
1684 |
+
variance: 0.1
|
1685 |
+
variance: 0.1
|
1686 |
+
variance: 0.2
|
1687 |
+
variance: 0.2
|
1688 |
+
step: 300
|
1689 |
+
offset: 0.5
|
1690 |
+
}
|
1691 |
+
}
|
1692 |
+
layer {
|
1693 |
+
name: "mbox_loc"
|
1694 |
+
type: "Concat"
|
1695 |
+
bottom: "conv4_3_norm_mbox_loc_flat"
|
1696 |
+
bottom: "fc7_mbox_loc_flat"
|
1697 |
+
bottom: "conv6_2_mbox_loc_flat"
|
1698 |
+
bottom: "conv7_2_mbox_loc_flat"
|
1699 |
+
bottom: "conv8_2_mbox_loc_flat"
|
1700 |
+
bottom: "conv9_2_mbox_loc_flat"
|
1701 |
+
top: "mbox_loc"
|
1702 |
+
concat_param {
|
1703 |
+
axis: 1
|
1704 |
+
}
|
1705 |
+
}
|
1706 |
+
layer {
|
1707 |
+
name: "mbox_conf"
|
1708 |
+
type: "Concat"
|
1709 |
+
bottom: "conv4_3_norm_mbox_conf_flat"
|
1710 |
+
bottom: "fc7_mbox_conf_flat"
|
1711 |
+
bottom: "conv6_2_mbox_conf_flat"
|
1712 |
+
bottom: "conv7_2_mbox_conf_flat"
|
1713 |
+
bottom: "conv8_2_mbox_conf_flat"
|
1714 |
+
bottom: "conv9_2_mbox_conf_flat"
|
1715 |
+
top: "mbox_conf"
|
1716 |
+
concat_param {
|
1717 |
+
axis: 1
|
1718 |
+
}
|
1719 |
+
}
|
1720 |
+
layer {
|
1721 |
+
name: "mbox_priorbox"
|
1722 |
+
type: "Concat"
|
1723 |
+
bottom: "conv4_3_norm_mbox_priorbox"
|
1724 |
+
bottom: "fc7_mbox_priorbox"
|
1725 |
+
bottom: "conv6_2_mbox_priorbox"
|
1726 |
+
bottom: "conv7_2_mbox_priorbox"
|
1727 |
+
bottom: "conv8_2_mbox_priorbox"
|
1728 |
+
bottom: "conv9_2_mbox_priorbox"
|
1729 |
+
top: "mbox_priorbox"
|
1730 |
+
concat_param {
|
1731 |
+
axis: 2
|
1732 |
+
}
|
1733 |
+
}
|
1734 |
+
|
1735 |
+
layer {
|
1736 |
+
name: "mbox_conf_reshape"
|
1737 |
+
type: "Reshape"
|
1738 |
+
bottom: "mbox_conf"
|
1739 |
+
top: "mbox_conf_reshape"
|
1740 |
+
reshape_param {
|
1741 |
+
shape {
|
1742 |
+
dim: 0
|
1743 |
+
dim: -1
|
1744 |
+
dim: 2
|
1745 |
+
}
|
1746 |
+
}
|
1747 |
+
}
|
1748 |
+
layer {
|
1749 |
+
name: "mbox_conf_softmax"
|
1750 |
+
type: "Softmax"
|
1751 |
+
bottom: "mbox_conf_reshape"
|
1752 |
+
top: "mbox_conf_softmax"
|
1753 |
+
softmax_param {
|
1754 |
+
axis: 2
|
1755 |
+
}
|
1756 |
+
}
|
1757 |
+
layer {
|
1758 |
+
name: "mbox_conf_flatten"
|
1759 |
+
type: "Flatten"
|
1760 |
+
bottom: "mbox_conf_softmax"
|
1761 |
+
top: "mbox_conf_flatten"
|
1762 |
+
flatten_param {
|
1763 |
+
axis: 1
|
1764 |
+
}
|
1765 |
+
}
|
1766 |
+
|
1767 |
+
layer {
|
1768 |
+
name: "detection_out"
|
1769 |
+
type: "DetectionOutput"
|
1770 |
+
bottom: "mbox_loc"
|
1771 |
+
bottom: "mbox_conf_flatten"
|
1772 |
+
bottom: "mbox_priorbox"
|
1773 |
+
top: "detection_out"
|
1774 |
+
include {
|
1775 |
+
phase: TEST
|
1776 |
+
}
|
1777 |
+
detection_output_param {
|
1778 |
+
num_classes: 2
|
1779 |
+
share_location: true
|
1780 |
+
background_label_id: 0
|
1781 |
+
nms_param {
|
1782 |
+
nms_threshold: 0.45
|
1783 |
+
top_k: 400
|
1784 |
+
}
|
1785 |
+
code_type: CENTER_SIZE
|
1786 |
+
keep_top_k: 200
|
1787 |
+
confidence_threshold: 0.01
|
1788 |
+
}
|
1789 |
+
}
|
face_detector/res10_300x300_ssd_iter_140000.caffemodel
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2a56a11a57a4a295956b0660b4a3d76bbdca2206c4961cea8efe7d95c7cb2f2d
|
3 |
+
size 10666211
|