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lines changed Original file line number Diff line number Diff line change 10104 . 训练模型
11115 . 计算` mAP `
1212
13- ` 50 ` 轮训练完成后能够实现` 97.31% ` 的` mAP `
13+ 对于` Location DataSet ` , ` 50 ` 轮训练完成后能够实现` 97.31% mAP `
14+
15+ 对于` VOC 07 ` ,` 50 ` 轮训练完成后能够实现` xxx mAP ` 以及` xxx FPS `
1416
1517## 相关链接
1618
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2- # 日志
2+ # 定位数据集训练日志
33
44## 训练参数
55
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2+ # 07 VOC训练日志
3+
4+ ## 训练参数
5+
6+ * ` S=7, B=2, C=3 `
7+ * 缩放至` (448, 448) ` ,进行数据标准化处理
8+ * 优化器:` SGD ` ,学习率` 1e-3 ` ,动量大小` 0.9 `
9+ * 衰减器:每隔` 4 ` 轮衰减` 4% ` ,学习因子` 0.96 `
10+
11+ ## 检测结果
12+
13+ ```
14+ 。。。
15+ ```
16+
17+ ## 训练日志
18+
19+ ```
20+ $ python train.py
21+ Epoch 0/49
22+ ----------
23+ train Loss: 9.3544
24+ save model
25+
26+ Epoch 1/49
27+ ----------
28+ train Loss: 7.4459
29+ save model
30+
31+ Epoch 2/49
32+ ----------
33+ train Loss: 7.1511
34+ save model
35+
36+ Epoch 3/49
37+ ----------
38+ train Loss: 6.9450
39+ save model
40+
41+ Epoch 4/49
42+ ----------
43+ train Loss: 6.7656
44+ save model
45+
46+ Epoch 5/49
47+ ----------
48+ train Loss: 6.6077
49+ save model
50+
51+ Epoch 6/49
52+ ----------
53+ train Loss: 6.4264
54+ save model
55+
56+ Epoch 7/49
57+ ----------
58+ train Loss: 6.2750
59+ save model
60+
61+ Epoch 8/49
62+ ----------
63+ train Loss: 6.0318
64+ save model
65+
66+ Epoch 9/49
67+ ----------
68+ train Loss: 5.7777
69+ save model
70+
71+ Epoch 10/49
72+ ----------
73+ train Loss: 5.4760
74+ save model
75+
76+ Epoch 11/49
77+ ----------
78+ train Loss: 5.1784
79+ save model
80+
81+ Epoch 12/49
82+ ----------
83+ train Loss: 4.8067
84+ save model
85+
86+ Epoch 13/49
87+ ----------
88+ train Loss: 4.4603
89+ save model
90+
91+ Epoch 14/49
92+ ----------
93+ train Loss: 4.1291
94+ save model
95+
96+ Epoch 15/49
97+ ----------
98+ train Loss: 3.7810
99+ save model
100+
101+ Epoch 16/49
102+ ----------
103+ train Loss: 3.4259
104+ save model
105+
106+ Epoch 17/49
107+ ----------
108+ train Loss: 3.1166
109+ save model
110+
111+ Epoch 18/49
112+ ----------
113+ train Loss: 2.8041
114+ save model
115+
116+ Epoch 19/49
117+ ----------
118+ train Loss: 2.4853
119+ save model
120+
121+ Epoch 20/49
122+ ----------
123+ train Loss: 2.2107
124+ save model
125+
126+ Epoch 21/49
127+ ----------
128+ train Loss: 1.9424
129+ save model
130+
131+ Epoch 22/49
132+ ----------
133+ train Loss: 1.7307
134+ save model
135+
136+ Epoch 23/49
137+ ----------
138+ train Loss: 1.5518
139+ save model
140+
141+ Epoch 24/49
142+ ----------
143+ train Loss: 1.3712
144+ save model
145+
146+ Epoch 25/49
147+ ----------
148+ train Loss: 1.2174
149+ save model
150+
151+ Epoch 26/49
152+ ----------
153+ train Loss: 1.1142
154+ save model
155+
156+ Epoch 27/49
157+ ----------
158+ train Loss: 1.0305
159+ save model
160+
161+ Epoch 28/49
162+ ----------
163+ train Loss: 0.9270
164+ save model
165+
166+ Epoch 29/49
167+ ----------
168+ train Loss: 0.8465
169+ save model
170+
171+ Epoch 30/49
172+ ----------
173+ train Loss: 0.7739
174+ save model
175+
176+ Epoch 31/49
177+ ----------
178+ train Loss: 0.7228
179+ save model
180+
181+ Epoch 32/49
182+ ----------
183+ train Loss: 0.6765
184+ save model
185+
186+ Epoch 33/49
187+ ----------
188+ train Loss: 0.6264
189+ save model
190+
191+ Epoch 34/49
192+ ----------
193+ train Loss: 0.5932
194+ save model
195+
196+ Epoch 35/49
197+ ----------
198+ train Loss: 0.5646
199+ save model
200+
201+ Epoch 36/49
202+ ----------
203+ train Loss: 0.5359
204+ save model
205+
206+ Epoch 37/49
207+ ----------
208+ train Loss: 0.4941
209+ save model
210+
211+ Epoch 38/49
212+ ----------
213+ train Loss: 0.4682
214+ save model
215+
216+ Epoch 39/49
217+ ----------
218+ train Loss: 0.4482
219+ save model
220+
221+ Epoch 40/49
222+ ----------
223+ train Loss: 0.4276
224+ save model
225+
226+ Epoch 41/49
227+ ----------
228+ train Loss: 0.4048
229+ save model
230+
231+ Epoch 42/49
232+ ----------
233+ train Loss: 0.3874
234+ save model
235+
236+ Epoch 43/49
237+ ----------
238+ train Loss: 0.3759
239+ save model
240+
241+ Epoch 44/49
242+ ----------
243+ train Loss: 0.3607
244+ save model
245+
246+ Epoch 45/49
247+ ----------
248+ train Loss: 0.3415
249+ save model
250+
251+ Epoch 46/49
252+ ----------
253+ train Loss: 0.3327
254+ save model
255+
256+ Epoch 47/49
257+ ----------
258+ train Loss: 0.3222
259+ save model
260+
261+ Epoch 48/49
262+ ----------
263+ train Loss: 0.3141
264+ save model
265+
266+ Epoch 49/49
267+ ----------
268+ train Loss: 0.2978
269+ save model
270+
271+ Training complete in 214m 30s
272+ ```
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22# 数据集
33
4- 当前使用` 3 ` 类定位数据集,参考[[ 数据集] Image Localization Dataset] ( https://blog.zhujian.life/posts/a2d65e1.html )
4+ 当前使用了两种数据集进行测试:
5+
6+ * 定位数据集(` 3 ` 类)
7+ * ` VOC 07 ` 数据集(` 20 ` 类)
8+
9+ ## 定位数据集
10+
11+ 参考[[ 数据集] Image Localization Dataset] ( https://blog.zhujian.life/posts/a2d65e1.html )
512
613```
714{'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
815```
916
10- ## 解析
17+ ### 解析
1118
1219下载数据集后,解压到` py/data ` 目录,得到` training_images ` ,其格式如下:
1320
@@ -43,4 +50,8 @@ $ python parse_location.py
4350 ├── cucumber_11.jpg
4451。。。
4552。。。
46- ```
53+ ```
54+
55+ ## VOC 07
56+
57+ 。。。
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