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lines changed Original file line number Diff line number Diff line change 1- # YOLO_v1
1+ # YOLO_v1
2+
3+ 实现` YOLO_v1 ` 算法
4+
5+ ## 数据集
6+
7+ 使用` 3 ` 类定位数据集,参考[[ 数据集] Image Localization Dataset] ( https://blog.zhujian.life/posts/a2d65e1.html )
8+
9+ ```
10+ {'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
11+ ```
12+
13+ ## 实现流程
14+
15+ 1 . 创建训练数据集
16+ 2 . 定义` YoLo_v1 ` 模型
17+ 3 . 定义损失函数` Multi-part Loss `
18+ 4 . 训练模型
19+ 5 . 计算` mAP `
Original file line number Diff line number Diff line change 1+
2+ # 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+ ## 训练日志
12+
13+ ```
14+ $ python train.py
15+ Epoch 0/49
16+ ----------
17+ train Loss: 4.3550
18+ save model
19+
20+ Epoch 1/49
21+ ----------
22+ train Loss: 3.4803
23+ save model
24+
25+ Epoch 2/49
26+ ----------
27+ train Loss: 3.3921
28+ save model
29+
30+ Epoch 3/49
31+ ----------
32+ train Loss: 3.0650
33+ save model
34+
35+ Epoch 4/49
36+ ----------
37+ train Loss: 2.9081
38+ save model
39+
40+ Epoch 5/49
41+ ----------
42+ train Loss: 2.5893
43+ save model
44+
45+ Epoch 6/49
46+ ----------
47+ train Loss: 2.5640
48+ save model
49+
50+ Epoch 7/49
51+ ----------
52+ train Loss: 2.4905
53+ save model
54+
55+ Epoch 8/49
56+ ----------
57+ train Loss: 2.1913
58+ save model
59+
60+ Epoch 9/49
61+ ----------
62+ train Loss: 2.1152
63+ save model
64+
65+ Epoch 10/49
66+ ----------
67+ train Loss: 1.9428
68+ save model
69+
70+ Epoch 11/49
71+ ----------
72+ train Loss: 1.7271
73+ save model
74+
75+ Epoch 12/49
76+ ----------
77+ train Loss: 1.4372
78+ save model
79+
80+ Epoch 13/49
81+ ----------
82+ train Loss: 1.8185
83+
84+ Epoch 14/49
85+ ----------
86+ train Loss: 1.5460
87+
88+ Epoch 15/49
89+ ----------
90+ train Loss: 1.1692
91+ save model
92+
93+ Epoch 16/49
94+ ----------
95+ train Loss: 1.0540
96+ save model
97+
98+ Epoch 17/49
99+ ----------
100+ train Loss: 0.9028
101+ save model
102+
103+ Epoch 18/49
104+ ----------
105+ train Loss: 0.7702
106+ save model
107+
108+ Epoch 19/49
109+ ----------
110+ train Loss: 0.7176
111+ save model
112+
113+ Epoch 20/49
114+ ----------
115+ train Loss: 0.7485
116+
117+ Epoch 21/49
118+ ----------
119+ train Loss: 0.6307
120+ save model
121+
122+ Epoch 22/49
123+ ----------
124+ train Loss: 0.5581
125+ save model
126+
127+ Epoch 23/49
128+ ----------
129+ train Loss: 0.5320
130+ save model
131+
132+ Epoch 24/49
133+ ----------
134+ train Loss: 0.5893
135+
136+ Epoch 25/49
137+ ----------
138+ train Loss: 0.5185
139+ save model
140+
141+ Epoch 26/49
142+ ----------
143+ train Loss: 0.6156
144+
145+ Epoch 27/49
146+ ----------
147+ train Loss: 0.5096
148+ save model
149+
150+ Epoch 28/49
151+ ----------
152+ train Loss: 0.5403
153+
154+ Epoch 29/49
155+ ----------
156+ train Loss: 0.4653
157+ save model
158+
159+ Epoch 30/49
160+ ----------
161+ train Loss: 0.3850
162+ save model
163+
164+ Epoch 31/49
165+ ----------
166+ train Loss: 0.3609
167+ save model
168+
169+ Epoch 32/49
170+ ----------
171+ train Loss: 0.4063
172+
173+ Epoch 33/49
174+ ----------
175+ train Loss: 0.3349
176+ save model
177+
178+ Epoch 34/49
179+ ----------
180+ train Loss: 0.2629
181+ save model
182+
183+ Epoch 35/49
184+ ----------
185+ train Loss: 0.3319
186+
187+ Epoch 36/49
188+ ----------
189+ train Loss: 0.2790
190+
191+ Epoch 37/49
192+ ----------
193+ train Loss: 0.2487
194+ save model
195+
196+ Epoch 38/49
197+ ----------
198+ train Loss: 0.2325
199+ save model
200+
201+ Epoch 39/49
202+ ----------
203+ train Loss: 0.2146
204+ save model
205+
206+ Epoch 40/49
207+ ----------
208+ train Loss: 0.2087
209+ save model
210+
211+ Epoch 41/49
212+ ----------
213+ train Loss: 0.1626
214+ save model
215+
216+ Epoch 42/49
217+ ----------
218+ train Loss: 0.1446
219+ save model
220+
221+ Epoch 43/49
222+ ----------
223+ train Loss: 0.1372
224+ save model
225+
226+ Epoch 44/49
227+ ----------
228+ train Loss: 0.1260
229+ save model
230+
231+ Epoch 45/49
232+ ----------
233+ train Loss: 0.1231
234+ save model
235+
236+ Epoch 46/49
237+ ----------
238+ train Loss: 0.1232
239+
240+ Epoch 47/49
241+ ----------
242+ train Loss: 0.1475
243+
244+ Epoch 48/49
245+ ----------
246+ train Loss: 0.1226
247+ save model
248+
249+ Epoch 49/49
250+ ----------
251+ train Loss: 0.1000
252+ save model
253+
254+ Training complete in 6m 28s
255+ ```
256+
257+ ## 检测结果
258+
259+ ```
260+ compute mAP
261+ {'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
262+ 99.43% = cucumber AP
263+ 98.39% = eggplant AP
264+ 99.22% = mushroom AP
265+ mAP = 99.01%
266+ ```
Original file line number Diff line number Diff line change 1+
2+ # 架构解析
3+
4+ 使用` Python ` 实现,相关源码位于` py ` 目录下
5+
6+ ```
7+ ├── batch_detect.py
8+ ├── data
9+ ├── detector.py
10+ ├── lib
11+ ├── data
12+ │ ├── __init__.py
13+ │ └── parse_location.py
14+ ├── __init__.py
15+ ├── models
16+ │ ├── basic_conv2d.py
17+ │ ├── __init__.py
18+ │ ├── location_dataset.py
19+ │ ├── multi_part_loss.py
20+ │ ├── __pycache__
21+ │ └── yolo_v1.py
22+ ├── __pycache__
23+ ├── train.py
24+ └── utils
25+ ├── draw.py
26+ ├── file.py
27+ ├── __init__.py
28+ ├── __pycache__
29+ ├── util.py
30+ └── voc_map.py
31+ └── models
32+ ```
33+
34+ * 数据集操作:` lib/data/parse_location.py `
35+ * 模型定义:` lib/models `
36+ * 训练文件:` lib/train.py `
37+ * 批量测试:` batch_detect.py `
Original file line number Diff line number Diff line change @@ -29,4 +29,6 @@ extra_javascript:
2929 - ' https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
3030# 导航
3131nav :
32- - Home : index.md
32+ - Home : index.md
33+ - 架构解析 : ' 架构解析.md'
34+ - log : log.md
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