搭建训练的环境参考yolo的官网:GitHub – ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
获取工程可以从yolo的官方网站获取或者直接从跟本人的网盘获取（非免费介意误下载）：无需配置训练的数据解和下载数据，搭建好训练环境后就可以使用本工程直接训练交通标示的检测：链接: https://pan.baidu.com/s/1koS7b5-i2B4cNV1LeYarzw 提取码: w6gb
( 内部包含修改后的工程和训练用的数据集：训练9170样本测试1097样本，训练好的模型可以直接使用哦,当前训练时间较短部分检测效果不是很好可以在当前模型上继续训练 ）
当前支持的类别
准备好数据后配置文件coco.yaml
# class names
names: ['i1', 'i10', 'i11', 'i12', 'i13', 'i14', 'i15', 'i2', 'i3', 'i4', 'i5', 'il100', 'il110', 'il50', 'il60', 'il70', 'il80', 'il90', 'io', 'ip', 'p1', 'p10', 'p11', 'p12', 'p13', 'p14', 'p15', 'p16', 'p17', 'p18', 'p19', 'p2', 'p20', 'p21', 'p22', 'p23', 'p24', 'p25', 'p26', 'p27', 'p28', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'pa10', 'pa12', 'pa13', 'pa14', 'pa8', 'pb', 'pc', 'pg', 'ph1.5', 'ph2', 'ph2.1', 'ph2.2', 'ph2.4', 'ph2.5', 'ph2.8', 'ph2.9', 'ph3', 'ph3.2', 'ph3.5', 'ph3.8', 'ph4', 'ph4.2', 'ph4.3', 'ph4.5', 'ph4.8', 'ph5', 'ph5.3', 'ph5.5', 'pl10', 'pl100', 'pl110', 'pl120', 'pl15', 'pl20', 'pl25', 'pl30', 'pl35', 'pl40', 'pl5', 'pl50', 'pl60', 'pl65', 'pl70', 'pl80', 'pl90', 'pm10', 'pm13', 'pm15', 'pm1.5', 'pm2', 'pm20', 'pm25', 'pm30', 'pm35', 'pm40', 'pm46', 'pm5', 'pm50', 'pm55', 'pm8', 'pn', 'pne', 'po', 'pr10', 'pr100', 'pr20', 'pr30', 'pr40', 'pr45', 'pr50', 'pr60', 'pr70', 'pr80', 'ps', 'pw2', 'pw2.5', 'pw3', 'pw3.2', 'pw3.5', 'pw4', 'pw4.2', 'pw4.5', 'w1', 'w10', 'w12', 'w13', 'w16', 'w18', 'w20', 'w21', 'w22', 'w24', 'w28', 'w3', 'w30', 'w31', 'w32', 'w34', 'w35', 'w37', 'w38', 'w41', 'w42', 'w43', 'w44', 'w45', 'w46', 'w47', 'w48', 'w49', 'w5', 'w50', 'w55', 'w56', 'w57', 'w58', 'w59', 'w60', 'w62', 'w63', 'w66', 'w8', 'wo', 'i6', 'i7', 'i8', 'i9', 'ilx', 'p29', 'w29', 'w33', 'w36', 'w39', 'w4', 'w40', 'w51', 'w52', 'w53', 'w54', 'w6', 'w61', 'w64', 'w65', 'w67', 'w7', 'w9', 'pax', 'pd', 'pe', 'phx', 'plx', 'pmx', 'pnl', 'prx', 'pwx', 'w11', 'w14', 'w15', 'w17', 'w19', 'w2', 'w23', 'w25', 'w26', 'w27', 'pl0', 'pl4', 'pl3', 'pm2.5', 'ph4.4', 'pn40', 'ph3.3', 'ph2.6']
# number of classes
nc: 221
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: classself/coco/train2017.txt # 118287 images
val: classself/coco/val2017.txt # 5000 images
test: classself/coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
准备好数据后配置文件yolov5s_self.yaml
path: classself/coco
train: train2017.txt
val: val2017.txt
names:
0: i1
1: i10
2: i11
3: i12
4: i13
5: i14
6: i15
7: i2
8: i3
9: i4
10: i5
11: il100
12: il110
13: il50
14: il60
15: il70
16: il80
17: il90
18: io
19: ip
20: p1
21: p10
22: p11
23: p12
24: p13
25: p14
26: p15
27: p16
28: p17
29: p18
30: p19
31: p2
32: p20
33: p21
34: p22
35: p23
36: p24
37: p25
38: p26
39: p27
40: p28
41: p3
42: p4
43: p5
44: p6
45: p7
46: p8
47: p9
48: pa10
49: pa12
50: pa13
51: pa14
52: pa8
53: pb
54: pc
55: pg
56: ph1.5
57: ph2
58: ph2.1
59: ph2.2
60: ph2.4
61: ph2.5
62: ph2.8
63: ph2.9
64: ph3
65: ph3.2
66: ph3.5
67: ph3.8
68: ph4
69: ph4.2
70: ph4.3
71: ph4.5
72: ph4.8
73: ph5
74: ph5.3
75: ph5.5
76: pl10
77: pl100
78: pl110
79: pl120
80: pl15
81: pl20
82: pl25
83: pl30
84: pl35
85: pl40
86: pl5
87: pl50
88: pl60
89: pl65
90: pl70
91: pl80
92: pl90
93: pm10
94: pm13
95: pm15
96: pm1.5
97: pm2
98: pm20
99: pm25
100: pm30
101: pm35
102: pm40
103: pm46
104: pm5
105: pm50
106: pm55
107: pm8
108: pn
109: pne
110: po
111: pr10
112: pr100
113: pr20
114: pr30
115: pr40
116: pr45
117: pr50
118: pr60
119: pr70
120: pr80
121: ps
122: pw2
123: pw2.5
124: pw3
125: pw3.2
126: pw3.5
127: pw4
128: pw4.2
129: pw4.5
130: w1
131: w10
132: w12
133: w13
134: w16
135: w18
136: w20
137: w21
138: w22
139: w24
140: w28
141: w3
142: w30
143: w31
144: w32
145: w34
146: w35
147: w37
148: w38
149: w41
150: w42
151: w43
152: w44
153: w45
154: w46
155: w47
156: w48
157: w49
158: w5
159: w50
160: w55
161: w56
162: w57
163: w58
164: w59
165: w60
166: w62
167: w63
168: w66
169: w8
170: wo
171: i6
172: i7
173: i8
174: i9
175: ilx
176: p29
177: w29
178: w33
179: w36
180: w39
181: w4
182: w40
183: w51
184: w52
185: w53
186: w54
187: w6
188: w61
189: w64
190: w65
191: w67
192: w7
193: w9
194: pax
195: pd
196: pe
197: phx
198: plx
199: pmx
200: pnl
201: prx
202: pwx
203: w11
204: w14
205: w15
206: w17
207: w19
208: w2
209: w23
210: w25
211: w26
212: w27
213: pl0
214: pl4
215: pl3
216: pm2.5
217: ph4.4
218: pn40
219: ph3.3
220: ph2.6
开始训练
#训练
python train.py --data classself/coco/coco.yaml --weights '' --cfg models/yolov5s_self.yaml --img 640 --workers 0 --device 0
#断点续训
python train.py --resume runs/exp/weights/last.pt
python train.py --resume训练模型保存路径
测试
python detect.py --source data/images --weights runs/train/exp2/weights/best.pt测试结果路径
测试结果：
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