注册

基于yolov5的交通标识检测的训练(包含样本:训练9170样本测试1097样本,检测类别221类)

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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