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带标注的交通标识数据集，可识别汽车，公交，自行车，摩托，人，识别率94.6%，6523张图，支持yolo，coco json，voc xml,文末有模型训练代码

模型训练指标参数：

模型训练图：

数据集拆分

总图数：6523 张图数
训练集

5712 张图

验证集

541 张图

测试集

270 张图

预处理

自动定向：应用

调整大小：拉伸到640x640

增强

无

数据集标签：

[‘motorbike’, ‘bus’, ‘bicycle’, ‘car’, ‘person’]

标签解释
car —— 小汽车；轿车
bus —— 公交车；巴士
bicycle —— 自行车
motorbike —— 摩托车
person —— 人；人员

数据集图片和标注信息示例：

数据集下载：

yolo26: https://download.csdn.net/download/pbymw8iwm/92903060

yolo v12:https://download.csdn.net/download/pbymw8iwm/92903043

yolo v11:https://download.csdn.net/download/pbymw8iwm/92903046

yolo v9:https://download.csdn.net/download/pbymw8iwm/92903047

yolo v8: https://download.csdn.net/download/pbymw8iwm/92903052

yolo v7: https://download.csdn.net/download/pbymw8iwm/92903057

coco json: https://download.csdn.net/download/pbymw8iwm/92903067

pascal voc xml: https://download.csdn.net/download/pbymw8iwm/92903065

YOLO模型训练

下载数据集之后解压到当前文件夹，然后将 我的仓库 https://gitcode.com/pbymw8iwm/YOLOProject里的训练模型脚本复制到文件夹下，假设你使用的是yolov8来训练你就用 python train_yolov8.py
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模型验证测试情况：

验证测试代码：

#需要安装pip install ultralytics
from ultralytics import YOLO
import cv2
 
# 加载训练好的 YOLO .pt 模型
model = YOLO('best.pt')  # 替换为你实际的 .pt 模型文件路径
 
# 定义要测试的图片路径
image_path = './image.jpg'  # 替换为你实际的图片文件路径
 
# 使用模型对图片进行预测
results = model(image_path)
 
# 获取预测结果
for result in results:
    # 获取绘制了检测框的图片
    annotated_image = result.plot()
 
    # 显示图片
    cv2.imshow("YOLOv Inference", annotated_image)
 
    # 等待按键退出
    cv2.waitKey(0)
 
    # 关闭所有 OpenCV 窗口
    cv2.destroyAllWindows()

推理结果：

{
“predictions”: [
{
“x”: 572,
“y”: 281.5,
“width”: 136,
“height”: 157,
“confidence”: 0.94,
“class”: “person”,
“class_id”: 4,
“detection_id”: “4b2f1d34-b470-4587-8c7c-8a0b961e7168”
},
{
“x”: 497.5,
“y”: 175,
“width”: 41,
“height”: 56,
“confidence”: 0.896,
“class”: “car”,
“class_id”: 2,
“detection_id”: “5c6b66f1-48f7-4427-8de3-be9131645f34”
},
{
“x”: 406,
“y”: 131,
“width”: 26,
“height”: 44,
“confidence”: 0.886,
“class”: “car”,
“class_id”: 2,
“detection_id”: “80e9d748-8e61-4fd7-adc8-371231738ea8”
},
{
“x”: 369,
“y”: 129.5,
“width”: 26,
“height”: 43,
“confidence”: 0.885,
“class”: “car”,
“class_id”: 2,
“detection_id”: “00f62cec-07d9-4679-a425-ee3733418106”
},
{
“x”: 319,
“y”: 581,
“width”: 28,
“height”: 118,
“confidence”: 0.87,
“class”: “bicycle”,
“class_id”: 0,
“detection_id”: “91387656-4be2-4779-8a87-41fcdd63785c”
},
{
“x”: 408,
“y”: 176,
“width”: 14,
“height”: 48,
“confidence”: 0.854,
“class”: “motorbike”,
“class_id”: 3,
“detection_id”: “78002339-77a0-44be-bf0d-6abef0e66849”
},
{
“x”: 393.5,
“y”: 83.5,
“width”: 21,
“height”: 35,
“confidence”: 0.843,
“class”: “car”,
“class_id”: 2,
“detection_id”: “fe9d6d19-e17c-4e51-b683-d01de25379f8”
},
{
“x”: 344.5,
“y”: 554,
“width”: 25,
“height”: 108,
“confidence”: 0.83,
“class”: “bicycle”,
“class_id”: 0,
“detection_id”: “012f65cf-b37d-4548-a7b6-08856a3f1b98”
},
{
“x”: 382,
“y”: 105.5,
“width”: 22,
“height”: 35,
“confidence”: 0.82,
“class”: “car”,
“class_id”: 2,
“detection_id”: “686ddfef-5e08-46d6-b587-1e7f9d7e8c00”
},
{
“x”: 408,
“y”: 50.5,
“width”: 18,
“height”: 25,
“confidence”: 0.792,
“class”: “car”,
“class_id”: 2,
“detection_id”: “40e10bf4-4b10-4fba-9532-8940b8b636f0”
},
{
“x”: 419.5,
“y”: 80.5,
“width”: 17,
“height”: 27,
“confidence”: 0.784,
“class”: “car”,
“class_id”: 2,
“detection_id”: “3d433388-e066-401f-ad3f-0cdd2793c8e1”
},
{
“x”: 519,
“y”: 62,
“width”: 16,
“height”: 24,
“confidence”: 0.749,
“class”: “car”,
“class_id”: 2,
“detection_id”: “0c7ad7fc-8df8-4685-b5bf-709e15a606ef”
},
{
“x”: 432,
“y”: 51,
“width”: 18,
“height”: 30,
“confidence”: 0.726,
“class”: “car”,
“class_id”: 2,
“detection_id”: “dcaf0852-98f9-495f-b261-a29442fe3723”
}
]
}