带标注的交通工具分类数据集，17334张原始图片，识别率92.4%，可识别汽车，公共汽车，自行车，摩托车，支持yolo，coco json，pascal voc xml格式

带标注的交通工具分类数据集17334张原始图片识别率92.4%可识别汽车公共汽车自行车摩托车支持yolococo jsonpascal voc xml格式模型训练指标参数标签car bus bicycle motorcycle数据集构成和参数训练集 17334 张图验证集1321 张图测试集720 张图预处理自动方向校正已应用尺寸调整拉伸至 640×640数据增强每个训练样本的输出数量390° 旋转顺时针、逆时针剪切变换水平 ±10°、垂直 ±10°亮度-25% 25%曝光度-15% 15%模糊最大 1.4 像素数据集图片和标注信息模型训练#如果存在版本兼容问题使用:pip install --upgrade ultralytics from ultralytics import YOLO # 加载预训练的 YOLOv8 模型 model YOLO(yolov8n.pt) # 训练模型 results model.train( datadata.yaml, # 数据集的配置文件路径 epochs100, # 训练的轮数 imgsz640 # 输入图像的大小 ) # 评估模型 metrics model.val()模型测试#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(trained_yolov8n.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 path/to/your/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(YOLOv8 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: 383.5,y: 320.5,width: 117,height: 151,confidence: 0.942,class: car,class_id: 2,detection_id: 6079c802-fb51-48ac-a3ba-daff12410c70},{x: 303.5,y: 208.5,width: 85,height: 77,confidence: 0.927,class: car,class_id: 2,detection_id: af921b00-c284-4d69-87a7-5f6219c5e21f},{x: 184,y: 367.5,width: 44,height: 111,confidence: 0.891,class: motorcycle,class_id: 3,detection_id: 354f067d-b57a-45dc-899b-ccc1b018a709},{x: 239,y: 437,width: 48,height: 86,confidence: 0.887,class: motorcycle,class_id: 3,detection_id: 1ba8c8a5-fda4-43db-9820-3b543bad6abc},{x: 325,y: 160,width: 46,height: 42,confidence: 0.849,class: car,class_id: 2,detection_id: 4637dd72-87a1-4ae4-a798-72a9b6d42840},{x: 240,y: 175.5,width: 24,height: 57,confidence: 0.849,class: motorcycle,class_id: 3,detection_id: eda15d42-b3e9-4f68-bf4a-9f345c58f780},{x: 103.5,y: 271.5,width: 43,height: 73,confidence: 0.843,class: motorcycle,class_id: 3,detection_id: 47b15d81-1bb8-4ec3-bf6b-733b4b75e3b0},{x: 209.5,y: 136.5,width: 43,height: 39,confidence: 0.835,class: car,class_id: 2,detection_id: 5a0cf508-3754-4713-95f8-38f107ae46aa},{x: 477.5,y: 217,width: 33,height: 64,confidence: 0.833,class: motorcycle,class_id: 3,detection_id: fb1b0d08-a415-47d6-916e-e5ec7fb978e3},{x: 155.5,y: 178,width: 21,height: 50,confidence: 0.824,class: motorcycle,class_id: 3,detection_id: 1b35b25d-81f2-4411-b8e3-1259463c7d53},{x: 375.5,y: 224,width: 23,height: 62,confidence: 0.807,class: motorcycle,class_id: 3,detection_id: 75891512-9190-4106-8162-335810658fc7},{x: 129,y: 174.5,width: 20,height: 49,confidence: 0.759,class: motorcycle,class_id: 3,detection_id: a6f495ba-9f86-4c5e-a17b-662b8f405a0c},{x: 8.5,y: 397.5,width: 17,height: 139,confidence: 0.628,class: car,class_id: 2,detection_id: 75e47a66-4741-4d74-9e79-e16665791503},{x: 383.5,y: 133,width: 13,height: 34,confidence: 0.523,class: motorcycle,class_id: 3,detection_id: ae2e46ea-8493-4377-bdad-7ca902ab8bf7}]}数据集下载地址所有yolococo jsonpascal voc xml格式的数据集下载地址可在这里https://download.csdn.net/download/pbymw8iwm/92757808