带标注的房屋平面设计图数据集，可识别平面图中的墙，窗，门数据集，识别率85.9%，5015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：5015 张图数
训练集

4215 张图

验证集

400 张图

测试集

400 张图

预处理

自动定向：应用

调整大小：拉伸到1200x1200

增强

无

数据集标签：

[‘door’, ‘window’, ‘wall’]

标签解释 门，窗，墙

数据集图片和标注信息示例：

数据集下载：

yolo26: https://download.csdn.net/download/pbymw8iwm/92842421

yolo v12: https://download.csdn.net/download/pbymw8iwm/92842443

yolo v11:https://download.csdn.net/download/pbymw8iwm/92843751

yolo v9:https://download.csdn.net/download/pbymw8iwm/92843754

yolo v8:https://download.csdn.net/download/pbymw8iwm/92843749

yolo v7:https://download.csdn.net/download/pbymw8iwm/92843745

coco json:https://download.csdn.net/download/pbymw8iwm/92843747

pascal voc xml:https://download.csdn.net/download/pbymw8iwm/92843742

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
​​

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()

验证测试

推理结果：

{
“predictions”: [
{
“x”: 171.5,
“y”: 348.5,
“width”: 37,
“height”: 9,
“confidence”: 0.832,
“class”: “door”,
“class_id”: 0,
“detection_id”: “9ef9c283-e534-4aa7-87f9-77a15c54ff6c”
},
{
“x”: 315,
“y”: 379.5,
“width”: 10,
“height”: 31,
“confidence”: 0.831,
“class”: “door”,
“class_id”: 0,
“detection_id”: “9b1bad78-7e80-4257-8117-7bd267cc6191”
},
{
“x”: 488,
“y”: 575,
“width”: 80,
“height”: 10,
“confidence”: 0.829,
“class”: “window”,
“class_id”: 2,
“detection_id”: “baf4601a-acfa-4d28-bf96-4a269d5e8a65”
},
{
“x”: 308,
“y”: 576,
“width”: 82,
“height”: 10,
“confidence”: 0.823,
“class”: “window”,
“class_id”: 2,
“detection_id”: “595e144a-85ff-485d-ae0f-f6587fae68ba”
},
{
“x”: 501.5,
“y”: 110.5,
“width”: 103,
“height”: 9,
“confidence”: 0.815,
“class”: “window”,
“class_id”: 2,
“detection_id”: “56265d64-088c-4d9d-adba-e43ebe8a3038”
},
{
“x”: 159,
“y”: 110.5,
“width”: 102,
“height”: 9,
“confidence”: 0.814,
“class”: “window”,
“class_id”: 2,
“detection_id”: “0ad3a71f-aca0-4244-b727-06d32860ba87”
},
{
“x”: 225.5,
“y”: 503,
“width”: 11,
“height”: 152,
“confidence”: 0.813,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “de57efc9-9495-4322-9de2-2f4248919bdc”
},
{
“x”: 423,
“y”: 575.5,
“width”: 30,
“height”: 11,
“confidence”: 0.806,
“class”: “window”,
“class_id”: 2,
“detection_id”: “5784ba83-3693-4ce6-9938-9b89b70aca57”
},
{
“x”: 300,
“y”: 110.5,
“width”: 102,
“height”: 9,
“confidence”: 0.805,
“class”: “window”,
“class_id”: 2,
“detection_id”: “942b54f9-1b1a-4263-b998-b132e6e84646”
},
{
“x”: 269.5,
“y”: 431,
“width”: 99,
“height”: 10,
“confidence”: 0.805,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “7b139243-3d74-4145-bbeb-a360c007523f”
},
{
“x”: 428.5,
“y”: 110.5,
“width”: 33,
“height”: 9,
“confidence”: 0.805,
“class”: “door”,
“class_id”: 0,
“detection_id”: “3aa6a2c8-44a7-47c7-933e-0716d8deaa3a”
},
{
“x”: 328,
“y”: 110.5,
“width”: 502,
“height”: 9,
“confidence”: 0.793,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “d5009ac5-2e12-4995-913f-80946da00f30”
},
{
“x”: 577,
“y”: 342.5,
“width”: 12,
“height”: 473,
“confidence”: 0.788,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “4204cfce-febc-4d6c-a458-c8c0719f8520”
},
{
“x”: 314.5,
“y”: 389,
“width”: 11,
“height”: 90,
“confidence”: 0.783,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “c39edcff-c525-4ff4-a15b-9f5b51da4eb6”
},
{
“x”: 174,
“y”: 287.5,
“width”: 38,
“height”: 5,
“confidence”: 0.763,
“class”: “door”,
“class_id”: 0,
“detection_id”: “496c8805-39bd-4d7f-ba1d-ef7adb3efd78”
},
{
“x”: 187.5,
“y”: 347.5,
“width”: 263,
“height”: 9,
“confidence”: 0.745,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “dc0b924f-461c-4fd0-b5fa-69092b12d142”
},
{
“x”: 59.5,
“y”: 228.5,
“width”: 11,
“height”: 245,
“confidence”: 0.737,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “c402391a-bcd7-4a50-ba8c-ca0172914412”
},
{
“x”: 141,
“y”: 304.5,
“width”: 6,
“height”: 25,
“confidence”: 0.734,
“class”: “door”,
“class_id”: 0,
“detection_id”: “5b6aebbf-d2ed-462c-8950-aa1bfd74aaea”
},
{
“x”: 443,
“y”: 461,
“width”: 6,
“height”: 234,
“confidence”: 0.73,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “c4fb0140-1ae5-482b-9693-541853c70156”
},
{
“x”: 337,
“y”: 287.5,
“width”: 38,
“height”: 5,
“confidence”: 0.725,
“class”: “door”,
“class_id”: 0,
“detection_id”: “5e838f23-ac06-43e0-87db-a27378fde29d”
},
{
“x”: 494.5,
“y”: 399.5,
“width”: 33,
“height”: 5,
“confidence”: 0.721,
“class”: “door”,
“class_id”: 0,
“detection_id”: “45e5b598-3739-4d3c-9e77-d28dca96f4a3”
},
{
“x”: 444,
“y”: 418,
“width”: 6,
“height”: 28,
“confidence”: 0.71,
“class”: “door”,
“class_id”: 0,
“detection_id”: “85b5c5cf-e3b0-41e7-9148-ac5cbd8e7b27”
},
{
“x”: 511.5,
“y”: 398.5,
“width”: 141,
“height”: 5,
“confidence”: 0.698,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “20a293ea-8da0-439f-899d-e0cc8fd90107”
},
{
“x”: 419.5,
“y”: 433.5,
“width”: 33,
“height”: 5,
“confidence”: 0.675,
“class”: “door”,
“class_id”: 0,
“detection_id”: “dd98be13-01bf-4bc3-b761-f55c2c173c3e”
},
{
“x”: 226.5,
“y”: 197.5,
“width”: 5,
“height”: 183,
“confidence”: 0.659,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “eee72c6c-d13f-42ea-bc5c-113350451aa5”
},
{
“x”: 405,
“y”: 433,
“width”: 82,
“height”: 4,
“confidence”: 0.653,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “56b7b565-3279-4236-9048-4a6a40c9a66d”
},
{
“x”: 140.5,
“y”: 318,
“width”: 5,
“height”: 66,
“confidence”: 0.642,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “8fc477b5-aeee-4422-a638-1b7d0a5728a6”
},
{
“x”: 366.5,
“y”: 504,
“width”: 5,
“height”: 146,
“confidence”: 0.622,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “4e9bc216-55e0-475a-839e-321646e361bd”
},
{
“x”: 580.5,
“y”: 79,
“width”: 5,
“height”: 56,
“confidence”: 0.605,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “819a7a24-c21b-4db2-9ab5-c853de71e902”
},
{
“x”: 491.5,
“y”: 345.5,
“width”: 181,
“height”: 5,
“confidence”: 0.604,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “138e1e37-1f38-4ea1-b69f-f84cf5d146c8”
},
{
“x”: 408.5,
“y”: 575,
“width”: 253,
“height”: 10,
“confidence”: 0.588,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “c1736dcc-b888-4e6a-865a-8afb1c074a47”
},
{
“x”: 392.5,
“y”: 78,
“width”: 5,
“height”: 56,
“confidence”: 0.583,
“class”: “wall”,
“class_id”: 1,
“detection_id”: “fd2aca0b-829b-49a8-9d0c-5ecbf94f4c24”
}
]
}