带标注的家具识别数据集，可识别床，椅子，餐桌， 门，水槽，沙发，马桶，浴缸等，识别率87%，支持yolo，coco json，pascal voc xml格式的模型训练
模型训练指标参数：
模型训练图：
数据集拆分
训练集
3190图像
验证集
84图像
测试集
67图像
预处理
自动定向：应用
调整大小：拉伸到640×640
增强
每个训练样本的输出数量：5
90° 旋转：顺时针、逆时针
旋转角度：-15° 至 +15° 之间
灰度化：应用于 15% 的图像
模糊：最大 2.5 像素
噪声：最多 0.1% 的像素添加
噪声裁剪遮挡：3 个遮挡框，每个尺寸为 10%
数据集标签：
'achair', 'bathtub', 'bed', 'chair', 'ctable', 'door', 'dtable', 'sink', 'sofa', 'ssink', 'ssofa', 'toilet'
数据集图片和标注信息示例：
数据集下载：
yolo26:https://download.csdn.net/download/pbymw8iwm/92774769
yolo v12:https://download.csdn.net/download/pbymw8iwm/92774768
yolo v11:https://download.csdn.net/download/pbymw8iwm/92774766
yolo v9:https://download.csdn.net/download/pbymw8iwm/92774770
yolo v8:https://download.csdn.net/download/pbymw8iwm/92774765
yolo v7:https://download.csdn.net/download/pbymw8iwm/92774763
yolo v5:https://download.csdn.net/download/pbymw8iwm/92774767
yolo darknet:https://download.csdn.net/download/pbymw8iwm/92774771
coco json: https://download.csdn.net/download/pbymw8iwm/92774772
pascal voc xml:https://download.csdn.net/download/pbymw8iwm/92774764
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
注意，请根据你的GPU能力来适当调整训练参数，比如训练batch，patience，workers，以及模型类型（如果你的GPU硬件条件限制，可以联系作者进行付费模型训练，部分模型只需要一杯奶茶钱）
模型下载：
https://download.csdn.net/download/pbymw8iwm/92774837
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": 80.5, "y": 495, "width": 121, "height": 88, "confidence": 0.905, "class": "sofa", "class_id": 8, "detection_id": "8398ebd1-f2e2-45c8-857c-fbd32a5025f6" }, { "x": 148.5, "y": 205.5, "width": 51, "height": 87, "confidence": 0.905, "class": "ctable", "class_id": 4, "detection_id": "14974335-9c0e-43b6-9ff5-e7f6464c692f" }, { "x": 149.5, "y": 59.5, "width": 51, "height": 85, "confidence": 0.904, "class": "ctable", "class_id": 4, "detection_id": "15696dd0-b737-4030-9294-bb573ebbd913" }, { "x": 331, "y": 464.5, "width": 84, "height": 123, "confidence": 0.9, "class": "dtable", "class_id": 6, "detection_id": "23f9f47c-26a5-4283-bbb3-c26898326631" }, { "x": 507.5, "y": 362, "width": 89, "height": 70, "confidence": 0.9, "class": "bathtub", "class_id": 1, "detection_id": "c63a8c50-c5fe-42fb-9a9c-293b2c4d4cd3" }, { "x": 70, "y": 394.5, "width": 60, "height": 93, "confidence": 0.895, "class": "ctable", "class_id": 4, "detection_id": "01ce1cd4-1206-4550-bc9f-e51f6893ca40" }, { "x": 413, "y": 509.5, "width": 70, "height": 65, "confidence": 0.88, "class": "sink", "class_id": 7, "detection_id": "ab339152-dac7-4a86-8af3-c4dc7caa506b" }, { "x": 353, "y": 67, "width": 88, "height": 98, "confidence": 0.879, "class": "bed", "class_id": 2, "detection_id": "7f11e368-d765-48d8-8ab2-242afc98abc7" }, { "x": 282, "y": 300.5, "width": 60, "height": 129, "confidence": 0.877, "class": "door", "class_id": 5, "detection_id": "a289d9ea-e623-480f-81cc-99baa9d6733c" }, { "x": 428.5, "y": 395.5, "width": 69, "height": 113, "confidence": 0.875, "class": "door", "class_id": 5, "detection_id": "34ebf0f8-68ba-464e-9d0d-908ac165f0ad" }, { "x": 251, "y": 583.5, "width": 60, "height": 113, "confidence": 0.859, "class": "door", "class_id": 5, "detection_id": "f6e28cdc-e8e5-4f38-9336-1c8584e096eb" }, { "x": 595.5, "y": 430.5, "width": 49, "height": 67, "confidence": 0.807, "class": "toilet", "class_id": 11, "detection_id": "1c7f0b5a-6ad9-44f1-a69f-5f950af2fb35" }, { "x": 360.5, "y": 177.5, "width": 79, "height": 101, "confidence": 0.801, "class": "bed", "class_id": 2, "detection_id": "ca1a2bd2-c15e-480d-952f-dae5ecb239c5" } ] }
推理结果：
{ "predictions": [ { "x": 523, "y": 327.5, "width": 28, "height": 49, "confidence": 0.93, "class": "ctable", "class_id": 4, "detection_id": "146f4cb7-2a4b-40c6-b820-236a457da41e" }, { "x": 244.5, "y": 579.5, "width": 51, "height": 55, "confidence": 0.922, "class": "bed", "class_id": 2, "detection_id": "e60e12b1-d1f4-4fc8-b2a1-a489d979910c" }, { "x": 279.5, "y": 125.5, "width": 63, "height": 89, "confidence": 0.913, "class": "dtable", "class_id": 6, "detection_id": "d406b8ff-8f98-4d11-ab45-731ae1e4ae2f" }, { "x": 244.5, "y": 343.5, "width": 63, "height": 89, "confidence": 0.912, "class": "dtable", "class_id": 6, "detection_id": "afaed4b6-1237-4b7e-98f0-0caa5b60bf75" }, { "x": 597.5, "y": 453.5, "width": 35, "height": 87, "confidence": 0.91, "class": "bed", "class_id": 2, "detection_id": "bb0f5c3c-6473-4bcc-a9cb-0d073672862d" }, { "x": 437, "y": 453.5, "width": 36, "height": 87, "confidence": 0.908, "class": "bed", "class_id": 2, "detection_id": "dfba14f1-fa9a-4510-a4b3-55f9b349a05c" }, { "x": 525, "y": 203.5, "width": 24, "height": 85, "confidence": 0.908, "class": "bathtub", "class_id": 1, "detection_id": "c63c76ce-9abb-48fe-a17f-ba454494f0e4" }, { "x": 545, "y": 453, "width": 36, "height": 86, "confidence": 0.904, "class": "bed", "class_id": 2, "detection_id": "7634a0e6-fa93-4cf9-8796-bedc729b4c62" }, { "x": 523, "y": 68, "width": 26, "height": 88, "confidence": 0.903, "class": "bathtub", "class_id": 1, "detection_id": "dfd89ffd-1c6d-45f4-b04e-f744bda18002" }, { "x": 541.5, "y": 595.5, "width": 31, "height": 51, "confidence": 0.902, "class": "ctable", "class_id": 4, "detection_id": "3eb3ca0c-a30e-4656-ba22-388aaa55d5f5" }, { "x": 491.5, "y": 451.5, "width": 35, "height": 85, "confidence": 0.902, "class": "bed", "class_id": 2, "detection_id": "9bb9a427-6013-48ef-baf5-04cc2cfc3a01" }, { "x": 453.5, "y": 594.5, "width": 29, "height": 49, "confidence": 0.889, "class": "ctable", "class_id": 4, "detection_id": "372eedfe-7bd1-430a-a2e6-e103b21879f1" }, { "x": 124.5, "y": 119.5, "width": 27, "height": 51, "confidence": 0.884, "class": "ctable", "class_id": 4, "detection_id": "dddaa611-03d6-4ad4-8da8-8ac9205cb229" }, { "x": 288, "y": 251, "width": 38, "height": 66, "confidence": 0.882, "class": "door", "class_id": 5, "detection_id": "27db1168-baab-4a76-8173-4d32421475a2" }, { "x": 459.5, "y": 252, "width": 37, "height": 64, "confidence": 0.874, "class": "door", "class_id": 5, "detection_id": "d97d831b-d950-438c-8d7b-b9d3bcc1837d" }, { "x": 497.5, "y": 595, "width": 31, "height": 50, "confidence": 0.873, "class": "ctable", "class_id": 4, "detection_id": "6afe1d28-f4c9-4e8d-bd78-3d1bd6b12b11" }, { "x": 396.5, "y": 45.5, "width": 27, "height": 41, "confidence": 0.87, "class": "toilet", "class_id": 11, "detection_id": "087a58a2-72e9-41bd-93f7-25a6e0ee86dd" }, { "x": 189, "y": 414, "width": 44, "height": 68, "confidence": 0.869, "class": "door", "class_id": 5, "detection_id": "db083400-1f73-4bb3-8e60-4cb9362b431e" }, { "x": 395.5, "y": 239, "width": 27, "height": 40, "confidence": 0.865, "class": "toilet", "class_id": 11, "detection_id": "815d3e0c-3f27-4529-9510-ea7d4a7123b1" }, { "x": 318.5, "y": 582.5, "width": 31, "height": 51, "confidence": 0.865, "class": "chair", "class_id": 3, "detection_id": "ea4d5ee0-53f6-4086-88d4-94b20790b5ed" }, { "x": 159.5, "y": 115.5, "width": 31, "height": 113, "confidence": 0.859, "class": "sofa", "class_id": 8, "detection_id": "274ab05b-ae04-4123-a033-d1471e0f7aad" }, { "x": 119, "y": 248.5, "width": 40, "height": 67, "confidence": 0.855, "class": "door", "class_id": 5, "detection_id": "561f9eee-60b0-4631-a7c5-956469510388" }, { "x": 357, "y": 495.5, "width": 46, "height": 65, "confidence": 0.852, "class": "door", "class_id": 5, "detection_id": "43374764-b6d8-4d0c-a3a7-e72213dfae00" }, { "x": 593.5, "y": 598, "width": 35, "height": 56, "confidence": 0.85, "class": "ssofa", "class_id": 10, "detection_id": "d427e30f-a0a2-408d-a605-be3c2ca9cc26" }, { "x": 19.5, "y": 119, "width": 39, "height": 66, "confidence": 0.845, "class": "door", "class_id": 5, "detection_id": "e2b5b57c-7a3f-400c-8356-11f1dd769b7f" }, { "x": 80.5, "y": 115.5, "width": 33, "height": 113, "confidence": 0.835, "class": "sofa", "class_id": 8, "detection_id": "9b792622-1d2b-4c5e-88f0-130c4f61532b" }, { "x": 349, "y": 67, "width": 24, "height": 72, "confidence": 0.828, "class": "sink", "class_id": 7, "detection_id": "53822221-72fb-42cf-9af1-312aaea3ad44" }, { "x": 240, "y": 28.5, "width": 50, "height": 35, "confidence": 0.81, "class": "sink", "class_id": 7, "detection_id": "cd54f2b5-5be7-4eb7-9940-4a05be133027" }, { "x": 408, "y": 591, "width": 36, "height": 58, "confidence": 0.809, "class": "bed", "class_id": 2, "detection_id": "ea52b384-956c-4781-a631-6c7938493cb3" } ] }
