YOLOV8 安全帽佩戴检测（含训练好的模型和训练集）免费下载_yolov8 安全帽模型下载

四步教你完成YOLO v8 安全帽检测！！！

（首先你自己有python 3.7及以上版本）

第一步：pip install ultralytics

C:\Users\Administrator>pip install ultralytics
Collecting ultralytics
  Downloading ultralytics-8.0.151-py3-none-any.whl (616 kB)
     |████████████████████████████████| 616 kB 12 kB/s
Requirement already satisfied: pillow>=7.1.2 in c:\python\python37\lib\site-packages (from ultralytics) (8.1.1)
Requirement already satisfied: scipy>=1.4.1 in c:\python\python37\lib\site-packages (from ultralytics) (1.6.1)
Collecting opencv-python>=4.6.0
  Downloading opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl (38.6 MB)
     |                                | 40 kB 9.5 kB/s eta 1:07:27

第二步：pip freeze 

确保安装完成ultralytics库

ultralytics==8.0.145
urllib3==1.26.3

第三步：运行 yolo_train.py

D:\==\safehat>python yolo_train.py
Ultralytics YOLOv8.0.145  Python-3.7.2 torch-1.8.0+cpu CPU (AMD E2-3200 APU with Radeon(tm) HD Graphics)
WARNING  Upgrade to torch>=2.0.0 for deterministic training.
engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=safehat.yaml, epochs=100, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train

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yolo_train.py代码如下：

from ultralytics import YOLO

model = YOLO('yolov8n.pt') #加载模型

model.train(data = 'safehat.yaml',epochs=1)

model.val()

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训练完成后，会在train文件下生成weight文件夹，下面有两个文件best.pt和 last.pt

第四步：运行# python yolo_test.py

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yolo_test.py代码如下：

from ultralytics import YOLO

model = YOLO('best.pt')

model.predict( '1.jpg', save = True)

model.predict( '2.jpg', save = True)
model.predict('3.jpg',save = True, classes = [0, 2], line_width = 30)

#model.predict('myself2.jpg',save = True, classes = [0, 2], line_width = 30)

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会在runs文件夹下生成结果图片

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2024.2.28更新训练结果

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注意：文件目录下，一定有以下几个文件，特别是红框标准：

不然运行会报错！！！

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训练好的模型与代码集

如下：

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2024.2.28更新训练集下载链接：

https://www.kaggle.com/datasets/snehilsanyal/construction-site-safety-image-dataset-roboflow

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 参考文章链接如下；

基于yolov8，训练一个安全帽佩戴的目标检测模型

=================2024.2.29 1个积分下载链接=======================

为了方便大家，特提供下载链接（1积分）

https://download.csdn.net/download/paul123456789io/88888856

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