# 基于YOLO V5-5.0的头盔佩戴识别 **Repository Path**: tanggitee/yolov5-5.0 ## Basic Information - **Project Name**: 基于YOLO V5-5.0的头盔佩戴识别 - **Description**: 基于YOLOV5-5.0的头盔识别 - **Primary Language**: Python - **License**: GPL-3.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 21 - **Forks**: 3 - **Created**: 2022-02-15 - **Last Updated**: 2025-02-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # yolov5训练自己的数据集 ## 1.下载`yolo v5`源码 在GitHub中搜索`yolov5`，找到[ultralytics](https://github.com/ultralytics)/**[yolov5](https://github.com/ultralytics/yolov5)**项目，下载`.zip`文件，这里我下载的是`5.0`版本，或者使用git bash 输入https://github.com/ultralytics/yolov5.git下载代码。若下载的是`yolov5-5.0.zip`文件，下载完成后解压至代码编辑的地方。 ## 2.使用Anaconda创建虚拟环境 若无anaconda环境，也可直接使用python环境 在Anaconda Prompt中输入`conda create --name yolov5 python=3.8` 输入y回车，然后输入命令`conda activate yolov5`进入虚拟环境。 yoloV5[要求](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)[**在Python>= 3.7.0**](https://www.python.org/)环境中，包括[**PyTorch> = 1.7。**](https://pytorch.org/get-started/locally/) 然后我们进入解压后的YOLO V5项目文件夹，使用`pip install -r requirements.txt`命令下载项目所需依赖包（无anaconda可直接使用本命令安装依赖库，默认你安装好了python） 安装完成后,我们进入[PyTorch](https://pytorch.org/)官网,这里我选择以下配置: `PyTorch Build`选择`Stable (1.10.2)` `Your OS`选择`Windows` 系统 `Package`选择`Pip` 注意这里最好选用pip,conda会一直出现报错 `Language`选择`Python` `Compute Platform`选择`CUDA 10.2`有显卡建议选这个,没有显卡选择`CPU` `Run this Command:`显示 ```python pip3 install torch==1.10.2+cu102 torchvision==0.11.3+cu102 torchaudio===0.10.2+cu102 -f https://download.pytorch.org/whl/cu102/torch_stable.html ``` 将上面命令复制到控制台,安装pytorch,显示Successful即可 ## 3.建立VOC格式标准文件夹 在`yolov5-5.0\`下创建`make_voc_dir.py` ```python import os os.makedirs('VOCdevkit/VOC2007/Annotations') os.makedirs('VOCdevkit/VOC2007/JPEGImages') ``` 运行`make_voc_dir.py` 在`\yolov5-5.0\VOCdevkit\VOC2007\Annotations`中存放`xml`格式文件 在`\yolov5-5.0\VOCdevkit\VOC2007\JPEGImages`中存放`JPG`格式文件 ## 4.将xml格式转换成yolo格式 在`yolov5-5.0\`下创建`voc_to_yolo.py` ```python import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join import random from shutil import copyfile classes = ["crack","helmet"] # 这个列表里存放的是你的类别 TRAIN_RATIO = 90 # 训练的比例 # 遍历文件夹 def clear_hidden_files(path): dir_list = os.listdir(path) for i in dir_list: abspath = os.path.join(os.path.abspath(path), i) if os.path.isfile(abspath): if i.startswith("._"): os.remove(abspath) else: clear_hidden_files(abspath) # 对宽高进行归一化操作 size:原图的宽和高 def convert(size, box): dw = 1. / size[0] dh = 1. / size[1] x = (box[0] + box[1]) / 2.0 y = (box[2] + box[3]) / 2.0 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) # 解析XML def convert_annotation(image_id): in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' % image_id,'rb') out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' % image_id, 'w') tree = ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) bb = convert((w, h), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') in_file.close() out_file.close() wd = os.getcwd() wd = os.getcwd() data_base_dir = os.path.join(wd, "VOCdevkit/") if not os.path.isdir(data_base_dir): os.mkdir(data_base_dir) work_sapce_dir = os.path.join(data_base_dir, "VOC2007/") if not os.path.isdir(work_sapce_dir): os.mkdir(work_sapce_dir) annotation_dir = os.path.join(work_sapce_dir, "Annotations/") if not os.path.isdir(annotation_dir): os.mkdir(annotation_dir) clear_hidden_files(annotation_dir) image_dir = os.path.join(work_sapce_dir, "JPEGImages/") if not os.path.isdir(image_dir): os.mkdir(image_dir) clear_hidden_files(image_dir) yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/") if not os.path.isdir(yolo_labels_dir): os.mkdir(yolo_labels_dir) clear_hidden_files(yolo_labels_dir) yolov5_images_dir = os.path.join(data_base_dir, "images/") if not os.path.isdir(yolov5_images_dir): os.mkdir(yolov5_images_dir) clear_hidden_files(yolov5_images_dir) yolov5_labels_dir = os.path.join(data_base_dir, "labels/") if not os.path.isdir(yolov5_labels_dir): os.mkdir(yolov5_labels_dir) clear_hidden_files(yolov5_labels_dir) yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/") if not os.path.isdir(yolov5_images_train_dir): os.mkdir(yolov5_images_train_dir) clear_hidden_files(yolov5_images_train_dir) yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/") if not os.path.isdir(yolov5_images_test_dir): os.mkdir(yolov5_images_test_dir) clear_hidden_files(yolov5_images_test_dir) yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/") if not os.path.isdir(yolov5_labels_train_dir): os.mkdir(yolov5_labels_train_dir) clear_hidden_files(yolov5_labels_train_dir) yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/") if not os.path.isdir(yolov5_labels_test_dir): os.mkdir(yolov5_labels_test_dir) clear_hidden_files(yolov5_labels_test_dir) train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w') test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w') train_file.close() test_file.close() train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a') test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a') list_imgs = os.listdir(image_dir) # list image_one files prob = random.randint(1, 100) print("Probability: %d" % prob) for i in range(0, len(list_imgs)): path = os.path.join(image_dir, list_imgs[i]) if os.path.isfile(path): image_path = image_dir + list_imgs[i] voc_path = list_imgs[i] (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path)) (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path)) annotation_name = nameWithoutExtention + '.xml' annotation_path = os.path.join(annotation_dir, annotation_name) label_name = nameWithoutExtention + '.txt' label_path = os.path.join(yolo_labels_dir, label_name) prob = random.randint(1, 100) print("Probability: %d" % prob) if (prob < TRAIN_RATIO): # train dataset if os.path.exists(annotation_path): train_file.write(image_path + '\n') convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_train_dir + voc_path) copyfile(label_path, yolov5_labels_train_dir + label_name) else: # test dataset if os.path.exists(annotation_path): test_file.write(image_path + '\n') convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_test_dir + voc_path) copyfile(label_path, yolov5_labels_test_dir + label_name) train_file.close() test_file.close() ``` 执行一下`voc_to_yolo.py` ```shell (yoloV5) E:\PythonCode\yoloV5_toukui\yolov5-5.0>python voc_to_yolo.py Probability: 6 Probability: 79 Probability: 26 Probability: 19 Probability: 64 Probability: 5 Probability: 80 Probability: 40 Probability: 46 Probability: 23 Probability: 87 Probability: 19 Probability: 71 Probability: 62 Probability: 53 Probability: 74 Probability: 10 Probability: 19 Probability: 90 Probability: 35 Probability: 100 Probability: 27 Probability: 77 Probability: 65 Probability: 34 Probability: 95 Probability: 43 ``` 可以看到`\yolov5-5.0\VOCdevkit`文件下内生成了`images`和`labels`文件夹,文件夹内有`train`(训练样本)和`val`(验证样本)文件夹,文件夹内yolov5已经对标签进行了归一化处理,里面的参数分别为|类别数|中心点坐标归一化后的结果|宽和高的一个结果|；在`\yolov5-5.0`文件夹内生成了`yolov5_train.txt`和`yolov5_val.txt`两个文件 以及`\yolov5-5.0\VOCdevkit\VOC2007\YOLOLabels\`内的文件对项目没有影响,可以直接删除 ## 5.修改yaml配置文件 进入`\yolov5-5.0\data\`文件夹内，打开`voc.yaml`文件, 原`voc.yaml`文件 ```yaml # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ # Train command: python train.py --data voc.yaml # Default dataset location is next to /yolov5: # /parent_folder # /VOC # /yolov5 # download command/URL (optional) download: bash data/scripts/get_voc.sh # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../VOC/images/train/ # 16551 images val: ../VOC/images/val/ # 4952 images # number of classes nc: 20 # class names names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] ``` 修改后的`voc.yaml`文件 ```yaml # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ # Train command: python train.py --data voc.yaml # Default dataset location is next to /yolov5: # /parent_folder # /VOC # /yolov5 # download command/URL (optional) download: bash data/scripts/get_voc.sh # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: VOCdevkit/images/train/ # train: ../VOC/images/train/ # 16551 images val: VOCdevkit/images/val/ # val: ../VOC/images/val/ # 4952 images # number of classes nc: 2 # nc: 20 # class names names: [ 'crack', 'helmet' ] # names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', # 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] ``` 将`train` `val` 修改为目的路径 `nc`为类别数量 `names`为类别名 ## 6.开始训练 ### 6.1权重文件下载 在`\yolov5-5.0\weights\`里放置`.pt`文件,可以执行`download_weights.sh`下载官方`.pt`文件,也可直接去GitHub上[下载](https://github.com/ultralytics/yolov5/releases) 这里我使用`yolov5m.pt`标准版模型 ### 6.2 参数修改 再点开`train.py`,找到`if __name__ == '__main__':`开始修改参数 --- 修改权重文件地址为`default='weights/yolov5m.pt'` `parser.add_argument('--weights', type=str, default='weights/yolov5m.pt'` --- config可改可不改,这里修改为`default='models/yolov5m.yaml'` `parser.add_argument('--cfg', type=str, default='models/yolov5m.yaml', ` 这里的`yolov5m.yaml`里的`anchors:`需要通过`kmeans`进行聚类 可参考这篇博客进行配置[YOLOv5训练自己的数据集](https://blog.csdn.net/qq_36756866/article/details/109111065) --- 修改data文件地址为`default='data/voc.yaml', ` `parser.add_argument('--data', type=str, default='data/voc.yaml', ` --- `hyp`为随机数相关参数,不用修改 --- `epochs`为训练的轮次,这里是300次`default=300` --- `batch-size`每次给的批次,这里由于我的内存限制我每次只给1次`default=1` `parser.add_argument('--batch-size', type=int, default=1,` --- `img-size`输入图像大小,这里是640x640`default=[640, 640]` --- 其余参数不用修改,***接下来我们就可以开始训练了!!!*** --- 这里出现报错`AttributeError: Can't get attribute 'SPPF' on ` 解决方法: 去Tags6里面的[model/common.py](https://github.com/ultralytics/yolov5/blob/v6.0/models/common.py)里面去找到这个SPPF的类,把它拷过来到你这个Tags5的model/common.py里面,这样你的代码就也有这个类了,还要引入一个warnings包就行了！ 增加SPPF这个类内容如下，将下面的代码复制到你们的`common.py`里面即可，记得把`import warnings`放在上面去： ```python import warnings class SPPF(nn.Module): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) ``` 完美解决 --- 运行`train.py` ```shell (yoloV5_toukui) E:\PythonCode\yoloV5_toukui\yolov5-5.0>python train.py github: skipping check (not a git repository) YOLOv5 2021-4-11 torch 1.10.1+cpu CPU Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=False, cfg='models/yolov5m.yaml', data='data/voc.yaml', device='', entity=None, epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs\\train\\exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='weights/yolov5m.pt', workers=8, world_size=1) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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 wandb: Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended) Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 5280 models.common.Focus [3, 48, 3] 1 -1 1 41664 models.common.Conv [48, 96, 3, 2] 2 -1 1 65280 models.common.C3 [96, 96, 2] 3 -1 1 166272 models.common.Conv [96, 192, 3, 2] 4 -1 1 629760 models.common.C3 [192, 192, 6] 5 -1 1 664320 models.common.Conv [192, 384, 3, 2] 6 -1 1 2512896 models.common.C3 [384, 384, 6] 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2] 8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]] 9 -1 1 4134912 models.common.C3 [768, 768, 2, False] 10 -1 1 295680 models.common.Conv [768, 384, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 1182720 models.common.C3 [768, 384, 2, False] 14 -1 1 74112 models.common.Conv [384, 192, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 296448 models.common.C3 [384, 192, 2, False] 18 -1 1 332160 models.common.Conv [192, 192, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 1035264 models.common.C3 [384, 384, 2, False] 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 4134912 models.common.C3 [768, 768, 2, False] 24 [17, 20, 23] 1 28287 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] D:\software\Anaconda3\envs\yoloV5_toukui\lib\site-packages\torch\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ..\aten\src\ATen\native\TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Model Summary: 391 layers, 21060447 parameters, 21060447 gradients, 50.4 GFLOPS Transferred 402/506 items from weights/yolov5m.pt Scaled weight_decay = 0.0005 Optimizer groups: 86 .bias, 86 conv.weight, 83 other train: Scanning 'VOCdevkit\labels\train' images and labels... 23 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 23/23 train: New cache created: VOCdevkit\labels\train.cache val: Scanning 'VOCdevkit\labels\val' images and labels... 3 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 3/3 [00:00< val: New cache created: VOCdevkit\labels\val.cache Plotting labels... ``` 最终结果 ```shell Epoch gpu_mem box obj cls total labels img_size 299/299 1.21G 0.04496 0.02003 0.02956 0.09454 1 640: 4%|███ | 1/23 [00:00<00:03, 5.90it/ 299/299 1.21G 0.06122 0.02847 0.0249 0.1146 6 640: 4%|███ | 1/23 [00:00<00:03, 5.90it/ 299/299 1.21G 0.06122 0.02847 0.0249 0.1146 6 640: 9%|██████ | 2/23 [00:00<00:03, 5.88 299/299 1.21G 0.06042 0.02963 0.02202 0.1121 2 640: 9%|██████ | 2/23 [00:00<00:03, 5.88 299/299 1.21G 0.06042 0.02963 0.02202 0.1121 2 640: 13%|█████████▏ | 3/23 [00:00<00:03, 299/299 1.21G 0.05973 0.02952 0.02186 0.1111 3 640: 13%|█████████▏ | 3/23 [00:00<00:03, 299/299 1.21G 0.05973 0.02952 0.02186 0.1111 3 640: 17%|████████████▏ | 4/23 [00:00<00:03 299/299 1.21G 0.05656 0.02924 0.02161 0.1074 2 640: 17%|████████████▏ | 4/23 [00:00<00:03 299/299 1.21G 0.05656 0.02924 0.02161 0.1074 2 640: 22%|███████████████▏ | 5/23 [00:00<00 299/299 1.21G 0.05358 0.02645 0.0194 0.09942 1 640: 22%|███████████████▏ | 5/23 [00:01<00 299/299 1.21G 0.05358 0.02645 0.0194 0.09942 1 640: 26%|██████████████████▎ | 6/23 [00:01 299/299 1.21G 0.05662 0.02863 0.01956 0.1048 5 640: 26%|██████████████████▎ | 6/23 [00:01 299/299 1.21G 0.05662 0.02863 0.01956 0.1048 5 640: 30%|█████████████████████▎ | 7/23 [00 299/299 1.21G 0.05714 0.02914 0.02038 0.1067 2 640: 30%|█████████████████████▎ | 7/23 [00 299/299 1.21G 0.05714 0.02914 0.02038 0.1067 2 640: 35%|████████████████████████▎ | 8/23 299/299 1.21G 0.05828 0.03214 0.0208 0.1112 5 640: 35%|████████████████████████▎ | 8/23 299/299 1.21G 0.05828 0.03214 0.0208 0.1112 5 640: 39%|███████████████████████████▍ | 9/ 299/299 1.21G 0.06111 0.03179 0.02086 0.1138 3 640: 39%|███████████████████████████▍ | 9/ 299/299 1.21G 0.06111 0.03179 0.02086 0.1138 3 640: 43%|██████████████████████████████ | 1 299/299 1.21G 0.05965 0.03091 0.01985 0.1104 1 640: 43%|██████████████████████████████ | 1 299/299 1.21G 0.05965 0.03091 0.01985 0.1104 1 640: 48%|█████████████████████████████████ 299/299 1.21G 0.06099 0.03505 0.01993 0.116 10 640: 48%|█████████████████████████████████ 299/299 1.21G 0.06099 0.03505 0.01993 0.116 10 640: 52%|████████████████████████████████████ 299/299 1.21G 0.06144 0.03449 0.01998 0.1159 3 640: 52%|████████████████████████████████████ 299/299 1.21G 0.06144 0.03449 0.01998 0.1159 3 640: 57%|███████████████████████████████████████ 299/299 1.21G 0.06225 0.03479 0.02013 0.1172 3 640: 57%|███████████████████████████████████████ 299/299 1.21G 0.06225 0.03479 0.02013 0.1172 3 640: 61%|██████████████████████████████████████████ 299/299 1.21G 0.06229 0.03422 0.02033 0.1168 3 640: 61%|██████████████████████████████████████████ 299/299 1.21G 0.06229 0.03422 0.02033 0.1168 3 640: 65%|█████████████████████████████████████████████ 299/299 1.21G 0.06267 0.03524 0.02006 0.118 10 640: 65%|█████████████████████████████████████████████ 299/299 1.21G 0.06267 0.03524 0.02006 0.118 10 640: 70%|████████████████████████████████████████████████ 299/299 1.21G 0.06301 0.03419 0.01984 0.117 1 640: 70%|████████████████████████████████████████████████ 299/299 1.21G 0.06301 0.03419 0.01984 0.117 1 640: 74%|███████████████████████████████████████████████████ 299/299 1.21G 0.0642 0.03599 0.02012 0.1203 11 640: 74%|███████████████████████████████████████████████████ 299/299 1.21G 0.0642 0.03599 0.02012 0.1203 11 640: 78%|███████████████████████████████████████████████████ 299/299 1.21G 0.06377 0.03509 0.02048 0.1193 1 640: 78%|███████████████████████████████████████████████████ 299/299 1.21G 0.06377 0.03509 0.02048 0.1193 1 640: 83%|███████████████████████████████████████████████████ 299/299 1.21G 0.06399 0.03754 0.02059 0.1221 8 640: 83%|███████████████████████████████████████████████████ 299/299 1.21G 0.06399 0.03754 0.02059 0.1221 8 640: 87%|███████████████████████████████████████████████████ 299/299 1.21G 0.06392 0.03851 0.02078 0.1232 6 640: 87%|███████████████████████████████████████████████████ 299/299 1.21G 0.06392 0.03851 0.02078 0.1232 6 640: 91%|███████████████████████████████████████████████████ 299/299 1.21G 0.06446 0.03858 0.02099 0.124 4 640: 91%|███████████████████████████████████████████████████ 299/299 1.21G 0.06446 0.03858 0.02099 0.124 4 640: 96%|███████████████████████████████████████████████████ 299/299 1.21G 0.06421 0.03818 0.02112 0.1235 2 640: 96%|███████████████████████████████████████████████████ 299/299 1.21G 0.06421 0.03818 0.02112 0.1235 2 640: 100%|███████████████████████████████████████████████████ 299/299 1.21G 0.06421 0.03818 0.02112 0.1235 2 640: 100%|█████████████████████████████████████████████████████████████████████| 23/23 [00:04<00:00, 5.65it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████████████████████████████▌ | Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█████████████████████████████████████████████ Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|███████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.45it/s] all 3 12 0.182 0.55 0.177 0.0306 crack 3 10 0.166 0.1 0.0881 0.0248 helmet 3 2 0.199 1 0.265 0.0364300 epochs completed in 0.638 hours.Optimizer stripped from runs\train\exp4\weights\last.pt, 42.5MBOptimizer stripped from runs\train\exp4\weights\best.pt, 42.5MB ``` 各参数意义: box 回归框的损失 obj 置信度的损失 cls 类别的损失 total 总的损失 mAP@.5 mAP@.5:.95 置信度为0.5~0.95的一个置信度的map值 ## 7.使用训练好的权重文件进行识别 打开`detect.py`,找到`if __name__ == '__main__':` 载入预训练权重`default='weights/yolov5s.pt'`修改为`default='runs/train/exp/weights/last.pt'` 将要测试的图片放入`data/images` 运行`python detect.py`,运行完成后,会在`runs`下生成`detect`文件夹,里面也有一个`exp`文件夹里面存放着预测后的结果. 若要进行视频流的检测,只需修改`source`里文件的路径为视频所在路径即可. ```python parser.add_argument('--source', type=str, default='data/images', help='source') ``` 修改为 ```python parser.add_argument('--source', type=str, default='/Desktop/a.mp4', help='source') ``` ## 8 使用USB摄像头进行识别 打开`detect.py`,找到`if __name__ == '__main__':` 将路径设置为0 `parser.add_argument('--source', type=str, default='0` 再进入`yolov5-5.0\utils\datasets.py`的279行~282行,将这四行注释掉, ```python if 'youtube.com/' in url or 'youtu.be/' in url: # if source is YouTube video check_requirements(('pafy', 'youtube_dl')) import pafy url = pafy.new(url).getbest(preftype="mp4").url ``` 再运行`python detect.py`即可