yolov5训练自己的数据集
1.下载yolo v5源码
基于YOLOV5的头盔佩戴检测识别系统源码+训练好的数据+权重文件。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要求在Python>= 3.7.0环境中，包括PyTorch> = 1.7。
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
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
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
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
(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
# 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
# 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
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
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640x640default=[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 <module 'models.common' from 'E:\\PythonCode\\yoloV5_toukui\\yolov5-5.0\\models\\common.py'>
解决方法:
去Tags6里面的model/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：
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
(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
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 [email protected] [email protected]:.95: 50%|█████████████████████████████▌ | Class Images Labels P R [email protected] [email protected]:.95: 100%|█████████████████████████████████████████████ Class Images Labels P R [email protected] [email protected]:.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
[email protected] [email protected]:.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.
parser.add_argument('--source', type=str, default='data/images', help='source')
修改为
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,
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
下载地址：
基于YOLOV5的头盔佩戴检测识别系统源码+训练好的数据+权重文件