相关资料：

• 目标检测—教你利用yolov5训练自己的目标检测模型_炮哥带你学的博客-CSDN博客_yolov5训练自己的模型
• 目标检测 YOLOv5 开源代码项目调试与讲解实战【土堆 x 布尔艺数】_哔哩哔哩_bilibili
• 手把手教你搭建自己的yolov5目标检测平台_哔哩哔哩_bilibili

一、环境配置

1、Python环境

官网链接：Start Locally | PyTorch

conda create -n pytorch python==3.7.3
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pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
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下的慢/卡死？？

==>

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
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conda install pytorch torchvision
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2、下载项目

这里我使用的是5.0版本的代码

GitHub - ultralytics/yolov5 at v5.0

安装相关依赖：

pip install -r requirements.txt
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二、如何利用YOLOv5进行预测

环境配置好后，我们就可以体验一下YOLOv5了

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
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parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    opt = parser.parse_args()
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2、体验一下

python detect.py --source image/1.jpg
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问题：

预测后又结果图片，但是并没有框出识别结果

解决方法：

我们进入detect.py，在第53行加入

cudnn.benchmark = True
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效果展示：

报错记录及解决

AttributeError: Can't get attribute 'SPPF' on models.common' from  '....\\common.py'>
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有的同学找不到SPPF这个类，那我现在直接粘贴在这里，你们只需要复制到你们的common.py里面即可，记得把import warnings放在上面去：

models/common.py

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))
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然后：

AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'
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然后我们找到事发现场：

File "E:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\upsampling.py", line 154, in forward
    recompute_scale_factor=self.recompute_scale_factor)
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Anaconda3\envs\pytorch\Lib\site-packages\torch\nn\modules

做出以下修改

    def forward(self, input: Tensor) -> Tensor:
        # return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
        #                      recompute_scale_factor=self.recompute_scale_factor)

        return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)
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之后。。。。

RuntimeError: The size of tensor a (80) must match the size of tensor b (56) at non-singleton dimension 3
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解决方法：

下载：
https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
替换默认下载的yolov5s.pt，因为默认下载的是V6.1的

替换后，在运行 detect.py就OK了

然后成功！

三、自定义数据集训练YOLOv5，并预测

1、数据准备

首先我们需要把VOC格式数据集，转换为YOLO格式数据集

我们统一一下目录结构

运行以下脚本

voc_to_yolo.py

需要更改以下 line 9 的 classes，这里我的数据集有俩标签 [“spoil”, “target”]

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 = ["spoil", "target"]

TRAIN_RATIO = 80


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)


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)


def convert_annotation(image_id):
    in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' % image_id, encoding='utf-8')
    out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' % image_id, 'w', encoding='utf-8')
    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 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()

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2、开始训练

正常训练，终端输出：

Image sizes 640 train, 640 test
Using 0 dataloader workers
Logging results to runs\train\exp2
Starting training for 50 epochs...
0/49     2.88G    0.1165   0.03494   0.03206    0.1835        32       640:  76%|███████▋  | 68/89 [01:24<00:22,  1.07s/it]
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50 epochs completed in 1.430 hours.

Optimizer stripped from runs\train\exp2\weights\last.pt, 14.4MB
Optimizer stripped from runs\train\exp2\weights\best.pt, 14.4MB

进程已结束，退出代码为 0
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报错记录：

BrokenPipeError: [Errno 32] Broken pipe
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• 原因：
  在训练过程中，设置的num_workers过大

• 解决方法：

  utils\datasets.py

  将 line 81 下的 num_workers 改为 0

  num_workers=0
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3、预测

修改一下detect.py

 parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp2/weights/best.pt', help='model.pt path(s)')
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开始预测

python detect.py --source image/2.jpg
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