基于官方YOLOv4开发构建目标检测模型超详细实战教程【以自建缺陷检测数据集为例】

本文主要是介绍基于官方YOLOv4开发构建目标检测模型超详细实战教程【以自建缺陷检测数据集为例】，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

本文是关于基于YOLOv4开发构建目标检测模型的超详细实战教程，超详细实战教程相关的博文在前文有相应的系列，感兴趣的话可以自行移步阅读即可：
《基于yolov7开发实践实例分割模型超详细教程》

《YOLOv7基于自己的数据集从零构建模型完整训练、推理计算超详细教程》

《DETR (DEtection TRansformer)基于自建数据集开发构建目标检测模型超详细教程》

《基于yolov5-v7.0开发实践实例分割模型超详细教程》

《轻量级模型YOLOv5-Lite基于自己的数据集【焊接质量检测】从零构建模型超详细教程》

《轻量级模型NanoDet基于自己的数据集【接打电话检测】从零构建模型超详细教程》

《基于YOLOv5-v6.2全新版本模型构建自己的图像识别模型超详细教程》

《基于自建数据集【海底生物检测】使用YOLOv5-v6.1/2版本构建目标检测模型超详细教程》

《超轻量级目标检测模型Yolo-FastestV2基于自建数据集【手写汉字检测】构建模型训练、推理完整流程超详细教程》

《基于YOLOv8开发构建目标检测模型超详细教程【以焊缝质量检测数据场景为例】》

最早期接触v3和v4的时候印象中模型的训练方式都是基于Darknet框架开发构建的，模型都是通过cfg文件进行配置的，从v5开始才全面转向了PyTorch形式的项目，延续到了现在。

yolov4.cfg如下：

[net]
batch=64
subdivisions=8
# Training
#width=512
#height=512
width=608
height=608
channels=3
momentum=0.949
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1learning_rate=0.0013
burn_in=1000
max_batches = 500500
policy=steps
steps=400000,450000
scales=.1,.1#cutmix=1
mosaic=1#:104x104 54:52x52 85:26x26 104:13x13 for 416[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=mish# Downsample[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[route]
layers = -2[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[route]
layers = -1,-7[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish# Downsample[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[route]
layers = -2[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish[route]
layers = -1,-10[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish# Downsample[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[route]
layers = -2[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish[route]
layers = -1,-28[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish# Downsample[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[route]
layers = -2[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish[route]
layers = -1,-28[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish# Downsample[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[route]
layers = -2[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish[shortcut]
from=-3
activation=linear[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish[route]
layers = -1,-16[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=mish##########################[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky### SPP ###
[maxpool]
stride=1
size=5[route]
layers=-2[maxpool]
stride=1
size=9[route]
layers=-4[maxpool]
stride=1
size=13[route]
layers=-1,-3,-5,-6
### End SPP ###[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[upsample]
stride=2[route]
layers = 85[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[route]
layers = -1, -3[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[upsample]
stride=2[route]
layers = 54[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[route]
layers = -1, -3[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky##########################[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear[yolo]
mask = 0,1,2
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.2
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5[route]
layers = -4[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=256
activation=leaky[route]
layers = -1, -16[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear[yolo]
mask = 3,4,5
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.1
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5[route]
layers = -4[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=512
activation=leaky[route]
layers = -1, -37[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear[yolo]
mask = 6,7,8
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
scale_x_y = 1.05
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5

yolov4-tiny.cfg如下：

[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1learning_rate=0.00261
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky##################################[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear[yolo]
mask = 3,4,5
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes=80
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6[route]
layers = -4[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[upsample]
stride=2[route]
layers = -1, 23[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear[yolo]
mask = 1,2,3
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes=80
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6

最开始的时候还是蛮喜欢这种形式的，非常的简洁，直接使用Darknet框架训练也很方便，到后面随着模型改进各种组件的替换，Darknet变得越发不适用了。YOLOv4的话感觉定位相比于v3和v5来说比较尴尬一些，git里面搜索yolov4，结果如下所示：

排名第一的项目是pytorch-YOLOv4，地址在这里，如下所示：

从说明里面来看，这个只是一个minimal的实现：

官方的实现应该是：

仔细看的话会发现，官方这里提供了YOLOv3风格的实现项目以及YOLOv5风格的实现项目，本文主要是以YOLOv3风格的YOLOv4项目为基准来讲解完整的实践流程，项目地址在这里，如下所示：

首先下载所需要的项目，如下：

下载到本地解压缩后，如下所示：

网上直接百度下载这两个weights文件放在weights目录下，如下所示：

然后随便复制过来一个自己之前yolov5项目的数据集放在当前项目目录下，我是前面刚好基于yolov5做了钢铁缺陷检测项目，数据集可以直接拿来用，如果没有现成的数据集的话可以看我签名yolov5的超详细教程里面可以按照步骤自己创建数据集即可。如下所示：

这里我选择的是基于yolov4-tiny版本的模型来进行开发训练，为的就是计算速度能够更快一些。

修改train.py里面的内容，如下所示：

parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/yolov4-tiny.weights', help='initial weights path')
parser.add_argument('--cfg', type=str, default='cfg/yolov4-tiny.cfg', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/self.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()

终端直接执行：

python train.py

即可。

当然也可以选择基于参数指定的形式启动，如下：

python train.py --device 0 --batch-size 16 --img 640 640 --data self.yaml --cfg cfg/yolov4-tiny.cfg --weights 'weights/yolov4-tiny.weights' --name yolov4-tiny

根据个人喜好来选择即可。

启动训练终端输出如下所示：