yolov5超级详细教程（包含win10的错误解决⽅案）⼀、下载yolov5
parse error怎么解决github/ultralytics/yolov5
git clone也可以，下载压缩包解压缩也可以
得到我们的yolov5⽂件夹
⼆、准备我们的权重⽂件和数据集
在D:\jestonnano\yolov5\data\scripts下有相应下载的⽂件，运⾏即可下载
或者在⽹上下载相应的coco128.zip数据集和yolov5权重
将权重⽂件放在yolov5下，然后在yolov5下创建⽂件夹dataset，把coco128放⼊
三、配置对应的环境
1.运⾏命令
# pip install -
2.或者⼿动下载
# base ----------------------------------------
matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.1.2
Pillow
PyYAML>=5.3.1
scipy>=1.4.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.41.0
# logging -------------------------------------
tensorboard>=2.4.1
# wandb
# plotting ------------------------------------
seaborn>=0.11.0
pandas
# export --------------------------------------
# coremltools>=4.1
# onnx>=1.9.0
# scikit-learn==0.19.2  # for coreml quantization
# extras --------------------------------------
# Cython  # for pycocotools github/cocodataset/cocoapi/issues/172
# pycocotools>=2.0  # COCO mAP
# albumentations>=1.0.3
thop  # FLOPs computation
四、设置训练集
在yolov5/data下，以coco128.yaml举例
我们可以看到代码：
# YOLOv5  by Ultralytics ultralytics, licensed under GNU GPL v3.0
# COCO128 dataset www.kaggle/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
#    └── coco128  ← downloads here
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/, or 3) list: [path/to/imgs1, path/to/imgs2, ..] path:../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:# test images (optional)
# Classes
nc:80# number of classes
names:['person','bicycle','car','motorcycle','airplane','bus','train','truck','boat','traffic light', 'fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow',
'elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee',
'skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard',
'tennis racket','bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple',
'sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair','couch',
'potted plant','bed','dining table','toilet','tv','laptop','mouse','remote','keyboard','cell phone',
'microwave','oven','toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear',
'hair drier','toothbrush']# class names
# Download script/URL (optional)
download: github/ultralytics/yolov5/releases/download/v1.0/coco128.zip
由于我们已经下载了coco128.zip，我们修改代码为
path:../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:# test images (optional)
# Classes
nc:80# number of classes
names:['person','bicycle','car','motorcycle','airplane','bus','train','truck','boat','traffic light', 'fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow',
'elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee',
'skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard',
'tennis racket','bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple',
'sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair','couch',
'potted plant','bed','dining table','toilet','tv','laptop','mouse','remote','keyboard','cell phone',
'microwave','oven','toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear',
'hair drier','toothbrush']# class names
如果我们要训练我们⾃⼰的数据集，我们可以修改训练数据集的路径，nc，names等等
即
train：换成你⾃⼰的训练集地址
val：换成⾃⼰的val地址
nc: 换成你⾃⼰定义的类别的数量
names：换成⾃⼰定义的类别的名称
怎么定义我们的数据集呢？
模仿coco128，我们要有我们的图⽚集和标签集
使⽤Labelbox和CVAT⼯具进⾏图像标记，将标签导出为darknet格式，×.txt每张图像⼀个⽂件(如果图像中没有对象，则不需要×.txt⽂件)。
txt⽂件内容有：
每个对象⼀⾏
每⾏都是class x_center y_center width height格式
框坐标必须采⽤归⼀化的xywh格式（从0到1）。如果boxes以像素为单位，划分x_center和width关于图像宽度，y_center和height关于图像⾼度。
类别定义利⽤序号，⼀般从0开始。
每个图像的标签⽂件应该可以通过在其路径名中简单地替换/images/ *.jpg为/labels/*.txt来定位。
当然我们也可以去互联⽹搜索现成的数据集
五、选择模型
在yolov5/models下
有五个模型⽂件，这⾥我们以yolov5s.yaml为例⼦
内容为
# Parameters
nc:80# number of classes
depth_multiple:0.33# model depth multiple
width_multiple:0.50# layer channel multiple
anchors:
-[10,13,16,30,33,23]# P3/8
-[30,61,62,45,59,119]# P4/16
-[116,90,156,198,373,326]# P5/32
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1,1, Focus,[64,3]],# 0-P1/2
[-1,1, Conv,[128,3,2]],# 1-P2/4
[-1,3, C3,[128]],
[-1,1, Conv,[256,3,2]],# 3-P3/8
[-1,9, C3,[256]],
[-1,1, Conv,[512,3,2]],# 5-P4/16
[-1,9, C3,[512]],
[-1,1, Conv,[1024,3,2]],# 7-P5/32
[-1,1, SPP,[1024,[5,9,13]]],
[-1,3, C3,[1024,False]],# 9
]
# YOLOv5 head
head:
[[-1,1, Conv,[512,1,1]],
[-1,1, nn.Upsample,[None,2,'nearest']],
[[-1,6],1, Concat,[1]],# cat backbone P4
[-1,3, C3,[512,False]],# 13
[-1,1, Conv,[256,1,1]],
[-1,1, nn.Upsample,[None,2,'nearest']],
[[-1,4],1, Concat,[1]],# cat backbone P3
[-1,3, C3,[256,False]],# 17 (P3/8-small)
[-1,1, Conv,[256,3,2]],
[[-1,14],1, Concat,[1]],# cat head P4
[-1,3, C3,[512,False]],# 20 (P4/16-medium)
[-1,1, Conv,[512,3,2]],
[[-1,10],1, Concat,[1]],# cat head P5
[-1,3, C3,[1024,False]],# 23 (P5/32-large)
[[17,20,23],1, Detect,[nc, anchors]],# Detect(P3, P4, P5)
]
如果你⽤的⾃⼰的数据集或者其它的数据集，根据你的类别修改nc数值即可六、开始训练
我们打开train.py，可以看到
def parse_opt(known=False):
parser = argparse.ArgumentParser()
parser.add_argument('--weights',type=str, default='yolov5s.pt',help='initial weights path')
parser.add_argument('--cfg',type=str, default='',help='model.yaml path')
parser.add_argument('--data',type=str, default='data/coco128.yaml',help='dataset.yaml path')
parser.add_argument('--hyp',type=str, default='data/hyps/hyp.scratch.yaml',help='hyperparameters path')
parser.add_argument('--epochs',type=int, default=300)
parser.add_argument('--batch-size',type=int, default=16,help='total batch size for all GPUs')
parser.add_argument('--imgsz','--img','--img-size',type=int, default=640,help='train, val image size (pixels)')
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('--noval', action='store_true',help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true',help='disable autoanchor check')
parser.add_argument('--evolve',type=int, nargs='?', const=300,help='evolve hyperparameters for x generations')
parser.add_argument('--bucket',type=str, default='',help='gsutil bucket')
parser.add_argument('--cache',type=str, nargs='?', const='ram',help='--cache images in "ram" (default) or "disk"')
parser.add_argument('--image-weights', action='store_true',help='use weighted image selection for training')
parser.add_argument('--device', default='',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 multi-class data as single-class')
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('--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('--entity', default=None,help='W&B entity')
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')
parser.add_argument('--quad', action='store_true',help='quad dataloader')
parser.add_argument('--linear-lr', action='store_true',help='linear LR')
parser.add_argument('--label-smoothing',type=float, default=0.0,help='Label smoothing epsilon')
parser.add_argument('--upload_dataset', action='store_true',help='Upload dataset as W&B artifact table')
parser.add_argument('--bbox_interval',type=int, default=-1,help='Set bounding-box image logging interval for W&B')
parser.add_argument('--save_period',type=int, default=-1,help='Log model after every "save_period" epoch')
parser.add_argument('--artifact_alias',type=str, default="latest",help='version of dataset artifact to be used')
parser.add_argument('--local_rank',type=int, default=-1,help='DDP parameter, do not modify')
parser.add_argument('--freeze',type=int, default=0,help='Number of layers to freeze. backbone=10, all=24')
opt = parser.parse_known_args()[0]if known else parser.parse_args()
return opt
这都是可以修改的参数，学习率啥的我们都不⽤修改，我们只⽤修改模型路径，权重，batch-size，epoch即可，在yolov5下打开我们的powershell/cmd
python train.py --img 640--batch-size 16--epochs 10
--data data/coco128.yaml 之间空格间隔就⾏，我是为了美观
--cfg  models/yolov5s.yaml
--weights ''代表从头开始训练
--weights 'yolov5s.pt'代表从我们下载的权重即预训练的模型加载开始训练
到这⾥我们win10的⼩伙伴可能会遇到⼀些错误，就是
UnicodeDecodeError: 'gbk' codec can't decode byte .................
这是因为我们open⽂件的时候txt的编码是utf8
解决⽅案：
只需要将train.py⾥⾯的69⾏with open(hyp) as f: 改为 with open(hyp,‘rb’) as f:，将yolo.py⾥的86⾏with open(cfg) as f：也改为with open(cfg,‘rb’) as f：即可

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