【yolov8目标检测】使用yolov8训练自己的数据集

目录

准备数据集 

python安装yolov8 

配置yaml 

从0开始训练

从预训练模型开始训练

准备数据集 

首先得准备好数据集，你的数据集至少包含images和labels，严格来说你的images应该包含训练集train、验证集val和测试集test，不过为了简单说明使用步骤，其中test可以不要，val和train可以用同一个，因此我这里只用了一个images

其中images装的是图片数据，labels装的是与图片一一对应同名的yolo格式txt，即类别号，经过归一化的中心x和y坐标以及宽和高

python安装yolov8 

然后我们开始准备yolov8，使用python的API的话就比较简单，首先安装一下yolov8

用pip的话安装的话

pip install ultralytics

使用pycharm安装的话

配置yaml 

安装完了之后，差不多就可以开始了，我们首先看看官方给的代码

from ultralytics import YOLO

# Load a model

model = YOLO("yolov8n.yaml") # build a new model from scratch

model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)

# Use the model

model.train(data="coco128.yaml", epochs=3) # train the model

metrics = model.val() # evaluate model performance on the validation set

results = model("https://ultralytics.com/images/bus.jpg") # predict on an image

path = model.export(format="onnx") # export the model to ONNX format

其中迷惑的是yolov8n.yaml、yolov8n.pt和coco128.yaml这几个文件，yolov8n.yaml是yolov8的配置，yolov8n.pt是预训练的模型，coco128.yaml是coco数据集的配置参数

因此如果我们想要训练自己的模型的话，需要修改一下配置文件，首先到GitHub上下载yolov8n.yaml和coco128.yaml下来，这两个文件的位置有可能会变，所以最好在仓库上直接搜索

大概长这样，你也可以自己创建，然后把内容复制进去

yolov8n.yaml

# Ultralytics YOLO , AGPL-3.0 license

# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters

nc: 80 # number of classes

scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'

# [depth, width, max_channels]

n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs

s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs

m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs

l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs

x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone

backbone:

# [from, repeats, module, args]

- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2

- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4

- [-1, 3, C2f, [128, True]]

- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8

- [-1, 6, C2f, [256, True]]

- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16

- [-1, 6, C2f, [512, True]]

- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32

- [-1, 3, C2f, [1024, True]]

- [-1, 1, SPPF, [1024, 5]] # 9

# YOLOv8.0n head

head:

- [-1, 1, nn.Upsample, [None, 2, 'nearest']]

- [[-1, 6], 1, Concat, [1]] # cat backbone P4

- [-1, 3, C2f, [512]] # 12

- [-1, 1, nn.Upsample, [None, 2, 'nearest']]

- [[-1, 4], 1, Concat, [1]] # cat backbone P3

- [-1, 3, C2f, [256]] # 15 (P3/8-small)

- [-1, 1, Conv, [256, 3, 2]]

- [[-1, 12], 1, Concat, [1]] # cat head P4

- [-1, 3, C2f, [512]] # 18 (P4/16-medium)

- [-1, 1, Conv, [512, 3, 2]]

- [[-1, 9], 1, Concat, [1]] # cat head P5

- [-1, 3, C2f, [1024]] # 21 (P5/32-large)

- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)

coco128.yaml

# Ultralytics YOLO , AGPL-3.0 license

# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics

# Example usage: yolo train data=coco128.yaml

# parent

# ├── ultralytics

# └── datasets

# └── coco128 ← downloads here (7 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, 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

names:

0: person

1: bicycle

2: car

3: motorcycle

4: airplane

5: bus

6: train

7: truck

8: boat

9: traffic light

10: fire hydrant

11: stop sign

12: parking meter

13: bench

14: bird

15: cat

16: dog

17: horse

18: sheep

19: cow

20: elephant

21: bear

22: zebra

23: giraffe

24: backpack

25: umbrella

26: handbag

27: tie

28: suitcase

29: frisbee

30: skis

31: snowboard

32: sports ball

33: kite

34: baseball bat

35: baseball glove

36: skateboard

37: surfboard

38: tennis racket

39: bottle

40: wine glass

41: cup

42: fork

43: knife

44: spoon

45: bowl

46: banana

47: apple

48: sandwich

49: orange

50: broccoli

51: carrot

52: hot dog

53: pizza

54: donut

55: cake

56: chair

57: couch

58: potted plant

59: bed

60: dining table

61: toilet

62: tv

63: laptop

64: mouse

65: remote

66: keyboard

67: cell phone

68: microwave

69: oven

70: toaster

71: sink

72: refrigerator

73: book

74: clock

75: vase

76: scissors

77: teddy bear

78: hair drier

79: toothbrush

# Download script/URL (optional)

download: https://ultralytics.com/assets/coco128.zip

然后修改yolov8n.yaml，把nc的数值改成你的数据集的类别数，我这里的数据集只有乌骨鸡和狮头鹅两个

# Ultralytics YOLO , AGPL-3.0 license

# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters

nc: 2 # number of classes

scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'

# [depth, width, max_channels]

n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs

s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs

m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs

l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs

x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone

backbone:

# [from, repeats, module, args]

- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2

- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4

- [-1, 3, C2f, [128, True]]

- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8

- [-1, 6, C2f, [256, True]]

- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16

- [-1, 6, C2f, [512, True]]

- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32

- [-1, 3, C2f, [1024, True]]

- [-1, 1, SPPF, [1024, 5]] # 9

# YOLOv8.0n head

head:

- [-1, 1, nn.Upsample, [None, 2, 'nearest']]

- [[-1, 6], 1, Concat, [1]] # cat backbone P4

- [-1, 3, C2f, [512]] # 12

- [-1, 1, nn.Upsample, [None, 2, 'nearest']]

- [[-1, 4], 1, Concat, [1]] # cat backbone P3

- [-1, 3, C2f, [256]] # 15 (P3/8-small)

- [-1, 1, Conv, [256, 3, 2]]

- [[-1, 12], 1, Concat, [1]] # cat head P4

- [-1, 3, C2f, [512]] # 18 (P4/16-medium)

- [-1, 1, Conv, [512, 3, 2]]

- [[-1, 9], 1, Concat, [1]] # cat head P5

- [-1, 3, C2f, [1024]] # 21 (P5/32-large)

- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)

然后修改coco128.yaml，我把文件名也改成了data.yaml，path改成images和labels的上一级目录地址，train改成训练集相对于path的地址，val也是改成验证集的相对于path的地址，我这里训练集和验证集用的是同一个嘿嘿嘿，然后把test注释掉，因为我没用测试集，还有就是names那里改成你的训练集的类别名，并把多余的类别删掉

# Ultralytics YOLO , AGPL-3.0 license

# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics

# Example usage: yolo train data=coco128.yaml

# parent

# ├── ultralytics

# └── datasets

# └── coco128 ← downloads here (7 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: C:/Users/Yezi/Desktop/人工智能实训/HW2/data # dataset root dir

train: images # train images (relative to 'path') 128 images

val: images # val images (relative to 'path') 128 images

#test: # test images (optional)

# Classes

names:

0: goose

1: chicken

这样子就配置好了

然后开始训练

从0开始训练

下面是从0开始训练的过程

其实训练的代码就两行

model = YOLO("yolov8n.yaml") # build a new model from scratch

model.train(data="data.yaml", epochs=5) # train the model

不过从0开始训练的效果并不好，下面是我自己的测试代码，由于我电脑比较烂，GPU摆不上用场，所以只能用cpu训练

from ultralytics import YOLO

import matplotlib.pyplot as plt

model = YOLO("yolov8n.yaml") # build a new model from scratch

model.train(data="data.yaml", epochs=30, device='cpu') # train the model

model.val(data="data.yaml")

results = model(r"C:\Users\Yezi\Desktop\人工智能实训\HW2\data\images\00909.jpg") # predict on an image

plt.imshow(results[0].plot())

plt.show()

results = model(r"C:\Users\Yezi\Desktop\人工智能实训\HW2\data\images\100318.jpg") # predict on an image

plt.imshow(results[0].plot())

plt.show()

从预训练模型开始训练

官方推荐用预训练好的模型开始训练

首先下载一个官方预训练好的模型

我这里下载的是yolov8n

然后使用预训练模型训练我的数据集

from ultralytics import YOLO

import matplotlib.pyplot as plt

model=YOLO("yolov8n.pt")

model.train(data="data.yaml", epochs=30, device='cpu') # train the model

model.val(data="data.yaml")

results = model(r"C:\Users\Yezi\Desktop\人工智能实训\HW2\data\images\00909.jpg") # predict on an image

plt.imshow(results[0].plot())

plt.show()

results = model(r"C:\Users\Yezi\Desktop\人工智能实训\HW2\data\images\100318.jpg") # predict on an image

plt.imshow(results[0].plot())

plt.show()

 乌骨鸡的效果是这样的

狮头鹅的效果是这样的

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