1. 准备数据集

数据集格式跟yolov5一样，关于如何准备数据集可见之前的文章。

2. 创建 mydata.yaml

格式参考coco128.yaml，主要是 train / validate文件的存放路径，可以是同一个。
在ultralytics-main/ultralytics/datasets中，有标准数据集的yaml文件。
coco128.yaml格式如下：

# 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

3. 修改models , yolo8.yaml （这一步好像没有必要）

在models文件夹内，存放了v3-v8各个版本的模型配置文件。对目标模型文件进行修改，主要是对分类类别数量

如我的目标是4类，将nc设置为4

4. 官网下载预训练模型

如果是想先把流程走通的话，建议选择yolo8n.pt , V8系列里，最小的模型。

5. 训练

官方参考格式如下：
注意，如果从头开始训练，model = yolov8n.yaml ; 如果采用与训练的方式，model = yolov8n.pt

yolo task =detect mode=train model=yolov8n.pt data=mydata.yaml epochs=200

关于命令行，yolo 后可以跟哪些参数，可参考yolo/cfg/default.yaml

6. predict

参考

https://github.com/ultralytics/ultralytics
https://docs.ultralytics.com/usage/cli/#val