yolov8训练流程

训练代码

from ultralytics import YOLO# Load a model
model = YOLO('yolov8n.yaml')  # build a new model from YAML
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights# Train the model
results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
print(results)

数据集路径

ultralytics/cfg/datasets/coco.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO 2017 dataset https://cocodataset.org by Microsoft
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
# Example usage: yolo train data=coco.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco  ← downloads here (20.1 GB)# 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)https://competitions.codalab.org/competitions/20794# Classes
names:0: person1: bicycle2: car3: motorcycle4: airplane5: bus6: train7: truck8: boat9: traffic light10: fire hydrant11: stop sign12: parking meter13: bench14: bird15: cat16: dog17: horse18: sheep19: cow20: elephant21: bear22: zebra23: giraffe24: backpack25: umbrella26: handbag27: tie28: suitcase29: frisbee30: skis31: snowboard32: sports ball33: kite34: baseball bat35: baseball glove36: skateboard37: surfboard38: tennis racket39: bottle40: wine glass41: cup42: fork43: knife44: spoon45: bowl46: banana47: apple48: sandwich49: orange50: broccoli51: carrot52: hot dog53: pizza54: donut55: cake56: chair57: couch58: potted plant59: bed60: dining table61: toilet62: tv63: laptop64: mouse65: remote66: keyboard67: cell phone68: microwave69: oven70: toaster71: sink72: refrigerator73: book74: clock75: vase76: scissors77: teddy bear78: hair drier79: toothbrush# Download script/URL (optional)
download: |from ultralytics.utils.downloads import downloadfrom pathlib import Path# Download labelssegments = True  # segment or box labelsdir = Path(yaml['path'])  # dataset root dirurl = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labelsdownload(urls, dir=dir.parent)# Download dataurls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)download(urls, dir=dir / 'images', threads=3)

数据集格式
安装labelme

conda create --name=labelme python=3
conda activate labelme
pip install labelme

启动

labelme  # just open gui