YOLOv8 上手体验

环境搭建

⚡注意

Python>=3.8
PyTorch>=1.8

CUDA

下载CUDA最新版本

PyTorch

安装PyTorch 命令获取 ，根据自己的情况选好后复制安装命令

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

ultralytics

pip install ultralytics

食用

cmd

cmd 先到需要的目录再 输入命令，它会保存到cmd当前所在目录：

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

预测结果存放在：\runs\detect\predict

Python

用法示例

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

导出官方模型到本地

cmd 先到需要的目录再 输入命令，它会保存到cmd当前所在目录：

yolo export model=yolov8n.pt format=torchscript

官网
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