ultralytics团队在最近又推出了YOLOv11，不知道在有生之年能不能看到YOLOv100呢哈哈。
根据官方文档，在 Python>=3.8并且PyTorch>=1.8的环境下即可安装YOLOv11，因此之前YOLOv8的环境是可以直接用的。
安装YOLOv11：
pip install ultralytics
命令行测试：
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
或者
yolo predict model=yolo11n.pt source='=bus.jpg'
得到结果：
Ultralytics 8.3.1 🚀 Python-3.9.19 torch-1.8.0+cu111 CUDA:0 (NVIDIA GeForce RTX 3070 Laptop GPU, 8192MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPsimage 1/1 D:\document\VScode_workspace\ultralytics-8.3.1\bus.jpg: 640x480 4 persons, 1 bus, 0.0ms
Speed: 11.5ms preprocess, 0.0ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 480)
Results saved to runs\detect\predict2
💡 Learn more at https://docs.ultralytics.com/modes/predict
VS Code: view Ultralytics VS Code Extension ⚡ at https://docs.ultralytics.com/integrations/vscode
python脚本测试：
from ultralytics import YOLO# Load a model
model = YOLO("yolo11n.pt")# Train the model
train_results = model.train(data="coco8.yaml", # path to dataset YAMLepochs=100, # number of training epochsimgsz=640, # training image sizedevice="cpu", # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu
)# Evaluate model performance on the validation set
metrics = model.val()# Perform object detection on an image
results = model("zidane.jpg")
results[0].show()# Export the model to ONNX format
path = model.export(format="onnx") # return path to exported model
测试结果如下：
