为什么使用 Ultralytics YOLO 进行推理？

多数据源兼容性：无论您的数据是单个图像、图像集合、视频文件还是实时视频流，预测模式都能处理。
流模式：使用流功能生成内存高效的结果对象生成器。通过在预测器的调用方法中设置 stream=True 启用此功能。
批处理：能够在单个批处理中处理多个图像或视频帧，从而加快推理时间。
集成友好：由于其灵活的 API，轻松与现有数据管道和其他软件组件集成。
Ultralytics YOLO 模型在推理期间返回 Python 结果对象列表或在传递 stream=True 时返回内存高效的结果对象生成器：
from ultralytics import YOLO

Load a model

model = YOLO("yolov8n.pt") # pretrained YOLOv8n model

Run batched inference on a list of images

results = model(["im1.jpg", "im2.jpg"], stream=True) # return a generator of Results objects

Process results generator

for result in results:

boxes = result.boxes  # Boxes object for bounding box outputs
masks = result.masks  # Masks object for segmentation masks outputs
keypoints = result.keypoints  # Keypoints object for pose outputs
probs = result.probs  # Probs object for classification outputs
obb = result.obb  # Oriented boxes object for OBB outputs
result.show()  # display to screen
result.save(filename="result.jpg")  # save to disk

from ultralytics import YOLO

Load a pretrained YOLOv8n model

model = www.laipuhuo.com YOLO("yolov8n.pt")

Define remote image or video URL

source = "https://ultralytics.com/images/bus.jpg"

Run inference on the source

results = model(source) # list of Results objects
from ultralytics import YOLO

Load a pretrained YOLOv8n model

model = YOLO("yolov8n.pt")

Define a path to a CSV file with images, URLs, videos and directories

source = "path/to/file.csv"

Run inference on the source

results = model(source) # list of Results objects
from ultralytics import YOLO

Load a pretrained YOLOv8n model

model = YOLO("yolov8n.pt")

Define a glob search for all JPG files in a directory

source = "path/to/dir/*.jpg"

OR define a recursive glob search for all JPG files including subdirectories

source = "path/to/dir/*/.jpg"

Run inference on the source

results = model(source, stream=True) # generator of Results objects

from ultralytics import www.laipuhuo.com YOLO

Load a pretrained YOLOv8n model

model = YOLO("yolov8n.pt")

Single stream with batch-size 1 inference

source = "rtsp://example.com/media.mp4" # RTSP, RTMP, TCP or IP streaming address

Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)

source = "path/to/list.streams" # *.streams text file with one streaming address per row

Run inference on the source

results = model(source, stream=True www.laipuhuo.com) # generator of Results objects