YOLO real-time object detectors All In One
YOLO real-time object detectors All In One
YOLO26
Ultralytics YOLO26 is the latest evolution in the YOLO series of real-time object detectors, engineered from the ground up for edge and low-power devices. It introduces a streamlined design that removes unnecessary complexity while integrating targeted innovations to deliver faster, lighter, and more accessible deployment.
Ultralytics YOLO26 是 YOLO 系列实时对象检测器的最新演进,从头开始专为边缘和低功耗设备而设计。它引入了简化的设计,消除了不必要的复杂性,同时集成了有针对性的创新,以实现更快、更轻、更易于访问的部署。

https://docs.ultralytics.com/models/yolo26/
https://docs.ultralytics.com/zh/models/yolo26/
demos
车牌识别检测,交通违章自动举报
YOLOE-26 支持基于文本和基于视觉的提示, 使用提示只需通过 predict 方法
# Python
from ultralytics import YOLO
# Initialize model
model = YOLO("yoloe-26l-seg.pt") # or select yoloe-26s/m-seg.pt for different sizes
# Set text prompt to detect person and bus. You only need to do this once after you load the model.
model.set_classes(["person", "bus"])
# Run detection on the given image
results = model.predict("path/to/image.jpg")
# Show results
results[0].show()
# Python
import numpy as np
from ultralytics import YOLO
from ultralytics.models.yolo.yoloe import YOLOEVPSegPredictor
# Initialize model
model = YOLO("yoloe-26l-seg.pt")
# Define visual prompts using bounding boxes and their corresponding class IDs.
# Each box highlights an example of the object you want the model to detect.
visual_prompts = dict(
bboxes=np.array(
[
[221.52, 405.8, 344.98, 857.54], # Box enclosing person
[120, 425, 160, 445], # Box enclosing glasses
],
),
cls=np.array(
[
0, # ID to be assigned for person
1, # ID to be assigned for glasses
]
),
)
# Run inference on an image, using the provided visual prompts as guidance
results = model.predict(
"ultralytics/assets/bus.jpg",
visual_prompts=visual_prompts,
predictor=YOLOEVPSegPredictor,
)
# Show results
results[0].show()
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refs
https://github.com/ultralytics/ultralytics
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