YOLO-Worldv2两分钟快速部署

本次部署使用的框架基于ultralytics，并且已经集成最新版本的YOLOv8框架：

一键环境配置

pip install ultralytics

基础使用

训练

from ultralytics import YOLOWorld
model = YOLOWorld('yolov8x-worldv2.pt')
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model('path/to/bus.jpg')

推理

from ultralytics import YOLOWorld
model = YOLOWorld('yolov8s-worldv2.pt')
results = model.predict('path/to/image.jpg')
results[0].show()

自定义标签
- 我们定义了保温杯，马克杯，纸杯，抽纸，笔记本，屏幕等标签。均不被包含在COCO类别定义中。

model = YOLOWorld('yolov8s-worldv2.pt')
model.set_classes(['Insulated_cup','mug','paper_cup','drawing_paper','laptop', 'screen', 'woman', 'wirless_earphone', 'Shoulder_bag'])
model.save("elephant_camel_world.pt")

导出onnx

model = YOLO('cup_mug_world.pt')
model.export(format="onnx", opset=11)

实时推理

简单修改代码，使得opencv调用网络摄像头并介入yolo-world。代码如下：

from ultralytics import YOLOWorld
import cv2
import numpy as np

model = YOLOWorld('yolov8x-worldv2.pt') 
model.set_classes(['Insulated_cup','mug','paper_cup','drawing_paper','laptop', 'screen', 'woman', 'wirless_earphone', 'Shoulder_bag'])
model.save("elephant_camel_world.pt")


capture = cv2.VideoCapture(2)

# while True:
ret, frame = capture.read()
# cv2.imshow("result", frame)
# cv2.waitKey(0)

while True:
    ret, frame = capture.read()
    results = model.predict(np.array(frame))
    cv2.imshow("result", results[0].plot(False))
    cv2.waitKey(1)