YOLOv8 + CLIP版本的YOLO-World模型已经发布,而且被ultralytics框架所支持,首先下载yolov8s-worldv2.pt模型,然后直接通过下面的代码即可推理测试: # Initialize a YOLO-World modelmodel = YOLO('yolov8s-worldv2.pt')# or choose yolov8m/l-world.pt # Define custom classesmodel.set_classes(["elephant"]...
AI代码解释 # Initialize aYOLO-World model model=YOLO('yolov8s-worldv2.pt')# or choose yolov8m/l-world.pt # Define custom classes model.set_classes(["elephant"])# Execute predictionforspecified categories on an image results=model.predict('D:/bird_test/elephant2.png')# Show results resul...
from ultralytics import YOLO # Initialize a YOLO-World model model = YOLO('yolov8s-worldv2.pt') # Define custom classes model.set_classes(["person", "bus"]) # Export the model model.export(format='onnx') 模型导出后结构如下图所示: 与其他模型不同的时,YOLO-World模型在推理时需要指定目...
# Initialize a YOLO-World model model = YOLO('yolov8/yolov8s-world.pt') # or choose yolov8m/l-world.pt # Define custom classes model.set_classes(["person"]) # Execute prediction for specified categories on an image results = model.predict('image_01.jpg') # Show results results[0]....
model.set_classes(["person"]) # Execute prediction for specified categories on an image results = model.predict('image_01.jpg') # Show results results[0].show() 这里,我们设定了只包括一个词汇的词汇表:["person"],那么自然,我们也可以根据需要设定多个词汇。由于设定了词汇表,所以就会用到CLIP,它...
yolo predict model=yolov8s-world.ptsource=path/to/image.jpg imgsz=640 设定词汇,进行推理: fromultralyticsimportYOLOif__name__ =='__main__':# Initialize a YOLO-World modelmodel = YOLO('yolov8/yolov8s-world.pt')# or choose yolov8m/l-world.pt# Define custom classesmodel.set_classes([...
如果只需要检测 "人 "和 "公共汽车 "对象,可以直接指定这些类model.set_classes(["person", "bus"]): AI检测代码解析 from ultralytics import YOLO # Initialize a YOLO-World model model = YOLO('yolov8s-world.pt') # or choose yolov8m/l-world.pt ...
pip install --no-cache "git+https://github.com/openai/CLIP.git" 下载yolo-worldv2权重github.com/ultralytics/ 2.yolo-world使用 (1)常规使用 from ultralytics import YOLO # Initialize a YOLO-World model model = YOLOWorld('yolov8s-worldv2.pt') # Execute prediction for specified categories on...
model=YOLO('elephant_camel_world.pt')model.export(format="onnx",opset=12) 可以看出模型得到ONNX格式文件的输出格式是动态的 使用ONNX格式模型直接推理部署,基于VMDM模型部署框架,直接导入模型,开始推。我直接生成并导出了两种自定义对象检测模型,分别支持识别大象跟鸟类,运行结果如下: ...
平台地址:https://sensecraft.seeed.cc/ai/#/model 2、在公共AI模型列表中找到【垃圾桶罐检测】模型,单击此模型图片,如下图所示。 3、进入【垃圾桶罐检测】模型介绍页面,单击“部署模型”按钮,如下图所示。 4、进入部署垃圾桶罐检测模型页面,按提示步骤先连接摄像头,再连接设备到电脑USB接口上,最后单击【连接设...