在ultralytics库中,YOLO模型通常是通过YOLO类来访问的,而不是直接作为模块导入。因此,正确的导入方式可能是: python from ultralytics import YOLO 注意这里的YOLO是大写的。如果你尝试导入yolo(小写的),Python将找不到对应的模块或对象,从而引发ImportError。 查找from ultralytics import yolo报错的详细信息和错误...
importsupervisionassv fromultralyticsimportYOLO classCountObject: def__init__(self, input_video_path, output_video_path)->None: # 加载YOLOv8模型 self.model = YOLO('yolov8s.pt') # 设置颜色 self.colors = sv.ColorPalette.default # 输入视频, 输出视频 self.input_video_path = input_video_path...
import warnings warnings.filterwarnings('ignore') from ultralytics import YOLO if __name__ == '__main__': model = YOLO('/yolo11/yolo11-2/runs/train/kitti-yolo11/weights/best.pt') # 选择训练好的权重路径 model.val(data='/Object_detection/LS/yolo11/yolo11-1/ultralytics/cfg/datasets...
from ultralytics import YOLO model = YOLO('yolov8x.yaml') # build a new model from YAML model = YOLO('yolov8x.pt') # load a pretrained model (recommended for training) model = YOLO('yolov8x.yaml').load('yolov8x.pt') # build from YAML and transfer weights model.train(data='/c...
from ultralytics import YOLO import cv2 import concurrent.futures import torch def extractImages(videoPath : str, imageFeatures : list[torch.Tensor]): capture = cv2.VideoCapture(videoPath) size =(640, 640) fps = capture.get(cv2.CAP_PROP_FPS) # get the frames of the video print(f"Frames...
與YOLOV8l.yaml一一對應: # Ultralytics YOLO , GPL-3.0 license # Parameters nc: 80 # number of classes depth_multiple: 1.00 # scales module repeats width_multiple: 1.00 # scales convolution channels # YOLOv8.0l backbone backbone: # [from, repeats, module, args] - [-1, 1, Conv, [64...
from ultralytics import YOLO model = YOLO('FastSAM-s.pt') # load a custom trained # TensorRT FP32 export # model.export(format='engine', device='0', imgsz=640) # TensorRT FP16 export model.export(format='engine', device='0', imgsz=640, half=True) ...
# Ultralytics YOLO , GPL-3.0 license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # scales module repeats width_multiple: 0.25 # scales convolution channels # YOLOv8.0n backbone backbone: # [from, repeats, module, args] - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 ...
pip install imageio[ffmpeg]importcv2importimageioimportjsonfromultralyticsimportYOLOfrompathlibimportPath# Load an official Segment model from YOLOv9model=YOLO('yolov9e-seg.pt')# define the function that changes YOLOV9 output to Person pathing API output formatdefchange_format(results,t...
YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above: from ultralytics import YOLO # Load a model model = YOLO("yolov8n.yaml") # build a new model from scratch model = YOLO("yolov8n.pt") # load a pretrained model ...