核心内容摘要
国产PLM软件的实施周期多久
YOLO26手势识别项目实战2-石头剪刀布实时检测系统其能识别检测出3种手势手语names: [Rock,Paper,Scissors]具体图片见如下第一步YOLO26介绍YOLO26采用了端到端无NMS推理直接生成预测结果无需非极大值抑制NMS后处理。
这种设计减少了延迟简化了集成并提高了部署效率。
此外YOLO26移除了分布焦点损失DFL从而增强了硬件兼容性特别是在边缘设备上的表现。
模型还引入了ProgLoss和小目标感知标签分配STAL显著提升了小目标检测的精度。
这对于物联网、机器人技术和航空影像等应用至关重要。
同时YOLO26采用了全新的MuSGD优化器结合了SGD和Muon优化技术提供更稳定的训练和更快的收敛速度。
第二步YOLO26网络结构第三步代码展示# Ultralytics YOLO , AGPL-
0 license from pathlib import Path from ultralytics.engine.model import Model from ultralytics.models import yolo from ultralytics.nn.tasks import ClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModel from ultralytics.utils import ROOT, yaml_load class YOLO(Model): YOLO (You Only Look Once) object detection model. def __init__(self, modelyolo11n.pt, taskNone, verboseFalse): Initialize YOLO model, switching to YOLOWorld if model filename contains -world. path Path(model) if -world in path.stem and path.suffix in {.pt, .yaml, .yml}: # if YOLOWorld PyTorch model new_instance YOLOWorld(path, verboseverbose) self.__class__ type(new_instance) self.__dict__ new_instance.__dict__ else: # Continue with default YOLO initialization super().__init__(modelmodel, tasktask, verboseverbose) property def task_map(self): Map head to model, trainer, validator, and predictor classes. return { classify: { model: ClassificationModel, trainer: yolo.classify.ClassificationTrainer, validator: yolo.classify.ClassificationValidator, predictor: yolo.classify.ClassificationPredictor, }, detect: { model: DetectionModel, trainer: yolo.detect.DetectionTrainer, validator: yolo.detect.DetectionValidator, predictor: yolo.detect.DetectionPredictor, }, segment: { model: SegmentationModel, trainer: yolo.segment.SegmentationTrainer, validator: yolo.segment.SegmentationValidator, predictor: yolo.segment.SegmentationPredictor, }, pose: { model: PoseModel, trainer: yolo.pose.PoseTrainer, validator: yolo.pose.PoseValidator, predictor: yolo.pose.PosePredictor, }, obb: { model: OBBModel, trainer: yolo.obb.OBBTrainer, validator: yolo.obb.OBBValidator, predictor: yolo.obb.OBBPredictor, }, } class YOLOWorld(Model): YOLO-World object detection model. def __init__(self, modelyolov8s-world.pt, verboseFalse) - None: Initialize YOLOv8-World model with a pre-trained model file. Loads a YOLOv8-World model for object detection. If no custom class names are provided, it assigns default COCO class names. Args: model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats. verbose (bool): If True, prints additional information during initialization. super().__init__(modelmodel, taskdetect, verboseverbose) # Assign default COCO class names when there are no custom names if not hasattr(self.model, names): self.model.names yaml_load(ROOT / cfg/datasets/coco
yaml).get(names) property def task_map(self): Map head to model, validator, and predictor classes. return { detect: { model: WorldModel, validator: yolo.detect.DetectionValidator, predictor: yolo.detect.DetectionPredictor, trainer: yolo.world.WorldTrainer, } } def set_classes(self, classes): Set classes. Args: classes (List(str)): A list of categories i.e. [person]. self.model.set_classes(classes) # Remove background if its given background if background in classes: classes.remove(background) self.model.names classes # Reset method class names # self.predictor None # reset predictor otherwise old names remain if self.predictor: self.predictor.model.names classes第四步统计训练过程的一些指标相关指标都有第五步运行预测代码#coding:utf-8 from ultralytics import YOLO import cv2 # 所需加载的模型目录 path models/best.pt # 需要检测的图片地址 img_path TestFiles/
jpg # 加载预训练模型 # conf
25 object confidence threshold for detection # iou
7 intersection over union (IoU) threshold for NMS model YOLO(path, taskdetect) results model.predict(img_path, iou
0.