)
基于深度学习YOLO26神经网络实现菌落微生物检测和识别，其能识别检测出5种菌落微生物检测：names: ['S.aureus', 'E.coli', 'P.aeruginosa', 'C.albicans', 'B.subtilis']
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-3.0 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, model="yolo11n.pt", task=None, verbose=False): """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, verbose=verbose) self.__class__ = type(new_instance) self.__dict__ = new_instance.__dict__ else: # Continue with default YOLO initialization super().__init__(model=model, task=task, verbose=verbose) @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, model="yolov8s-world.pt", verbose=False) -> 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__(model=model, task="detect", verbose=verbose) # 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/coco8.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 it's 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/000353.jpg" # 加载预训练模型 # conf 0.25 object confidence threshold for detection # iou 0.7 intersection over union (IoU) threshold for NMS model = YOLO(path, task='detect') results = model.predict(img_path, iou=0.5) # 检测图片 res = results[0].plot() cv2.imshow("YOLO26 Detection", res) cv2.waitKey(0)第六步：整个工程的内容
包含数据集、训练代码和预测代码
项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷
https://www.bilibili.com/video/BV1WJGu6eEBV/