验证码检测和识别3：基于深度学习YOLO26神经网络实现验证码检测和识别(含训练代码、数据集和GUI交互界面）
基于深度学习YOLO26神经网络实现验证码检测和识别，其能识别检测出1种验证码检测：names: ['captcha']
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 licensefrompathlibimportPathfromultralytics.engine.modelimportModelfromultralytics.modelsimportyolofromultralytics.nn.tasksimportClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModelfromultralytics.utilsimportROOT, yaml_loadclassYOLO(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"inpath.stemandpath.suffixin{".pt",".yaml",".yml"}:# if YOLOWorld PyTorch modelnew_instance = YOLOWorld(path, verbose=verbose) self.__class__ =type(new_instance) self.__dict__ = new_instance.__dict__else:# Continue with default YOLO initializationsuper().__init__(model=model, task=task, verbose=verbose)@propertydeftask_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, }, }classYOLOWorld(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 namesifnothasattr(self.model,"names"): self.model.names = yaml_load(ROOT /"cfg/datasets/coco8.yaml").get("names")@propertydeftask_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, } }defset_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 givenbackground =" "ifbackgroundinclasses: classes.remove(background) self.model.names = classes# Reset method class names# self.predictor = None # reset predictor otherwise old names remainifself.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（支持图片、文件夹、摄像头和视频功能）
第六步：整个工程的内容
有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码
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/BV1rzTT65EZu/