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香蕉成熟度检测和识别2：基于深度学习YOLOv5神经网络实现香蕉成熟度检测和识别(含训练代码和数据集)
基于深度学习YOLOv5神经网络实现香蕉成熟度检测和识别，其能识别检测出香蕉成熟度：names = {0:'freshripe', 1:'freshunripe', 2:'overripe', 3:'ripe', 4:'rotten', 5:'unripe'} CH_names = ["新鲜","未成熟", "过熟", "熟透", "腐烂", "未熟透"]
具体图片见如下：
第一步：YOLOv5介绍
YOLOv5是一种目标检测算法，它是YOLO（You Only Look Once）系列的最新版本。YOLOv5在YOLOv4的基础上进行了改进和优化，以提高检测的准确性和速度。
YOLOv5采用了一些新的技术和方法来改进目标检测的性能。其中包括以下几个方面：
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损失函数：YOLOv5使用了CIOU_Loss作为bounding box的损失函数。CIOU_Loss是一种改进的IOU_Loss，可以更好地衡量目标框的位置和大小。
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非极大值抑制（NMS）：YOLOv5使用NMS来抑制重叠的边界框，以减少重复检测的问题。
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聚类anchors：YOLOv5使用k-means聚类算法来生成anchors，这些anchors用于检测不同尺度的目标。
总的来说，YOLOv5在YOLOv4的基础上进行了一些改进和优化，以提高目标检测的准确性和速度。
标注数据，YOLOv5的训练和测试步骤，可以参考我的这篇博客：手把手教你通过YOLOv5训练自己的目标检测模型_yolov5怎么测试自己训练的结果-CSDN博客
第二步：YOLOv5网络结构
第三步：代码展示
# Ultralytics YOLO 🚀, AGPL-3.0 licensefrom pathlib import Pathfrom 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_loadclass 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 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)@propertydef 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 defaultCOCO 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 namesif not hasattr(self.model, "names"):self.model.names = yaml_load(ROOT / "cfg/datasets/coco8.yaml").get("names")@propertydef 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 givenbackground = " "if background in classes:classes.remove(background)self.model.names = classes# Reset method class names# self.predictor = None # reset predictor otherwise old names remainif 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 = "musa-acuminata-banana-9d0d6b30-4037-11ec-b5aa-94b86d66fd1d_jpg.rf.b8c6f66651835d01dc8bd851c72537c7.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')
# model = YOLO(path, task='detect',conf=0.5)# 检测图片
results = model(img_path)
res = results[0].plot()
cv2.imshow("YOLOv5 Detection", res)
cv2.waitKey(0)
第六步：整个工程的内容
香蕉成熟度数据集、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：➷➷➷
基于深度学习YOLOv5神经网络实现香蕉成熟度检测和识别(含训练代码、数据集和GUI交互界面)_哔哩哔哩_bilibili
