基于YOLOv8深度学习的学生课堂行为检测识别系统，其能识别三种学生课堂行为：names: ['举手', '读书', '写字']

具体图片见如下：

第一步：YOLOv8介绍

YOLOv8 是 ultralytics 公司在 2023 年 1月 10 号开源的 YOLOv5 的下一个重大更新版本,目前支持图像分类、物体检测和实例分割任务,在还没有开源时就收到了用户的广泛关注。

YOLOv8 算法的核心特性和改动可以归结为如下：

提供了一个全新的 SOTA 模型，包括 P5 640 和 P6 1280 分辨率的目标检测网络和基于 YOLACT 的实例分割模型。和 YOLOv5 一样，基于缩放系数也提供了 N/S/M/L/X 尺度的不同大小模型，用于满足不同场景需求

Backbone:
骨干网络和 Neck 部分可能参考了 YOLOv7 ELAN 设计思想，将 YOLOv5 的 C3 结构换成了梯度流更丰富的 C2f 结构，并对不同尺度模型调整了不同的通道数。

属于对模型结构精心微调，不再是无脑一套参数应用所有模型，大幅提升了模型性能。不过这个 C2f 模块中存在 Split 等操作对特定硬件部署没有之前那么友好了

Head： Head部分较yolov5而言有两大改进：1）换成了目前主流的解耦头结构(Decoupled-Head)，将分类和检测头分离 2）同时也从 Anchor-Based 换成了 Anchor-Free

Loss ：1) YOLOv8抛弃了以往的IOU匹配或者单边比例的分配方式，而是使用了Task-Aligned Assigner正负样本匹配方式。2）并引入了 Distribution Focal Loss(DFL)

Train:训练的数据增强部分引入了 YOLOX 中的最后 10 epoch 关闭 Mosiac 增强的操作，可以有效地提升精度

第二步：YOLOv8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（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：➷➷➷

PyTorch框架——基于深度学习YOLOv8神经网络学生课堂行为检测识别系统_哔哩哔哩_bilibili

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