YOLO的算法核心是什么？能列举代码？

YOLO的算法核心是基于深度学习神经网络的目标检测方法。其核心代码包括网络架构和训练过程，具体代码现在已经更新到了YOLOv5版本。这里给出一部分的代码作为参考：

from models.yolo import Model
from utils.general import (
    check_img_size, non_max_suppression, apply_classifier, scale_coords,
    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
...

class Detect(object):
    def __init__(self, source, weights, imgsz=640, conf_thres=0.4, iou_thres=0.5, augment=False, device=''):
        ...
        # Initialize model
        model = Model(cfg['arch'], ch=cfg['ch'], nc=nc, anchors=anchors, gd=cfg['gr'], gw=cfg['gw']).to(device)

        # Load weights
        if weights.endswith('.pt'):  # pytorch format
            model.load_state_dict(torch.load(weights, map_location=device)['model'].float().state_dict())
        else:  # darknet format
            _ = load_darknet_weights(model, weights)

        # Eval mode
        model.eval()

        # Fuse Conv2d + BatchNorm2d layers
        # model.fuse()

        # Set Dataloader
        vid_path, vid_writer = None, None
        if source.isnumeric():  # 支持采集卡直接读取图像流
            ...