科技提升安全，基于YOLOv4开发构建商超扶梯场景下行人安全行为姿态检测识别系统

本文主要是介绍科技提升安全，基于YOLOv4开发构建商超扶梯场景下行人安全行为姿态检测识别系统，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

在商超等人流量较为密集的场景下经常会报道出现一些行人在扶梯上摔倒、受伤等问题，随着AI技术的快速发展与不断普及，越来越多的商超、地铁等场景开始加装专用的安全检测预警系统，核心工作原理即使AI模型与摄像头图像视频流的实时计算，通过对行为扶梯上的行为进行实时检测识别来对出现的危险行为进行快速预警响应避免后续出现严重的后果。本文的主要目的就是想要基于商超扶梯场景来开发构建行人安全行为检测识别系统，探索分析基于AI科技提升安全保障的可行性，本文是AI助力商超扶梯等场景安全提升的第三篇文章，前文系列如下：

《科技提升安全，基于SSD开发构建商超扶梯场景下行人安全行为姿态检测识别系统》
https://blog.csdn.net/Together_CZ/article/details/134892776《科技提升安全，基于YOLOv3开发构建商超扶梯场景下行人安全行为姿态检测识别系统》 
https://blog.csdn.net/Together_CZ/article/details/134892866

首先看下实例效果：

简单看下实例数据情况：

本文是选择的YOLOv4这一算法模型来完成本文项目的开发构建，YOLOv4是一种实时目标检测模型，它在速度和准确度上都有显著的提高。相比于其前一代模型YOLOv3，YOLOv4在保持较高的检测精度的同时，还提高了检测速度。这主要得益于其采用的CSPDarknet53网络结构，主要有三个方面的优点：增强CNN的学习能力，使得在轻量化的同时保持准确性；降低计算瓶颈；降低内存成本。YOLOv4的目标检测策略采用的是“分而治之”的策略，将一张图片平均分成7×7个网格，每个网格分别负责预测中心点落在该网格内的目标。这种方法不需要额外再设计一个区域提议网络（RPN），从而减少了训练的负担。然而，尽管YOLOv4在许多方面都表现出色，但它仍然存在一些不足。例如，小目标检测效果较差。此外，当需要在资源受限的设备上部署像YOLOv4这样的大模型时，模型压缩是研究人员重新调整较大模型所需资源消耗的有用工具。

优点：

速度：YOLOv4 保持了 YOLO 算法一贯的实时性，能够在检测速度和精度之间实现良好的平衡。
精度：YOLOv4 采用了 CSPDarknet 和 PANet 两种先进的技术，提高了检测精度，特别是在检测小型物体方面有显著提升。
通用性：YOLOv4 适用于多种任务，如行人检测、车辆检测、人脸检测等，具有较高的通用性。
模块化设计：YOLOv4 中的组件可以方便地更换和扩展，便于进一步优化和适应不同场景。

缺点：

内存占用：YOLOv4 模型参数较多，因此需要较大的内存来存储和运行模型，这对于部分硬件设备来说可能是一个限制因素。
训练成本：YOLOv4 模型需要大量的训练数据和计算资源才能达到理想的性能，这可能导致训练成本较高。
精确度与速度的权衡：虽然 YOLOv4 在速度和精度之间取得了较好的平衡，但在极端情况下，例如检测高速移动的物体或复杂背景下的物体时，性能可能会受到影响。
误检和漏检：由于 YOLOv4 采用单一网络对整个图像进行预测，可能会导致一些误检和漏检现象。

如果对如何使用yolov4项目来开发构建自己的目标检测系统有疑问的可以看我前面的超详细博文教程：

《基于官方YOLOv4开发构建目标检测模型超详细实战教程【以自建缺陷检测数据集为例】》

《基于官方YOLOv4-u5【yolov5风格实现】开发构建目标检测模型超详细实战教程【以自建缺陷检测数据集为例】》

本文的项目开发是以第一篇教程为实例进行的，当然了如果想要使用第二篇的教程本质上也都是一样的。

self.names如下：

bow
down
shake
up

self.yaml如下：

# path
train: ./dataset/images/train/
val: ./dataset/images/test/
test: ./dataset/images/test/# number of classes
nc: 4# class names
names: ['bow', 'down', 'shake', 'up']

train.py如下所示：

import argparse
import logging
import math
import os
import random
import time
from pathlib import Path
from warnings import warnimport numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
import yaml
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdmimport test  # import test.py to get mAP after each epoch
#from models.yolo import Model
from models.models import *
from utils.autoanchor import check_anchors
from utils.datasets import create_dataloader
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \fitness, fitness_p, fitness_r, fitness_ap50, fitness_ap, fitness_f, strip_optimizer, get_latest_run,\check_dataset, check_file, check_git_status, check_img_size, print_mutation, set_logging
from utils.google_utils import attempt_download
from utils.loss import compute_loss
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_firstlogger = logging.getLogger(__name__)try:import wandb
except ImportError:wandb = Nonelogger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")def train(hyp, opt, device, tb_writer=None, wandb=None):logger.info(f'Hyperparameters {hyp}')save_dir, epochs, batch_size, total_batch_size, weights, rank = \Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank# Directorieswdir = save_dir / 'weights'wdir.mkdir(parents=True, exist_ok=True)  # make dirlast = wdir / 'last.pt'best = wdir / 'best.pt'results_file = save_dir / 'results.txt'# Save run settingswith open(save_dir / 'hyp.yaml', 'w') as f:yaml.dump(hyp, f, sort_keys=False)with open(save_dir / 'opt.yaml', 'w') as f:yaml.dump(vars(opt), f, sort_keys=False)# Configureplots = not opt.evolve  # create plotscuda = device.type != 'cpu'init_seeds(2 + rank)with open(opt.data) as f:data_dict = yaml.load(f, Loader=yaml.FullLoader)  # data dictwith torch_distributed_zero_first(rank):check_dataset(data_dict)  # checktrain_path = data_dict['train']test_path = data_dict['val']nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, namesassert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check# Modelpretrained = weights.endswith('.pt')if pretrained:with torch_distributed_zero_first(rank):attempt_download(weights)  # download if not found locallyckpt = torch.load(weights, map_location=device)  # load checkpointmodel = Darknet(opt.cfg).to(device)  # createstate_dict = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}model.load_state_dict(state_dict, strict=False)print('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # reportelse:model = Darknet(opt.cfg).to(device) # create# Optimizernbs = 64  # nominal batch sizeaccumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizinghyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decaypg0, pg1, pg2 = [], [], []  # optimizer parameter groupsfor k, v in dict(model.named_parameters()).items():if '.bias' in k:pg2.append(v)  # biaseselif 'Conv2d.weight' in k:pg1.append(v)  # apply weight_decayelif 'm.weight' in k:pg1.append(v)  # apply weight_decayelif 'w.weight' in k:pg1.append(v)  # apply weight_decayelse:pg0.append(v)  # all elseif opt.adam:optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentumelse:optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decayoptimizer.add_param_group({'params': pg2})  # add pg2 (biases)logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))del pg0, pg1, pg2# Scheduler https://arxiv.org/pdf/1812.01187.pdf# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLRlf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf']  # cosinescheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)# plot_lr_scheduler(optimizer, scheduler, epochs)# Loggingif wandb and wandb.run is None:opt.hyp = hyp  # add hyperparameterswandb_run = wandb.init(config=opt, resume="allow",project='YOLOv4' if opt.project == 'runs/train' else Path(opt.project).stem,name=save_dir.stem,id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)# Resumestart_epoch, best_fitness = 0, 0.0best_fitness_p, best_fitness_r, best_fitness_ap50, best_fitness_ap, best_fitness_f = 0.0, 0.0, 0.0, 0.0, 0.0if pretrained:# Optimizerif ckpt['optimizer'] is not None:optimizer.load_state_dict(ckpt['optimizer'])best_fitness = ckpt['best_fitness']best_fitness_p = ckpt['best_fitness_p']best_fitness_r = ckpt['best_fitness_r']best_fitness_ap50 = ckpt['best_fitness_ap50']best_fitness_ap = ckpt['best_fitness_ap']best_fitness_f = ckpt['best_fitness_f']# Resultsif ckpt.get('training_results') is not None:with open(results_file, 'w') as file:file.write(ckpt['training_results'])  # write results.txt# Epochsstart_epoch = ckpt['epoch'] + 1if opt.resume:assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)if epochs < start_epoch:logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %(weights, ckpt['epoch'], epochs))epochs += ckpt['epoch']  # finetune additional epochsdel ckpt, state_dict# Image sizesgs = 64 #int(max(model.stride))  # grid size (max stride)imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples# DP modeif cuda and rank == -1 and torch.cuda.device_count() > 1:model = torch.nn.DataParallel(model)# SyncBatchNormif opt.sync_bn and cuda and rank != -1:model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)logger.info('Using SyncBatchNorm()')# EMAema = ModelEMA(model) if rank in [-1, 0] else None# DDP modeif cuda and rank != -1:model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)# Trainloaderdataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,rank=rank, world_size=opt.world_size, workers=opt.workers)mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label classnb = len(dataloader)  # number of batchesassert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)# Process 0if rank in [-1, 0]:ema.updates = start_epoch * nb // accumulate  # set EMA updatestestloader = create_dataloader(test_path, imgsz_test, batch_size*2, gs, opt,hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloaderif not opt.resume:labels = np.concatenate(dataset.labels, 0)c = torch.tensor(labels[:, 0])  # classes# cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency# model._initialize_biases(cf.to(device))if plots:plot_labels(labels, save_dir=save_dir)if tb_writer:tb_writer.add_histogram('classes', c, 0)if wandb:wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.png')]})# Anchors# if not opt.noautoanchor:#     check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)# Model parametershyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current datasetmodel.nc = nc  # attach number of classes to modelmodel.hyp = hyp  # attach hyperparameters to modelmodel.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weightsmodel.names = names# Start trainingt0 = time.time()nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)# nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of trainingmaps = np.zeros(nc)  # mAP per classresults = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)scheduler.last_epoch = start_epoch - 1  # do not movescaler = amp.GradScaler(enabled=cuda)logger.info('Image sizes %g train, %g test\n''Using %g dataloader workers\nLogging results to %s\n''Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))torch.save(model, wdir / 'init.pt')for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------model.train()# Update image weights (optional)if opt.image_weights:# Generate indicesif rank in [-1, 0]:cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weightsiw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weightsdataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx# Broadcast if DDPif rank != -1:indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()dist.broadcast(indices, 0)if rank != 0:dataset.indices = indices.cpu().numpy()# Update mosaic border# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)# dataset.mosaic_border = [b - imgsz, -b]  # height, width bordersmloss = torch.zeros(4, device=device)  # mean lossesif rank != -1:dataloader.sampler.set_epoch(epoch)pbar = enumerate(dataloader)logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))if rank in [-1, 0]:pbar = tqdm(pbar, total=nb)  # progress baroptimizer.zero_grad()for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------ni = i + nb * epoch  # number integrated batches (since train start)imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0# Warmupif ni <= nw:xi = [0, nw]  # x interp# model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())for j, x in enumerate(optimizer.param_groups):# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])if 'momentum' in x:x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])# Multi-scaleif opt.multi_scale:sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # sizesf = sz / max(imgs.shape[2:])  # scale factorif sf != 1:ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)# Forwardwith amp.autocast(enabled=cuda):pred = model(imgs)  # forwardloss, loss_items = compute_loss(pred, targets.to(device), model)  # loss scaled by batch_sizeif rank != -1:loss *= opt.world_size  # gradient averaged between devices in DDP mode# Backwardscaler.scale(loss).backward()# Optimizeif ni % accumulate == 0:scaler.step(optimizer)  # optimizer.stepscaler.update()optimizer.zero_grad()if ema:ema.update(model)# Printif rank in [-1, 0]:mloss = (mloss * i + loss_items) / (i + 1)  # update mean lossesmem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)s = ('%10s' * 2 + '%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])pbar.set_description(s)# Plotif plots and ni < 3:f = save_dir / f'train_batch{ni}.jpg'  # filenameplot_images(images=imgs, targets=targets, paths=paths, fname=f)# if tb_writer:#     tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)#     tb_writer.add_graph(model, imgs)  # add model to tensorboardelif plots and ni == 3 and wandb:wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]})# end batch ------------------------------------------------------------------------------------------------# end epoch ----------------------------------------------------------------------------------------------------# Schedulerlr = [x['lr'] for x in optimizer.param_groups]  # for tensorboardscheduler.step()# DDP process 0 or single-GPUif rank in [-1, 0]:# mAPif ema:ema.update_attr(model)final_epoch = epoch + 1 == epochsif not opt.notest or final_epoch:  # Calculate mAPif epoch >= 3:results, maps, times = test.test(opt.data,batch_size=batch_size*2,imgsz=imgsz_test,model=ema.ema.module if hasattr(ema.ema, 'module') else ema.ema,single_cls=opt.single_cls,dataloader=testloader,save_dir=save_dir,plots=plots and final_epoch,log_imgs=opt.log_imgs if wandb else 0)# Writewith open(results_file, 'a') as f:f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)if len(opt.name) and opt.bucket:os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))# Logtags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95','val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss'x/lr0', 'x/lr1', 'x/lr2']  # paramsfor x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):if tb_writer:tb_writer.add_scalar(tag, x, epoch)  # tensorboardif wandb:wandb.log({tag: x})  # W&B# Update best mAPfi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]fi_p = fitness_p(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]fi_r = fitness_r(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]fi_ap50 = fitness_ap50(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]fi_ap = fitness_ap(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]if (fi_p > 0.0) or (fi_r > 0.0):fi_f = fitness_f(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]else:fi_f = 0.0if fi > best_fitness:best_fitness = fiif fi_p > best_fitness_p:best_fitness_p = fi_pif fi_r > best_fitness_r:best_fitness_r = fi_rif fi_ap50 > best_fitness_ap50:best_fitness_ap50 = fi_ap50if fi_ap > best_fitness_ap:best_fitness_ap = fi_apif fi_f > best_fitness_f:best_fitness_f = fi_f# Save modelsave = (not opt.nosave) or (final_epoch and not opt.evolve)if save:with open(results_file, 'r') as f:  # create checkpointckpt = {'epoch': epoch,'best_fitness': best_fitness,'best_fitness_p': best_fitness_p,'best_fitness_r': best_fitness_r,'best_fitness_ap50': best_fitness_ap50,'best_fitness_ap': best_fitness_ap,'best_fitness_f': best_fitness_f,'training_results': f.read(),'model': ema.ema.module.state_dict() if hasattr(ema, 'module') else ema.ema.state_dict(),'optimizer': None if final_epoch else optimizer.state_dict(),'wandb_id': wandb_run.id if wandb else None}# Save last, best and deletetorch.save(ckpt, last)if best_fitness == fi:torch.save(ckpt, best)if (best_fitness == fi) and (epoch >= 200):torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch))if best_fitness == fi:torch.save(ckpt, wdir / 'best_overall.pt')if best_fitness_p == fi_p:torch.save(ckpt, wdir / 'best_p.pt')if best_fitness_r == fi_r:torch.save(ckpt, wdir / 'best_r.pt')if best_fitness_ap50 == fi_ap50:torch.save(ckpt, wdir / 'best_ap50.pt')if best_fitness_ap == fi_ap:torch.save(ckpt, wdir / 'best_ap.pt')if best_fitness_f == fi_f:torch.save(ckpt, wdir / 'best_f.pt')if epoch == 0:torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))if ((epoch+1) % 25) == 0:torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))if epoch >= (epochs-5):torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))elif epoch >= 420: torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))del ckpt# end epoch ----------------------------------------------------------------------------------------------------# end trainingif rank in [-1, 0]:# Strip optimizersn = opt.name if opt.name.isnumeric() else ''fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):if f1.exists():os.rename(f1, f2)  # renameif str(f2).endswith('.pt'):  # is *.ptstrip_optimizer(f2)  # strip optimizeros.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None  # upload# Finishif plots:plot_results(save_dir=save_dir)  # save as results.pngif wandb:wandb.log({"Results": [wandb.Image(str(save_dir / x), caption=x) for x in['results.png', 'precision-recall_curve.png']]})logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))else:dist.destroy_process_group()wandb.run.finish() if wandb and wandb.run else Nonetorch.cuda.empty_cache()return resultsif __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default='weights/yolov4-tiny.weights', help='initial weights path')parser.add_argument('--cfg', type=str, default='cfg/yolov4-tiny.cfg', help='model.yaml path')parser.add_argument('--data', type=str, default='data/self.yaml', help='data.yaml path')parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')parser.add_argument('--epochs', type=int, default=100)parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')parser.add_argument('--rect', action='store_true', help='rectangular training')parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')parser.add_argument('--notest', action='store_true', help='only test final epoch')parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')parser.add_argument('--project', default='runs/train', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')opt = parser.parse_args()# Set DDP variablesopt.total_batch_size = opt.batch_sizeopt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1set_logging(opt.global_rank)if opt.global_rank in [-1, 0]:check_git_status()# Resumeif opt.resume:  # resume an interrupted runckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent pathassert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'with open(Path(ckpt).parent.parent / 'opt.yaml') as f:opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader))  # replaceopt.cfg, opt.weights, opt.resume = '', ckpt, Truelogger.info('Resuming training from %s' % ckpt)else:# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check filesassert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)opt.name = 'evolve' if opt.evolve else opt.nameopt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)  # increment run# DDP modedevice = select_device(opt.device, batch_size=opt.batch_size)if opt.local_rank != -1:assert torch.cuda.device_count() > opt.local_ranktorch.cuda.set_device(opt.local_rank)device = torch.device('cuda', opt.local_rank)dist.init_process_group(backend='nccl', init_method='env://')  # distributed backendassert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'opt.batch_size = opt.total_batch_size // opt.world_size# Hyperparameterswith open(opt.hyp) as f:hyp = yaml.load(f, Loader=yaml.FullLoader)  # load hypsif 'box' not in hyp:warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %(opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))hyp['box'] = hyp.pop('giou')# Trainlogger.info(opt)if not opt.evolve:tb_writer = None  # init loggersif opt.global_rank in [-1, 0]:logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')tb_writer = SummaryWriter(opt.save_dir)  # Tensorboardtrain(hyp, opt, device, tb_writer, wandb)# Evolve hyperparameters (optional)else:# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr'box': (1, 0.02, 0.2),  # box loss gain'cls': (1, 0.2, 4.0),  # cls loss gain'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight'iou_t': (0, 0.1, 0.7),  # IoU training threshold'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)'scale': (1, 0.0, 0.9),  # image scale (+/- gain)'shear': (1, 0.0, 10.0),  # image shear (+/- deg)'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)'mosaic': (1, 0.0, 1.0),  # image mixup (probability)'mixup': (1, 0.0, 1.0)}  # image mixup (probability)assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'opt.notest, opt.nosave = True, True  # only test/save final epoch# ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indicesyaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml'  # save best result hereif opt.bucket:os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if existsfor _ in range(300):  # generations to evolveif Path('evolve.txt').exists():  # if evolve.txt exists: select best hyps and mutate# Select parent(s)parent = 'single'  # parent selection method: 'single' or 'weighted'x = np.loadtxt('evolve.txt', ndmin=2)n = min(5, len(x))  # number of previous results to considerx = x[np.argsort(-fitness(x))][:n]  # top n mutationsw = fitness(x) - fitness(x).min()  # weightsif parent == 'single' or len(x) == 1:# x = x[random.randint(0, n - 1)]  # random selectionx = x[random.choices(range(n), weights=w)[0]]  # weighted selectionelif parent == 'weighted':x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination# Mutatemp, s = 0.8, 0.2  # mutation probability, sigmanpr = np.randomnpr.seed(int(time.time()))g = np.array([x[0] for x in meta.values()])  # gains 0-1ng = len(meta)v = np.ones(ng)while all(v == 1):  # mutate until a change occurs (prevent duplicates)v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)hyp[k] = float(x[i + 7] * v[i])  # mutate# Constrain to limitsfor k, v in meta.items():hyp[k] = max(hyp[k], v[1])  # lower limithyp[k] = min(hyp[k], v[2])  # upper limithyp[k] = round(hyp[k], 5)  # significant digits# Train mutationresults = train(hyp.copy(), opt, device, wandb=wandb)# Write mutation resultsprint_mutation(hyp.copy(), results, yaml_file, opt.bucket)# Plot resultsplot_evolution(yaml_file)print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')

终端执行即可启动训练，本文选择的是tiny系列的模型来进行开发训练的，终端日志输出如下所示：

结果文件如下所示：

weights目录如下所示：

Label数据可视化如下所示：

整体训练过程可视化如下所示：

【PR曲线】
精确率-召回率曲线（Precision-Recall Curve）是一种用于评估二分类模型性能的可视化工具。它通过绘制不同阈值下的精确率（Precision）和召回率（Recall）之间的关系图来帮助我们了解模型在不同阈值下的表现。
精确率是指被正确预测为正例的样本数占所有预测为正例的样本数的比例。召回率是指被正确预测为正例的样本数占所有实际为正例的样本数的比例。
绘制精确率-召回率曲线的步骤如下：
使用不同的阈值将预测概率转换为二进制类别标签。通常，当预测概率大于阈值时，样本被分类为正例，否则分类为负例。
对于每个阈值，计算相应的精确率和召回率。
将每个阈值下的精确率和召回率绘制在同一个图表上，形成精确率-召回率曲线。
根据曲线的形状和变化趋势，可以选择适当的阈值以达到所需的性能要求。
精确率-召回率曲线提供了更全面的模型性能分析，特别适用于处理不平衡数据集和关注正例预测的场景。曲线下面积（Area Under the Curve, AUC）可以作为评估模型性能的指标，AUC值越高表示模型的性能越好。
通过观察精确率-召回率曲线，我们可以根据需求选择合适的阈值来权衡精确率和召回率之间的平衡点。根据具体的业务需求和成本权衡，可以在曲线上选择合适的操作点或阈值。