基于YOLOv7的动物识别系统(源码＆部署教程)

 # 1.图片识别    # 2.支持视频识别 # 3.视频演示 [[YOLOv7]基于YOLOv7的水果识别系统(源码＆部署教程)_哔哩哔哩_bilibili](https://www.bilibili.com/video/BV1FD4y1z7NP/?vd_source=bc9aec86d164b67a7004b996143742dc) # 4.准备YOLOv7格式数据集 如果不懂yolo格式数据集是什么样子的，[建议先学习一下该博客](https://afdian.net/item?plan_id=0ecd69f85fe611edaf8c52540025c377)。大部分CVer都会推荐用labelImg进行数据的标注，我也不例外，推荐大家用labelImg进行数据标注。不过这里我不再详细介绍如何使用labelImg，网上有很多的教程。同时，标注数据需要用到图形交互界面，远程服务器就不太方便了，因此建议在本地电脑上标注好后再上传到服务器上。 这里假设我们已经得到标注好的yolo格式数据集，那么这个数据集将会按照如下的格式进行存放。  不过在这里面，train_list.txt和val_list.txt是后来我们要自己生成的，而不是labelImg生成的；其他的则是labelImg生成的。 接下来，就是生成 train_list.txt和val_list.txt。train_list.txt存放了所有训练图片的路径，val_list.txt则是存放了所有验证图片的路径，如下图所示，一行代表一个图片的路径。这两个文件的生成写个循环就可以了，不算难。 # 5.修改配置文件 参考[该博客教程](https://mbd.pub/o/bread/Y5WUmZdv)，总共有两个文件需要配置，一个是/yolov7/cfg/training/yolov7.yaml，这个文件是有关模型的配置文件；一个是/yolov7/data/coco.yaml，这个是数据集的配置文件。 ## 第一步，复制yolov7.yaml文件到相同的路径下，然后重命名，我们重命名为yolov7-Helmet.yaml。 ## 第二步，打开yolov7-Helmet.yaml文件，进行如下图所示的修改，这里修改的地方只有一处，就是把nc修改为我们数据集的目标总数即可。然后保存。  ## 第三步，复制coco.yaml文件到相同的路径下，然后重命名，我们命名为Helmet.yaml。 ## 第四步，打开Helmet.yaml文件，进行如下所示的修改，需要修改的地方为5处。 第一处：把代码自动下载COCO数据集的命令注释掉，以防代码自动下载数据集占用内存；第二处：修改train的位置为train_list.txt的路径；第三处：修改val的位置为val_list.txt的路径；第四处：修改nc为数据集目标总数；第五处：修改names为数据集所有目标的名称。然后保存。  # 6.训练代码 ``` import argparse import logging import math import os import random import time from copy import deepcopy from pathlib import Path from threading import Thread import 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 tqdm import test # import test.py to get mAP after each epoch from models.experimental import attempt_load from models.yolo import Model 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, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ check_requirements, print_mutation, set_logging, one_cycle, colorstr from utils.google_utils import attempt_download from utils.loss import ComputeLoss, ComputeLossOTA 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_first, is_parallel from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume logger = logging.getLogger(__name__) def train(hyp, opt, device, tb_writer=None): logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze # Directories wdir = save_dir / 'weights' wdir.mkdir(parents=True, exist_ok=True) # make dir last = wdir / 'last.pt' best = wdir / 'best.pt' results_file = save_dir / 'results.txt' # Save run settings with 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) # Configure plots = not opt.evolve # create plots cuda = device.type != 'cpu' init_seeds(2 + rank) with open(opt.data) as f: data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict is_coco = opt.data.endswith('coco.yaml') # Logging- Doing this before checking the dataset. Might update data_dict loggers = {'wandb': None} # loggers dict if rank in [-1, 0]: opt.hyp = hyp # add hyperparameters run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) loggers['wandb'] = wandb_logger.wandb data_dict = wandb_logger.data_dict if wandb_logger.wandb: weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check # Model pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location=device) # load checkpoint model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(state_dict, strict=False) # load logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report else: model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create with torch_distributed_zero_first(rank): check_dataset(data_dict) # check train_path = data_dict['train'] test_path = data_dict['val'] # Freeze freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # parameter names to freeze (full or partial) for k, v in model.named_parameters(): v.requires_grad = True # train all layers if any(x in k for x in freeze): print('freezing %s' % k) v.requires_grad = False # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss befo