第一步：准备数据
5种鸟类行为数据：self.class_indict =
["bowing_status", "grooming", "headdown", "vigilance_status", "walking"]
，总共有23790张图片，每个文件夹单独放一种数据
第二步：搭建模型
简介：
DenseNet（Dense Convolutional Network）稠密卷积网络
CVPR2017的优秀文章
从feature入手，通过对feature的极致利用达到更好的效果和更少的参数。
优点：
减轻了vanishing-gradient（梯度消失）
加强了feature的传递
更有效地利用了feature
一定程度上较少了参数数量
在深度学习网络中，随着网络深度的加深，梯度消失问题会愈加明显，解决方法是创建浅层与深层之间的短路径。在DenseNet中，在保证网络中层与层之间最大程度的信息传输的前提下，直接将所有层连接起来。
在传统卷积神经网络中，如果你有L层，那么就会有L个连接，但是在DenseNet中，会有（L+1）/2个连接。简单来说，就是每一层的输入来自前面所有层的输出。如下图是dense block的结构图，x是数据，H是网络层。
第三步：训练代码
1）损失函数为：交叉熵损失函数
2）训练代码：
import os import math import argparse import torch import torch.optim as optim from torch.utils.tensorboard import SummaryWriter from torchvision import transforms import torch.optim.lr_scheduler as lr_scheduler from model import densenet121, load_state_dict from my_dataset import MyDataSet from utils import read_split_data, train_one_epoch, evaluate def main(args): device = torch.device(args.device if torch.cuda.is_available() else "cpu") print(args) print('Start Tensorboard with "cd", view at http://localhost:6006/') tb_writer = SummaryWriter() if os.path.exists("./weights") is False: os.makedirs("./weights") train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path) data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), "val": transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])} # 实例化训练数据集 train_dataset = MyDataSet(images_path=train_images_path, images_class=train_images_label, transform=data_transform["train"]) # 实例化验证数据集 val_dataset = MyDataSet(images_path=val_images_path, images_class=val_images_label, transform=data_transform["val"]) batch_size = args.batch_size nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers print('Using {} dataloader workers every process'.format(nw)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=nw, collate_fn=train_dataset.collate_fn) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=nw, collate_fn=val_dataset.collate_fn) # 如果存在预训练权重则载入 model = densenet121(num_classes=args.num_classes).to(device) if args.weights != "": if os.path.exists(args.weights): load_state_dict(model, args.weights) else: raise FileNotFoundError("not found weights file: {}".format(args.weights)) # 是否冻结权重 if args.freeze_layers: for name, para in model.named_parameters(): # 除最后的全连接层外，其他权重全部冻结 if "classifier" not in name: para.requires_grad_(False) pg = [p for p in model.parameters() if p.requires_grad] optimizer = optim.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=1E-4, nesterov=True) # Scheduler https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf # cosine scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) for epoch in range(args.epochs): # train mean_loss = train_one_epoch(model=model, optimizer=optimizer, data_loader=train_loader, device=device, epoch=epoch) scheduler.step() # validate acc = evaluate(model=model, data_loader=val_loader, device=device) print("[epoch {}] accuracy: {}".format(epoch, round(acc, 3))) tags = ["loss", "accuracy", "learning_rate"] tb_writer.add_scalar(tags[0], mean_loss, epoch) tb_writer.add_scalar(tags[1], acc, epoch) tb_writer.add_scalar(tags[2], optimizer.param_groups[0]["lr"], epoch) torch.save(model.state_dict(), "./weights/model-{}.pth".format(epoch)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--num_classes', type=int, default=5) parser.add_argument('--epochs', type=int, default=100) parser.add_argument('--batch-size', type=int, default=16) parser.add_argument('--lr', type=float, default=0.001) parser.add_argument('--lrf', type=float, default=0.1) # 数据集所在根目录 # https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz parser.add_argument('--data-path', type=str, default=r"E:\20240717\data") # densenet121 官方权重下载地址 # https://download.pytorch.org/models/densenet121-a639ec97.pth parser.add_argument('--weights', type=str, default='densenet121.pth', help='initial weights path') parser.add_argument('--freeze-layers', type=bool, default=False) parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)') opt = parser.parse_args() main(opt)第四步：统计正确率
第五步：搭建GUI界面
视频演示地址：基于Pytorch框架的深度学习densenet121神经网络鸟类行为识别分类系统源码_哔哩哔哩_bilibili
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：➷➷➷
https://www.bilibili.com/video/BV1yvz5YXEj5/
