在现代社会生活与生产活动下，不可避免的会产生巨量且多样的垃圾。我国的人口和经济总量均位居世界前列，因此，必然面临着庞大数量的垃圾处理的难题。如何通过人工智能来对垃圾进行有效分类，成为当前备受关注的研究热点。本文为展开基于深度网络的垃圾识别与分类算法研究，先使用PyTorch框架中的transforms方法对数据进行预处理操作，后经过多次调参实验，对比朴素贝叶斯模型、Keras卷积神经网络模型、ResNeXt101模型的垃圾分类效果。确定最佳分类模型是ResNeXt101，该模型在GPU环境下的分类准确率达到了94.7%。最后利用postman软件来测试API接口，完成图片的在线预测。在微信开发者工具的基础上，利用一些天行数据的垃圾分类的API接口再结合最佳模型的API接口，开发出了一个垃圾分类微信小程序。本文的研究内容丰富和完善了垃圾图像分类的相关研究，也为后续的研究提供了一定的参考价值。

部分源码文件：

# 导入torch 相关包
import time

import torch
import torch.nn as nn

# 导入模型定义方法
import models
# 导入工具类
from utils.eval import accuracy
from utils.misc import AverageMeter
import numpy as np
# 导入进度条库
from progress.bar import Bar
#from progressbar import Bar
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def train(train_loader, model, criterion, optimizer):
    '''
    模型训练
    :param train_loader:
    :param model:
    :param criterion:
    :param optimizer:
    :return:
    '''
    # 定义保存更新变量
    data_time = AverageMeter()
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    end = time.time()

    #################
    # train the model
    #################
    model.train()

    # 训练每批数据，然后进行模型的训练
    ## 定义bar 变量
    bar = Bar('Processing',max = len(train_loader))
    for batch_index, (inputs, targets) in enumerate(train_loader):
        data_time.update(time.time() - end)
        # move tensors to GPU if cuda is_available
        inputs, targets = inputs.to(device), targets.to(device)
        # 在进行反向传播之前，我们使用zero_grad方法清空梯度
        optimizer.zero_grad()
        # 模型的预测
        outputs = model(inputs)
        # 计算loss
        loss = criterion(outputs, targets)
        # backward pass:
        loss.backward()
        # perform as single optimization step (parameter update)
        optimizer.step()

        # 计算acc和变量更新
        prec1, _ = accuracy(outputs.data, targets.data, topk=(1, 1))
        losses.update(loss.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        batch_time.update(time.time() - end)
        end = time.time()

        # plot progress
        ## 把主要的参数打包放进bar中
        # plot progress
        bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f}'.format(
            batch=batch_index + 1,
            size=len(train_loader),
            data=data_time.val,
            bt=batch_time.val,
            total=bar.elapsed_td,
            eta=bar.eta_td,
            loss=losses.avg,
            top1=top1.avg
        )
        bar.next()
    bar.finish()
    return (losses.avg, top1.avg)


def evaluate(val_loader,model, criterion,test = None):
    '''
    模型评估
    :param val_loader:
    :param model:
    :param criterion:
    :param test:
    :return:
    '''

    global best_acc

    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    predict_all = np.array([],dtype=int)
    labels_all = np.array([],dtype=int)

    #################
    # val the model
    #################
    model.eval()
    end = time.time()

    # 训练每批数据，然后进行模型的训练
    ## 定义bar 变量
    bar = Bar('Processing', max=len(val_loader))
    for batch_index, (inputs, targets) in enumerate(val_loader):
        data_time.update(time.time() - end)
        # move tensors to GPU if cuda is_available
        inputs, targets = inputs.to(device), targets.to(device)
        # 模型的预测
        outputs = model(inputs)
        # 计算loss
        loss = criterion(outputs, targets)

        # 计算acc和变量更新
        prec1, _ = accuracy(outputs.data, targets.data, topk=(1, 1))
        losses.update(loss.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        batch_time.update(time.time() - end)
        end = time.time()

        # 评估混淆矩阵的数据
        targets = targets.data.cpu().numpy() # 真实数据的y数值
        predic = torch.max(outputs.data,1)[1].cpu().numpy() # 预测数据y数值
        labels_all = np.append(labels_all,targets) # 数据赋值
        predict_all = np.append(predict_all,predic)

        ## 把主要的参数打包放进bar中
        # plot progress
        bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f}'.format(
            batch=batch_index + 1,
            size=len(val_loader),
            data=data_time.val,
            bt=batch_time.val,
            total=bar.elapsed_td,
            eta=bar.eta_td,
            loss=losses.avg,
            top1=top1.avg
        )
        bar.next()
    bar.finish()

    if test:
        return (losses.avg, top1.avg,predict_all,labels_all)
    else:
        return (losses.avg, top1.avg)


def set_parameter_requires_grad(model, feature_extract):
    '''

    :param model:  模型
    :param feature_extract: true 固定特征抽取层
    :return:
    '''
    if feature_extract:
        for param in model.parameters():
            # 不需要更新梯度，冻结某些层的梯度
            param.requires_grad = False


def initital_model(model_name, num_classes, feature_extract=True):
    """
    基于提供的pre_trained_model 进行初始化
    :param model_name:
    提供的模型名称，例如： resnext101_32x16d/resnext101_32x8d..
    :param num_classes: 图片分类个数
    :param feature_extract: 设置true ，固定特征提取层，优化全连接的分类器
    :return:
    """

    model_ft = None

    if model_name == 'resnext101_32x16d':
        # 加载facebook pre_trained_model resnext101,默认1000 类
        model_ft = models.resnext101_32x16d_wsl()
        # 设置 固定特征提取层
        set_parameter_requires_grad(model_ft, feature_extract)

        # 调整分类个数
        num_ftrs = model_ft.fc.in_features
        # 修改fc 的分类个数
        model_ft.fc = nn.Sequential(
            nn.Dropout(0.2),  # 防止过拟合
            nn.Linear(in_features=num_ftrs, out_features=num_classes)
        )
    elif model_name == 'resnext101_32x8d':
        # 加载facebook pre_trained_model resnext101,默认1000 类
        model_ft = models.resnext101_32x8d()
        # 设置 固定特征提取层
        set_parameter_requires_grad(model_ft, feature_extract)

        # 调整分类个数
        num_ftrs = model_ft.fc.in_features
        # 修改fc 的分类个数
        model_ft.fc = nn.Sequential(
            nn.Dropout(0.2),  # 防止过拟合
            nn.Linear(in_features=num_ftrs, out_features=num_classes)
        )

    else:
        print('Invalid model name,exiting..')
        exit()

    return model_ft


import codecs
def class_id2name():
    '''
    标签关系映射
    :return:
    '''

    clz_id2name = {}

    for line in codecs.open('data/garbage_label.txt','r',encoding='utf-8'):
        line = line.strip()
        _id = line.split(":")[0]
        _name = line.split(":")[1]
        clz_id2name[int(_id)] = _name
    return clz_id2name

项目结构：
本项目适合做计算机相关专业的毕业设计，[课程设计]，技术难度适中、工作量比较充实。