需要一个改进模型，提高准确率的方案

问题遇到的现象和发生背景

我正在做一个图像识别的项目（是一个识别十种手势的CNN算法，学习率在0.001的时候准确率才刚刚达到82.8%）
这个是数据集的来源：https://www.kaggle.com/datasets/gti-upm/leapgestrecog

问题相关代码，请勿粘贴截图

# -*- coding: utf-8 -*-
"""
Created on Fri Jul 22 10:26:33 2022
@author: Blucoris Liang
"""

import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import math

EPOCH=30
BATCH_SIZE=40
LR = 0.001

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                              std=[0.229, 0.224, 0.225])   

train_dataset = datasets.ImageFolder(
        'C:\\Users\\19544\\.spyder-py3\\leapGestRecog\\00',
        transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
                ]))

train_loader = Data.DataLoader(
        train_dataset,
        batch_size=BATCH_SIZE,
        shuffle=True)

test_loader = Data.DataLoader(
        datasets.ImageFolder(
                'C:\\Users\\19544\\.spyder-py3\\leapGestRecog\\03', 
                transforms.Compose([
                        transforms.Resize(256),
                        transforms.CenterCrop(224),
                        transforms.ToTensor(),
                        normalize,
                        ])),
        batch_size=BATCH_SIZE, shuffle=False,)

# 数据集长度
train_data_size = len(train_dataset) 
print('训练集的长度为:{}'.format(train_data_size))  



model = models.resnet50(pretrained=True)

################################
if torch.cuda.is_available():  #
    model = models.resnet50(pretrained=True).cuda()   #
################################



model.fc = torch.nn.Linear(in_features=512, out_features=10, bias=True).cuda()

fc_params = list(map(id, model.fc.parameters())) # map函数是将fc.parameters()的id返回并组成一个列表

base_params = filter(lambda p: id(p) not in fc_params, model.parameters()) # filter函数是将model.parameters()中地址不在fc.parameters的id中的滤出来

optimizer = torch.optim.SGD([ {'params': base_params}, {'params': model.fc.parameters(), 'lr': LR * 100}], lr=LR)

loss_func=nn.CrossEntropyLoss()

################################
if torch.cuda.is_available():  #
    loss_func = loss_func.cuda()   #
################################


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)
            
def accuracy(output, target, topk=(1,)):
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res           
            
train_losses = AverageMeter('TrainLoss', ':.4e')
train_top1 = AverageMeter('TrainAccuracy', ':6.2f')
test_losses = AverageMeter('TestLoss', ':.4e')
test_top1 = AverageMeter('TestAccuracy', ':6.2f')

for epoch in range(EPOCH):
    
    model.train()
    for i,(images,target) in enumerate(train_loader):
        ################################
        if torch.cuda.is_available():  #
            images = images.cuda()         #
            target = target.cuda()   #
        ################################
        output=model(images)
        ################################
        if torch.cuda.is_available():  #
            output = output.cuda()   #
        ################################
        loss= loss_func(output,target)
        
        acc1, = accuracy(output, target, topk=(1,))
        train_losses.update(loss.item(), images.size(0))
        train_top1.update(acc1[0], images.size(0))
        loss.backward(retain_graph=True)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        
        print('Epoch[{}/{}],TrainLoss:{}, TrainAccuracy:{}'.format(epoch,EPOCH,train_losses.val, train_top1.val))
           
    model.eval()
    with torch.no_grad():
        for i,(images,target) in enumerate(test_loader):
            ################################
            if torch.cuda.is_available():  #
                images = images.cuda()    #
                target = target.cuda()   #
            ################################
            output=model(images)
            loss= loss_func(output,target)
            
            acc1, = accuracy(output, target, topk=(1,))
            test_losses.update(loss.item(), images.size(0))
            test_top1.update(acc1[0], images.size(0))
            
    print('TestLoss:{}, TestAccuracy:{}'.format(test_losses.avg, test_top1.avg))

运行结果及报错内容

以上是我的代码，正确率只能达到大概82.8%

我的解答思路和尝试过的方法

目前已经作出了了调学习率、调训练轮数的尝试，但是对准确率的提升帮助不大

我想要达到的结果

希望有人给我提供一个具体的模型改进方向，使得识别准确率达到90%以上

这里有很多开源的模型，可以参考下

如果不建议使用什么框架的话，提供数据集，可以帮你完成