如何提高PyTorch的人工智能在图像分类器中的准确性？

Question

我正在尝试建立一个强大的图像分类器。但我有个问题。我使用CIFRAS-100数据集，并根据它训练了一个模型。请注意，正确的分类数等于15%。我试着继续学习的过程，但经过2-3次尝试，模型没有改变.

用于培训的代码：

import torch
import sys,os
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

trainset = torchvision.datasets.CIFAR100(root='./dataone', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR100(root='./dataone', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)
classes = ('aquatic mammals','fish','flowers','food containers','fruit and vegetables','household electrical devices','household furniture','insects','large carnivores','large man-made outdoor things','large natural outdoor scenes','large omnivores and herbivores','medium-sized mammals','non-insect invertebrates','people','reptiles','small mammals','trees','vehicles 1','vehicles 2')
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 100)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
import torch.optim as optim
PATH = "./model.pt"
model = Net()
net = Net()
print(os.path.exists(PATH))
if os.path.exists(PATH):
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
    checkpoint = torch.load(PATH)
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    epoch = checkpoint['epoch']
    loss = checkpoint['loss']
    print("using checkpoint")
    #model.eval()
    # - or -
    model.train()

#criterion = nn.CrossEntropyLoss()
#optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        print("training..")
        # print statistics
        #running_loss += loss.item()
        #if i % 2000 == 1999:    # print every 2000 mini-batches
        #    print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
        #    running_loss = 0.0

print('Finished Training')

#PATH = './cifar_net.pth'
#torch.save(net.state_dict(), PATH)

EPOCH = 5

LOSS = 0.4

torch.save({
            'epoch': EPOCH,
            'model_state_dict': net.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'loss': LOSS,
            }, PATH)```
It's based on PyTorch tutorial about image cassifiers, that can be found [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).
I took code for resuming training from [here.](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html)

Code that I used for testing model:

导入火炬导入火炬视觉导入torchvision.transforms作为转换

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

trainset = torchvision.datasets.CIFAR100(root='./dataone', train=False,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR100(root='./dataone', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)
classes = ('aquatic mammals','fish','flowers','food containers','fruit and vegetables','household electrical devices','household furniture','insects','large carnivores','large man-made outdoor things','large natural outdoor scenes','large omnivores and herbivores','medium-sized mammals','non-insect invertebrates','people','reptiles','small mammals','trees','vehicles 1','vehicles 2')
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 100)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()
PATH = './cifar_net.pth'
net.load_state_dict(torch.load(PATH))
correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
    for data in testloader:
        images, labels = data
        # calculate outputs by running images through the network
        outputs = net(images)
        # the class with the highest energy is what we choose as prediction
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(correct)
print(total)
print(f'Accuracy of the network on the 100000 test images: {100 * correct // total} %')```

它来自PyTorch提供的同一图像分类器教程。我增加了打印总数和正确检测到的图像进行测试。

我怎样才能提高准确度，所以它将至少在50-70%左右？或者这是正常的，这15%是不正确的吗？请帮帮忙。

Answer 1

你试过增加划时代的数量吗？训练通常需要数百到数千次迭代才能获得良好的效果。

还可以通过继续卷积层来改进结构，直到留下1×1×N图像，其中N是最终卷积中的滤波器数。然后压平并加入线性层。在池层之前进行批处理规范化和LeakyReLU激活也可能有帮助。最后，您应该对输出使用Softmax激活，因为您正在处理一个分类器。

我强烈建议查看流行的分类器，如VGG和ResNet。特别是ResNet有一个名为“剩余/跳过连接”的特性，它将一层输出的副本向前传递到线上，以弥补功能的损失。

Answer 2

你能提供准确的和损失的计划，以便我们能够更好地了解在培训中正在发生的事情(或者培训期间的精确性和损失的列表)。

此外，计算验证精度和每个时代后的损失是一个很好的做法，以监测网络在未见数据上的行为。

虽然，正如Xynias所说的，您可以对您的体系结构做一些改进--我相信第一步将是从准确性和损失的角度进行调查。

Answer 3

考虑到CIFAR100有100个类，这是可以期待的。你需要一个非常复杂的网络才能很好地完成这个任务。当然更多的功能地图，从64个或更多的频道开始。

这种Q&D结构经过10个年代左右(使用0.1的学习速率和256个批处理大小，我还添加了RandomHorizontalFlip()变换)，总的精度超过了50%：

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Conv2d(3, 128, 3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 128, 3, stride=1, padding=1),
            nn.ReLU(),
            nn.AvgPool2d(2, 2),
            nn.Conv2d(128, 256, 3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 256, 3, stride=1, padding=1),
            nn.ReLU(),
            nn.AvgPool2d(2, 2),
            nn.Flatten(),
            nn.Dropout(0.5),
            nn.Linear(16384, 100),
        )
    def forward(self, x):
        return self.layers(x)

为了获得更好的结果，您可以尝试实现类似ResNet的东西，或者使用预先制作的(可能是预先训练的)模型，例如，使用timm：

import timm
net = timm.create_model('resnet18d', pretrained=True, num_classes=100)

它以与上面相同的参数相当快地实现了目标度量。