Pytorch-Cifar

A pytorch implementation of several neural networks trained on the CIFAR-10 dataset.

Dataset

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

Here are the classes in the dataset, as well as 10 random images from each:

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

Experiment

Settings

batch_size: 1024
device: Tesla P100
Environment
- Python = 3.7.11
- torch = 1.9.0
- numpy = 1.21.2
- torchvision = 0.10.0

Model

We trained two models, one is LeNet5, and the other is a model designed by ourselves. The architecture of model is as follows:

class MY_CNN(nn.Module):  
    def __init__(self):  
        super(MY_CNN,self).__init__()  
        self.conv1=nn.Conv2d(3,16,3,1,padding=1)  
        self.conv2=nn.Conv2d(16,32,3,1,padding=1)  
        self.conv3 = nn.Conv2d(32, 64, 3, 1, padding=1)  
        self.conv4 = nn.Conv2d(64, 64, 3, 1, padding=1)  
        self.conv5 = nn.Conv2d(64, 128, 3, 1, padding=1)  
        self.conv6 = nn.Conv2d(128, 128, 3, 1, padding=1)  
        self.dropout1 = nn.Dropout(0.5)  
        self.fc1=nn.Linear(128*4*4,512)  
        self.fc2=nn.Linear(512,10)  
  
    def forward(self,x):  
        x = F.relu(self.conv1(x))  
        x = F.relu(self.conv2(x))  
        x = F.max_pool2d(x, 2, 2)  
        x = F.relu(self.conv3(x))  
        x = F.relu(self.conv4(x))  
        x = F.max_pool2d(x, 2, 2)  
        x = F.relu(self.conv5(x))  
        x = F.relu(self.conv6(x))  
        x = F.max_pool2d(x, 2, 2)  
        x = x.view(-1, 4 * 4 * 128)  
        x = F.relu(self.fc1(x))  
        x = self.dropout1(x)  
        x = self.fc2(x)  
        return x