CIFARデモ

CIFAR-10データセット

CIFAR-10は32×32ピクセルのカラー画像で、10種類の物体クラスを含みます。

実装コード

import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# データ拡張付きの前処理
train_transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
])

test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
])

# データセット
train_dataset = datasets.CIFAR10('./data', train=True, download=True, transform=train_transform)
test_dataset = datasets.CIFAR10('./data', train=False, transform=test_transform)

# CNNモデル
class CIFAR10Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.Conv2d(64, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25),

            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(),
            nn.Conv2d(128, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25),

            nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.Conv2d(256, 256, 3, padding=1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(256 * 4 * 4, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, 10)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

期待される性能

理解度チェック