Given a pre-trained ResNet152, in trying to calculate predictions bench-marks using some common datasets (using PyTorch), and the first RGB dataset that came to mind was CIFAR10. The thing is that CIFAR10 data is `3x32x32` and ResNet expects `3x224x224`. I've resized the data using the known approach of `transforms`:

```
preprocess = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
train = datasets.CIFAR10(root='./data', train=True, download=True, transform=preprocess)
test = datasets.CIFAR10(root='./data', train=False, download=True, transform=preprocess)
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size)

```

but this results in blurry samples and bad predictions. I was wondering what are the best approaches in those cases, as I see many papers using those datasets given advanced models like ResNes and VGG, and I'm not sure how this technical issue could be resolved.

thank you for your response!