CODEWITHSUNDEEP
deep-learningpytorchdatasetresnet
Hadar
Hadar

Reputation: 668

apply ResNet on CIFAR10 after resizing (pyTorch)

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!

Upvotes: 0

Views: 1813

Answers (2)

Mohamed Elawady
Mohamed Elawady

Reputation: 11

For image classification benchmarks, I recommend you to use the commonly used datasets (also pre-defined inside torch vision) with higher resolution: LSUN or Places365.

Upvotes: 0

Deusy94
Deusy94

Reputation: 757

Yes, you need to resize input images to the size 3x224x224. By doing so, after a normal training procedure, you should achieve outstanding results on CIFAR-10 (like 96% on the test-set).

I guess the main problem is that you're using a network that is pre-trained on higher resolution images (resnet152 comes pre-trained on imageNet), without any other training you can't expect good results changing the dataset drastically.

Upvotes: 1

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