akamaster/pytorch_resnet_cifar10

Health v2 · maintenance only

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1368 stars · 341 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of ResNet architectures (ResNet20 through ResNet1202) for CIFAR10/CIFAR100 that faithfully matches the original paper's specifications, unlike torchvision's ImageNet-oriented models. It includes pretrained models with test errors matching or beating the paper's reported results.

Use cases

• train resnet on cifar10 matching the original paper
• download pretrained resnet20 resnet56 for cifar10
• compare my model against paper-accurate resnet baselines on cifar
• get proper resnet implementation for cifar100 in pytorch
• reproduce resnet paper results

When to choose

• You need paper-faithful ResNet baselines on CIFAR10/100 for fair comparison
• You want pretrained CIFAR ResNet checkpoints
• You need small ResNet variants like ResNet20 not available in torchvision

When to avoid

• You need ResNets for ImageNet or other datasets
• You want modern architectures or training recipes
• You need validation-split-based model selection as in the paper

Facets

library · maturity maintenance

machine-learning deep-learning deep-learning computer-vision image-processing python resnet cifar10 cifar100 pytorch pretrained-models image-classification gpu

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