hysts/pytorch_image_classification

Health v2 · maintenance only

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1449 stars · 307 forks observed · 2026-08-28

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

A PyTorch library implementing many image classification architectures (ResNet, DenseNet, WRN, PyramidNet, SENet, etc.) and augmentation techniques (Cutout, Mixup, CutMix) with training configs for CIFAR, MNIST variants, and ImageNet. It reproduces paper results with reported benchmarks.

Use cases

• train image classification models on CIFAR-10
• reproduce results from ResNet or DenseNet papers
• benchmark data augmentation methods like Mixup and CutMix
• get baseline training code for ImageNet classification
• compare model architectures on MNIST datasets

When to choose

• you need faithful PyTorch reproductions of classic classification papers
• you want ready-made configs and benchmark numbers for CIFAR/ImageNet experiments

When to avoid

• you need production inference or deployment tooling
• you want actively maintained support for the latest architectures or Windows

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision benchmarking computer-vision deep-learning image-processing machine-learning python pytorch image-classification resnet densenet cifar10 imagenet data-augmentation research-reproducibility linux gpu

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