# weiaicunzai/pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

Repository: https://github.com/weiaicunzai/pytorch-cifar100
Canonical: https://ross.abutalabs.com/products/pytorch-cifar100
Language: Python
License Family: other
Topics: pytorch, image-classification, deep-learning, cifar100, resnet, googlenet, inceptionv4, xception, resnext, inceptionv3, inception-resnet-v2, densenet, shufflenet, mobilenet, squeezenet, nasnet, wideresnet
Last push: 2024-07-15T16:58:28+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3420, "days_push": 779, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4776, forks 1206 (observed 2026-08-28T04:08:59.506097+00:00)

## What it is
A PyTorch practice repository implementing dozens of classic CNN architectures (ResNet, DenseNet, VGG, GoogLeNet, Inception variants, Xception, MobileNet, ShuffleNet, SENet, and more) for training and evaluating on the CIFAR-100 dataset. It includes training and testing scripts with GPU support, warmup options, and TensorBoard logging.

## Use cases
- train resnet on cifar100 in pytorch
- compare cnn architectures on cifar100
- learn how to implement classic vision models from scratch
- benchmark mobilenet vs shufflenet accuracy
- pytorch image classification training script example
- study inception and xception implementations

## When to choose
- you want ready-to-run PyTorch implementations of many classic CNN architectures on CIFAR-100
- you are learning deep learning and want reference code for papers like ResNet or DenseNet
- you need a simple baseline for image classification experiments

## When to avoid
- you need production-grade training pipelines with distributed training or mixed precision
- you want pretrained ImageNet weights or transfer learning tooling
- you need a maintained library with a license, tests, and package releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing, computer-vision, benchmarking
- domain: deep-learning, computer-vision, image-processing, machine-learning, tutorials
- platform: python, cross-platform
- tags: pytorch, cifar100, image-classification, cnn-architectures, resnet, densenet, mobilenet, educational, gpu

## Member repositories
- weiaicunzai/pytorch-cifar100 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:59.506097+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:46.261253+00:00, confidence not recorded.
  - readme: https://github.com/weiaicunzai/pytorch-cifar100 (fetched 2026-08-28T04:08:59.506097+00:00, sha 42180e8133ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.