facebookresearch/pycls

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

2161 stars · 239 forks observed · 2026-08-28

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

pycls is a PyTorch-based codebase for image classification research developed by Facebook AI Research. It provides implementations of standard models like ResNet, ResNeXt, EfficientNet, and RegNet, along with tools for studying network design spaces and model populations.

Use cases

• train image classification models in pytorch
• reproduce regnet and resnet baselines
• run model design space sweeps for neural architecture research
• download pretrained image classification models
• benchmark models across different flop regimes
• implement and evaluate new classification research ideas

When to choose

• you need a simple, flexible codebase for image classification experiments
• you want to reproduce or build on FAIR papers like RegNet or design space studies
• you need efficient single-machine multi-GPU training for classification models
• you want pretrained baselines across a wide range of compute budgets

When to avoid

• you need a general-purpose computer vision toolkit beyond classification (e.g., detection or segmentation)
• you want a production inference serving system
• you need a high-level training API with minimal configuration
• you require active development or frequent updates

Facets

library · maturity maintenance

machine-learning deep-learning image-processing benchmarking computer-vision image-processing deep-learning machine-learning python cross-platform image-classification pytorch resnet regnet efficientnet model-zoo neural-architecture-search facebook-ai-research research gpu linux

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