facebookresearch/fvcore

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2250 stars · 237 forks observed · 2026-08-28

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

fvcore is a lightweight Python core library providing common functionality shared across FAIR's computer vision frameworks such as Detectron2, PySlowFast, and ClassyVision. It includes PyTorch layers, losses, hierarchical per-operator FLOP counting, parameter counting, BatchNorm statistics recomputation, and a scale-invariant hyperparameter scheduler.

Use cases

• count FLOPs of a PyTorch model per operator
• count model parameters recursively
• recompute BatchNorm population statistics
• schedule learning rate hyperparameters in a scale-invariant way
• share common layers and losses across computer vision projects

When to choose

• you use Detectron2, PySlowFast, or ClassyVision and need their shared core utilities
• you need to benchmark model compute cost (FLOPs) and parameter counts in PyTorch
• you want a tested, type-annotated utility library for vision research

When to avoid

• you need a full computer vision framework rather than shared utilities
• your project does not use PyTorch
• you need general-purpose ML tooling outside computer vision

Facets

library · maturity stable

machine-learning deep-learning benchmarking developer-tools computer-vision deep-learning machine-learning developer-tools python cross-platform pytorch flop-counting parameter-counting fair detectron2 model-analysis utilities gpu

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markdown · JSON · MCP: product_card(name="facebookresearch/fvcore")