Collection of common code that's shared among different research projects in FAIR computer vision team.

fvcore

fvcore is a light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks developed in FAIR, such as Detectron2, PySlowFast, and ClassyVision. All components in this library are type-annotated, tested, and benchmarked.

The computer vision team in FAIR is responsible for maintaining this library.

Features:

Besides some basic utilities, fvcore includes the following features:

Common pytorch layers, functions and losses in fvcore.nn.
A hierarchical per-operator flop counting tool: see this note for details.
Recursive parameter counting: see API doc.
Recompute BatchNorm population statistics: see its API doc.
A stateless, scale-invariant hyperparameter scheduler: see its API doc.

Install:

fvcore requires pytorch and python >= 3.6.

Use one of the following ways to install:

1. Install from PyPI (updated nightly)

pip install -U fvcore

2. Install from Anaconda Cloud (updated nightly)

conda install -c fvcore -c iopath -c conda-forge fvcore

3. Install latest from GitHub

pip install -U 'git+https://github.com/facebookresearch/fvcore'

4. Install from a local clone

git clone https://github.com/facebookresearch/fvcore
pip install -e fvcore

License

This library is released under the Apache 2.0 license.