{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"fvcore provides shared computer vision utilities for PyTorch, including neural network layers, loss functions, FLOP counting, parameter analysis, and hyperparameter scheduling used across FAIR's research frameworks.","skillfed_tags":["pytorch","computer-vision","profiling"],"use_cases":["Profile model computational cost (FLOPs) during development to optimize architectures before training.","Analyze parameter counts and memory footprint of neural networks for deployment planning.","Recompute batch normalization statistics on a new dataset without retraining the full model.","Schedule learning rates and other hyperparameters in a scale-invariant, reproducible way across experiments.","Access common PyTorch layers and loss functions shared across FAIR research codebases."],"what_it_does":"fvcore is a lightweight library maintained by Facebook AI Research (FAIR) that centralizes common computer vision utilities used across their research projects. It provides PyTorch-native components: standard neural network layers and loss functions, a hierarchical FLOP counter for profiling model efficiency, recursive parameter counting for model analysis, batch normalization statistics recomputation, and a stateless hyperparameter scheduler. All code is type-annotated and tested.\n\nThe library is designed for researchers and engineers working with PyTorch who need standard computer vision utilities without heavyweight dependencies. It has no runtime dependencies listed in the package metadata, though PyTorch itself is a hard requirement. The package is actively maintained at the repository level but has not received a PyPI release since December 2022, which may affect compatibility with very recent PyTorch versions.","worth_installing":"Yes, if you use PyTorch for computer vision research and need standard profiling or scheduling utilities. The library is actively maintained at the repository level, permissively licensed, and widely used in FAIR projects. However, note that the last PyPI release was December 2022\u2014verify compatibility with your PyTorch version before installing, and consider installing from GitHub if you need the latest code."},"id":"fvcore","links":{"html":"https://skillfed.io/packages/fvcore","md":"https://skillfed.io/packages/fvcore.md","pypi":"https://pypi.org/project/fvcore/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2022-12-21","license_spdx":null,"license_treatment":"permissive","name":"fvcore","python_support":"supports_current","summary":"Collection of common code shared among different research projects in FAIR computer vision team"},"popularity":{"monthly_downloads":2121804,"position":3277,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.5.post20221221"}