Overview of Microsoft’s Open‑Source Computer Vision Recipes Library

This article introduces Microsoft’s open‑source Computer Vision Recipes library, which aggregates best practices, code examples, and extensive documentation for computer‑vision tasks.

The library aims to help data scientists and machine‑learning engineers quickly build vision systems by providing Jupyter notebooks and utility functions built on top of state‑of‑the‑art (SOTA) libraries, with PyTorch as the underlying deep‑learning framework.

Project links are provided: the main repository https://github.com/microsoft/computervision-recipes , Jupyter notebook examples at https://github.com/microsoft/computervision-recipes/blob/master/scenarios , and utility functions at https://github.com/microsoft/computervision-recipes/blob/master/utils_cv .

The library targets users with a solid background in computer vision, offering source‑only code that can be customized for various real‑world visual problems.

It covers a wide range of vision scenarios, including:

Image classification – ready‑to‑run notebooks with default parameters for multiple datasets.

Image similarity – tools for building high‑accuracy retrieval systems.

Object detection – based on Torchvision’s Faster R‑CNN implementation.

Key‑point detection – using Mask R‑CNN extensions for pose estimation.

Image segmentation – leveraging fastai’s UNet with pretrained ResNet backbones.

Action recognition – video‑based models such as R(2+1)D, with pretrained weights from IG‑Kinetics.

Multi‑object tracking – integrating the FairMOT algorithm.

Crowd counting – production‑ready models (MCNN and OpenPose) with heuristic selection.

For each scenario, the repository provides example notebooks, best‑practice guidelines, and optional Azure integration to accelerate training on large datasets or to deploy models as web services.

The authors recommend running examples on GPU‑enabled machines for reasonable training speed, although CPU execution is technically possible.

Additional resources include dependency lists, installation instructions, testing procedures, and performance benchmarks.