Caer is a high-performance computer vision library for Python that aims to scale your research, not your boilerplate. Instead of stitching together the same OpenCV and framework glue code in every project, Caer wraps the most common vision workflows โ loading and preprocessing images and video, augmentation, classification and segmentation โ into a clean, consistent API. The project is open source on GitHub, where it has earned roughly 820 stars, and it is still actively developed, with recent updates in 2026.
Computer vision projects tend to drown in repetitive plumbing: decoding frames, resizing and normalizing tensors, building augmentation pipelines and shuttling data to the GPU. Caer removes much of that friction with sensible defaults and a design that stays close to the tools researchers already know, including OpenCV and PyTorch-style workflows. It supports GPU acceleration through CUDA, handles both image and video processing, and ships with extensive documentation and examples so a new experiment can be up and running in a few lines of code. It installs from PyPI with a simple pip install caer. The documentation includes API references and worked examples covering common research tasks, from training a classifier to processing video, which shortens the path from idea to experiment.
Caer is completely free and open source under the MIT license. There is no paid tier, hosted version or commercial edition โ you install the library and run it on your own hardware.
Caer suits computer vision researchers and Python developers who want to prototype and scale vision experiments quickly without rewriting the same preprocessing and augmentation boilerplate for every new project.