CVledd

CVledd is an open-source software framework and community platform for computer vision research. It provides tools to develop, evaluate, and share experiments and datasets with a focus on reproducibility. The project serves researchers, educators, and developers working on tasks such as object detection, segmentation, and video analysis.

Key features include dataset versioning and provenance, a modular experimentation pipeline, a model zoo, and built-in evaluation metrics. The platform supports automation and collaboration through a centralized repository of datasets, models, and notebooks, and integrates with common machine learning libraries to enable cross-environment workflows.

CVledd is implemented primarily in Python, with a web interface and a REST API. It uses PostgreSQL for metadata storage and provides Docker images and Kubernetes deployment options. The architecture emphasizes plug-in extensibility for data loaders, augmentations, metrics, and visualizations.

The project began in 2021 as a community initiative and was released under the Apache 2.0 license in 2022. Governance is community-driven, with maintainers drawn from academia and industry.

Users employ CVledd to manage large CV datasets, track experiments, and compare models using standardized metrics. It has been adopted by several labs for benchmarking and by instructors for teaching introductory CV courses.

Reception is mixed: supporters cite reproducibility and collaboration benefits, while critics note a learning curve and the need for comprehensive documentation.

Related projects include MLflow, DVC, and Hugging Face Datasets.