Segavatest

Segavatest is a benchmarking framework for segmentation algorithms in computer vision. It provides a standardized set of datasets, evaluation protocols, and reference implementations intended to facilitate fair comparisons among semantic and instance segmentation methods.

The framework includes a dataset catalog with established train/validation/test splits, a suite of evaluation metrics such as mean intersection-over-union (mIoU), pixel accuracy, and boundary-based scores, and a test harness that enforces consistent preprocessing, inference, and postprocessing across models. It supports multiple data modalities and annotation schemes to accommodate different segmentation tasks and research needs.

Segavatest is maintained by a community of researchers and practitioners and often features public leaderboards, versioned releases, and guidelines to encourage reproducible experiments. It is designed to be extensible, allowing new datasets, tasks, and metrics to be integrated without breaking existing benchmarks. The framework emphasizes reproducibility through controlled environments, documentation, and transparent result reporting.

Limitations include sensitivity to dataset biases and domain shift, which can affect the transferability of results to real-world applications. Users are advised to interpret scores within the context of the datasets and to complement benchmarks with additional evaluations in deployment-like conditions. Segavatest aims to provide a common, ongoing reference for assessing segmentation models while acknowledging the need for diverse evaluation scenarios.