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Awesome GitHub RepositoriesModel Robustness Benchmarks
Testing suites designed to evaluate machine learning model performance under adverse conditions and data corruptions.
Distinguishing note: Focuses on stress-testing model reliability against noise and corruption, rather than standard accuracy metrics.
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Awesome Model Robustness Benchmarks GitHub Repositories
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- open-mmlab/mmdetection
open-mmlab/mmdetection
32,409View on GitHubThis project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular
The project enables evaluating object detection and instance segmentation model robustness by testing performance against various image corruptions and severity levels using analysis scripts.
Pythoncascade-rcnnconvnextdetr
