2 repository-uri
Awesome GitHub RepositoriesSegmentation Model Validation
Tools for calculating performance metrics such as mean average precision for pixel-level image segmentation tasks.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Segmentation Model Validation. Refine with filters or upvote what's useful.
Awesome Segmentation Model Validation GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- ultralytics/ultralytics
ultralytics/ultralytics
58,468Vezi pe GitHubUltralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in
Validates segmentation accuracy by calculating performance metrics such as mean average precision for masks and boxes.
Pythonclicomputer-visiondeep-learning - leejunhyun/image_segmentation
LeeJunHyun/Image_Segmentation
3,063Vezi pe GitHubThis project is a biomedical image segmentation framework and PyTorch computer vision library. It provides a deep learning pipeline for isolating specific anatomical structures within medical imagery using pixel-level binary classification. The system utilizes an encoder-decoder neural architecture combined with attention-based feature refinement to highlight relevant anatomical regions and suppress background noise. The toolkit covers a full training workflow, including stochastic data augmentation for biomedical datasets, hyperparameter optimization, and model persistence for restoring pre
Measures the accuracy of segmentation predictions using similarity coefficients and precision metrics.
Python
