graph LR
Trainer["Trainer"]
Validator["Validator"]
Loss_Functions["Loss Functions"]
Metrics_Calculation["Metrics Calculation"]
Trainer -- "initiates validation of" --> Validator
Trainer -- "utilizes" --> Loss_Functions
Validator -- "provides results to" --> Trainer
Validator -- "utilizes" --> Metrics_Calculation
Loss_Functions -- "provides loss to" --> Trainer
Metrics_Calculation -- "provides metrics to" --> Validator
The Model Training & Evaluation Engine subsystem is responsible for orchestrating the entire model training and validation process, including managing training epochs, optimizing model parameters, calculating loss, and evaluating performance metrics on validation datasets.
Orchestrates the entire training lifecycle, including managing epochs, optimizing model parameters, and coordinating with the validation process. It sets up the training environment, configures the optimizer, and saves training progress.
Related Classes/Methods:
Evaluates the model's performance on validation datasets. It processes predictions, calculates various metrics, and prepares results for reporting and plotting.
Related Classes/Methods:
Provides a collection of loss functions (e.g., detection, rotated bounding box, keypoint, segmentation losses) used to quantify the discrepancy between model predictions and ground truth during training.
Related Classes/Methods:
Computes and manages a comprehensive set of performance metrics (e.g., mAP, precision, recall) to assess the model's accuracy and effectiveness, and facilitates their plotting and reporting.
Related Classes/Methods: