graph LR
Application_Orchestration["Application Orchestration"]
Configuration_Management["Configuration Management"]
Data_Management["Data Management"]
Model_Core["Model Core"]
Training_Engine["Training Engine"]
Evaluation_Metrics["Evaluation & Metrics"]
Optimization_Loss["Optimization & Loss"]
Checkpoint_Export["Checkpoint & Export"]
Application_Orchestration -- "configures and initializes" --> Configuration_Management
Application_Orchestration -- "initializes" --> Data_Management
Application_Orchestration -- "initializes" --> Model_Core
Application_Orchestration -- "initializes" --> Training_Engine
Application_Orchestration -- "initializes" --> Evaluation_Metrics
Application_Orchestration -- "initializes" --> Checkpoint_Export
Application_Orchestration -- "manages model persistence via" --> Checkpoint_Export
Configuration_Management -- "provides configuration to" --> Application_Orchestration
Configuration_Management -- "provides configuration to" --> Data_Management
Configuration_Management -- "provides configuration to" --> Model_Core
Configuration_Management -- "provides configuration to" --> Training_Engine
Configuration_Management -- "provides configuration to" --> Evaluation_Metrics
Configuration_Management -- "provides configuration to" --> Optimization_Loss
Configuration_Management -- "provides configuration to" --> Checkpoint_Export
Data_Management -- "provides data to" --> Training_Engine
Data_Management -- "provides data to" --> Evaluation_Metrics
Model_Core -- "processes data from" --> Data_Management
Model_Core -- "provides outputs to" --> Training_Engine
Model_Core -- "provides outputs to" --> Evaluation_Metrics
Model_Core -- "provides/receives weights from" --> Checkpoint_Export
Training_Engine -- "requests data from" --> Data_Management
Training_Engine -- "interacts with" --> Model_Core
Training_Engine -- "calculates loss with" --> Optimization_Loss
Training_Engine -- "informs learning rate to" --> Optimization_Loss
Training_Engine -- "saves/loads state via" --> Checkpoint_Export
Training_Engine -- "invokes" --> Evaluation_Metrics
Evaluation_Metrics -- "requests data from" --> Data_Management
Evaluation_Metrics -- "performs inference with" --> Model_Core
Evaluation_Metrics -- "reports metrics to" --> Training_Engine
Optimization_Loss -- "receives parameters/outputs from" --> Model_Core
Optimization_Loss -- "updates parameters in" --> Model_Core
Checkpoint_Export -- "provides/receives models to/from" --> Model_Core
click Application_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Application_Orchestration.md" "Details"
click Configuration_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Configuration_Management.md" "Details"
click Data_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Data_Management.md" "Details"
click Model_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Model_Core.md" "Details"
click Training_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Training_Engine.md" "Details"
click Optimization_Loss href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Optimization_Loss.md" "Details"
click Checkpoint_Export href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml-cvnets/Checkpoint_Export.md" "Details"
The ml-cvnets project is structured around a modular design, facilitating efficient development and deployment of computer vision models. At its core, Application Orchestration serves as the central control, managing the lifecycle of training, evaluation, and benchmarking processes. It initializes and coordinates interactions among key components, including Configuration Management for parameter handling, Data Management for data preparation, Model Core for model definitions, Training Engine for model optimization, Evaluation & Metrics for performance assessment, and Checkpoint & Export for model persistence and deployment. This clear separation of concerns ensures a robust and scalable architecture, where data flows logically from preparation through training and evaluation, culminating in deployable models.
Application Orchestration [Expand]
The primary entry point for different operational modes (training, evaluation, benchmarking). It initializes the application, parses top-level arguments, and orchestrates the overall process by invoking core engine components.
Related Classes/Methods:
Configuration Management [Expand]
Centralized system for managing project configurations and command-line arguments. It loads YAML configuration files, validates input parameters, and provides structured options to other components.
Related Classes/Methods:
Data Management [Expand]
Handles all aspects of data loading, preprocessing, augmentation, and batching for various data types. It creates datasets, applies transformations, and manages data loaders for training and evaluation.
Related Classes/Methods:
Model Core [Expand]
Contains the architectural definitions and building blocks for a wide range of computer vision models. This includes implementing forward passes and integrating neural augmentation techniques.
Related Classes/Methods:
Training Engine [Expand]
Manages the core training loop, including forward and backward passes, gradient accumulation, and logging of training progress. It orchestrates interactions between data, model, loss, and optimization components.
Related Classes/Methods:
Oversees the evaluation process for different tasks, including inference, prediction saving, and task-specific result analysis. It also implements various performance metrics for evaluating model outputs.
Related Classes/Methods:
Optimization & Loss [Expand]
Manages the optimizers and learning rate schedulers used during model training, and provides a collection of loss functions, including composite loss mechanisms.
Related Classes/Methods:
optim/__init__.pyoptim/scheduler/base_scheduler.pyloss_fn/composite_loss.pyloss_fn/classification/cross_entropy.py
Checkpoint & Export [Expand]
Handles the saving and loading of model weights, optimizer states, and other training progress information. It also manages the export of trained models to various deployment formats.
Related Classes/Methods: