graph LR
Configuration_Management["Configuration Management"]
Dataset_Management_Preprocessing["Dataset Management & Preprocessing"]
Model_Definition_Architecture["Model Definition & Architecture"]
Model_Training_Evaluation_Engine["Model Training & Evaluation Engine"]
Inference_Post_processing_Engine["Inference & Post-processing Engine"]
Model_Export_Deployment["Model Export & Deployment"]
External_Hub_Integration["External Hub Integration"]
Application_Utility_Modules["Application & Utility Modules"]
Configuration_Management -- "Provides dataset paths and augmentation parameters" --> Dataset_Management_Preprocessing
Configuration_Management -- "Supplies hyperparameters and training regimes" --> Model_Training_Evaluation_Engine
Configuration_Management -- "Delivers inference parameters and model paths" --> Inference_Post_processing_Engine
Dataset_Management_Preprocessing -- "Supplies augmented training and validation batches" --> Model_Training_Evaluation_Engine
Dataset_Management_Preprocessing -- "Provides preprocessed images for inference" --> Inference_Post_processing_Engine
Model_Definition_Architecture -- "Provides the neural network structure for training" --> Model_Training_Evaluation_Engine
Model_Definition_Architecture -- "Supplies the trained model structure for prediction" --> Inference_Post_processing_Engine
Model_Training_Evaluation_Engine -- "Updates model weights during training" --> Model_Definition_Architecture
Model_Training_Evaluation_Engine -- "Provides trained models for conversion" --> Model_Export_Deployment
Model_Training_Evaluation_Engine -- "Logs training metrics and uploads trained models" --> External_Hub_Integration
Inference_Post_processing_Engine -- "Outputs structured detection results (e.g., bounding boxes, masks)" --> Application_Utility_Modules
Inference_Post_processing_Engine -- "Logs inference results or uploads predictions" --> External_Hub_Integration
click Configuration_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/Configuration_Management.md" "Details"
click Dataset_Management_Preprocessing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/Dataset_Management_Preprocessing.md" "Details"
click Model_Training_Evaluation_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/Model_Training_Evaluation_Engine.md" "Details"
click Inference_Post_processing_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/Inference_Post_processing_Engine.md" "Details"
click Model_Export_Deployment href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/Model_Export_Deployment.md" "Details"
click External_Hub_Integration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DocLayout-YOLO/External_Hub_Integration.md" "Details"
The DocLayout-YOLO project implements a robust machine learning pipeline for document layout analysis, centered around its deep learning models. The Configuration Management component initializes the system, directing the Dataset Management & Preprocessing to prepare data. This data, along with the model structures from Model Definition & Architecture, feeds into the Model Training & Evaluation Engine for iterative learning and validation. Post-training, models can be deployed via the Inference & Post-processing Engine for real-world predictions, or optimized for various platforms by the Model Export & Deployment component. External Hub Integration provides seamless interaction with external model repositories, while Application & Utility Modules offer extended functionalities like object tracking and advanced analysis, building upon the core detection capabilities. This modular design ensures clear data flow and facilitates maintainability and extensibility.
Configuration Management [Expand]
Centralized module for managing and parsing all project-wide configuration settings, including dataset paths, model hyperparameters, training regimes, and inference parameters.
Related Classes/Methods:
Dataset Management & Preprocessing [Expand]
Handles the entire lifecycle of data, from loading and caching to on-the-fly augmentation and format conversion. It ensures data is in the correct format and ready for model consumption.
Related Classes/Methods:
Defines the foundational neural network building blocks (e.g., convolutions, attention mechanisms, custom layers) and composes them into specific model architectures (e.g., YOLOv10, SAM, RT-DETR).
Related Classes/Methods:
Model Training & Evaluation Engine [Expand]
Orchestrates the entire model training and validation process. This includes managing training epochs, optimizing model parameters, calculating loss, and evaluating performance metrics on validation datasets.
Related Classes/Methods:
doclayout_yolo/engine/trainer.pydoclayout_yolo/engine/validator.pydoclayout_yolo/utils/loss.pydoclayout_yolo.utils.metrics.py
Inference & Post-processing Engine [Expand]
Manages the end-to-end inference pipeline. It takes raw inputs, preprocesses them, executes the trained model, and then post-processes the raw model outputs into structured, usable results (e.g., bounding boxes, masks, keypoints).
Related Classes/Methods:
Model Export & Deployment [Expand]
Facilitates the conversion of trained models into various deployment-ready formats (e.g., ONNX, OpenVINO, TFLite, CoreML), enabling efficient deployment across different platforms.
Related Classes/Methods:
External Hub Integration [Expand]
Manages interactions with external model hubs (e.g., Hugging Face Hub). This includes authentication, loading pre-trained models, and uploading trained models or metrics.
Related Classes/Methods:
Provides higher-level computer vision applications and general utilities built upon the core detection and segmentation capabilities. This includes object tracking, distance calculation, heatmap generation, and object counting.
Related Classes/Methods: