ai-training/_docs/04_refactoring/01-code-improvements/list-of-changes.md

List of Changes

Run: 01-code-improvements Mode: guided Source: _docs/02_document/refactoring_notes.md Date: 2026-03-28

Summary

Apply 5 improvements from documentation review: update YOLO model, switch to built-in augmentation, remove processed directory, use hard symlinks for dataset formation, and unify configuration files.

Changes

C01: Update YOLO model to 26m variant

• File(s): src/constants.py, src/train.py
• Problem: Current model config uses yolo11m.yaml which trains from a YAML architecture definition
• Change: Update TrainingConfig.model to the YOLO 26m variant; ensure train_dataset() uses the updated model reference
• Rationale: Use updated model version as requested; pretrained weights improve convergence
• Risk: medium
• Dependencies: None

C02: Replace external augmentation with YOLO built-in

• File(s): src/train.py, src/augmentation.py
• Problem: augmentation.py uses albumentations to augment images into a separate processed_dir before training — adds complexity, disk usage, and a separate processing step
• Change: Remove the augment_annotations() call from the training pipeline; add YOLO built-in augmentation parameters (hsv_h, hsv_s, hsv_v, degrees, translate, scale, shear, flipud, fliplr, mosaic, mixup) to the model.train() call in train_dataset(), each on its own line with a descriptive comment; augmentation.py remains in codebase but is no longer called during training
• Rationale: YOLO's built-in augmentation applies on-the-fly during training, eliminating the pre-processing step and processed directory
• Risk: medium
• Dependencies: C01

C03: Remove processed directory — use data dir directly

• File(s): src/constants.py, src/train.py, src/exports.py, src/dataset-visualiser.py
• Problem: processed_dir, processed_images_dir, processed_labels_dir properties in Config are no longer needed when built-in augmentation is used; form_dataset() reads from processed dir; form_data_sample() reads from processed dir; visualise_processed_folder() reads from processed dir
• Change: Remove processed_dir/processed_images_dir/processed_labels_dir properties from Config; update form_dataset() to read from data_images_dir/data_labels_dir; update form_data_sample() similarly; update visualise_processed_folder() similarly
• Rationale: Processed directory is unnecessary without external augmentation step
• Risk: medium
• Dependencies: C02

C04: Use hard symlinks instead of file copies for dataset

• File(s): src/train.py
• Problem: copy_annotations() uses shutil.copy() to duplicate images and labels into train/valid/test splits — wastes disk space on large datasets
• Change: Replace shutil.copy() with os.link() to create hard links; add fallback to shutil.copy() for cross-filesystem scenarios
• Rationale: Hard links share the same inode, saving disk space while maintaining independent directory entries
• Risk: low
• Dependencies: C03

C05: Unify configuration — remove annotation-queue/config.yaml

• File(s): src/constants.py, src/annotation-queue/annotation_queue_handler.py, src/annotation-queue/config.yaml
• Problem: src/annotation-queue/config.yaml duplicates root config.yaml with different dirs values; annotation_queue_handler.py parses config manually via yaml.safe_load instead of using the shared Config model
• Change: Extend Config in constants.py to include queue and annotation-queue directory settings; refactor annotation_queue_handler.py to accept a Config instance (or import from constants); delete src/annotation-queue/config.yaml
• Rationale: Single source of truth for configuration eliminates drift risk and inconsistency
• Risk: medium
• Dependencies: None