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Oleksandr Bezdieniezhnykh a47fa135de Update configuration and test structure for improved clarity and functionality

- Modified `.gitignore` to include test fixture data while excluding test results.
- Updated `config.yaml` to change the model from 'yolo11m.yaml' to 'yolo26m.pt'.
- Enhanced `.cursor/rules/coderule.mdc` with additional guidelines for test environment consistency and infrastructure handling.
- Revised autopilot state management in `_docs/_autopilot_state.md` to reflect current progress and tasks.
- Removed outdated augmentation tests and adjusted dataset formation tests to align with the new structure.

These changes streamline the configuration and testing processes, ensuring better organization and clarity in the project.

2026-03-28 06:11:55 +02:00

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Research Findings

Run: 01-code-improvements Date: 2026-03-28

Current State Analysis

Training Pipeline

Uses yolo11m.yaml (architecture-only config, trains from scratch)
External augmentation via albumentations library in src/augmentation.py
Two-step process: augment → form dataset → train
Dataset formation copies files with shutil.copy(), duplicating ~8x storage

Configuration

Two config files: config.yaml (root) and src/annotation-queue/config.yaml
Annotation queue handler parses YAML manually instead of using shared Config model
Config drift risk between the two files

YOLO 26 Model Update

Ultralytics YOLO26 is the latest model family. The medium variant yolo26m.pt replaces yolo11m.yaml:

Uses pretrained weights (.pt) rather than architecture-only (.yaml)
Faster convergence with transfer learning
Improved accuracy on detection benchmarks

Built-in Augmentation Parameters

YOLO's model.train() supports the following augmentation parameters that replace the external albumentations pipeline:

Parameter	Default	Equivalent to current external aug
`hsv_h`	0.015	HueSaturationValue(hue_shift_limit=10)
`hsv_s`	0.7	HueSaturationValue(sat_shift_limit=10)
`hsv_v`	0.4	RandomBrightnessContrast + HSV
`degrees`	0.0	Affine(rotate=(-35,35)) → set to 35.0
`translate`	0.1	Default is sufficient
`scale`	0.5	Affine(scale=(0.8,1.2)) → default covers this
`shear`	0.0	Affine(shear=(-10,10)) → set to 10.0
`fliplr`	0.5	HorizontalFlip(p=0.6) → set to 0.6
`flipud`	0.0	Not used currently
`mosaic`	1.0	New — YOLO built-in
`mixup`	0.0	New — optional

Hard Symlinks

os.link() creates hard links sharing the same inode. Benefits:

Zero additional disk usage for dataset splits
Same read performance as regular files
Works on same filesystem (which is the case here — all under /azaion/)
Fallback to shutil.copy() for cross-filesystem edge cases

2.1 KiB Raw Blame History