mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 19:16:40 +00:00
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 - Removed outdated augmentation tests and adjusted dataset formation tests to align with the new structure.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.
This commit is contained in:
@@ -1,18 +1,5 @@
|
||||
# Performance Test Scenarios
|
||||
|
||||
## PT-AUG-01: Augmentation throughput
|
||||
- **Input**: 10 images from fixture dataset
|
||||
- **Action**: Run `augment_annotations()`, measure wall time
|
||||
- **Expected**: Completes within 60 seconds (10 images × 8 outputs = 80 files)
|
||||
- **Traces**: Restriction: Augmentation runs continuously
|
||||
- **Note**: Threshold is generous; actual performance depends on CPU
|
||||
|
||||
## PT-AUG-02: Parallel augmentation speedup
|
||||
- **Input**: 10 images from fixture dataset
|
||||
- **Action**: Run with ThreadPoolExecutor vs sequential, compare times
|
||||
- **Expected**: Parallel is ≥ 1.5× faster than sequential
|
||||
- **Traces**: AC: Parallelized per-image processing
|
||||
|
||||
## PT-DSF-01: Dataset formation throughput
|
||||
- **Input**: 100 images + labels
|
||||
- **Action**: Run `form_dataset()`, measure wall time
|
||||
|
||||
Reference in New Issue
Block a user