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https://github.com/azaion/ai-training.git
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Oleksandr Bezdieniezhnykh
142c6c4de8
- Replaced module-level path variables in constants.py with a structured Pydantic Config class. - Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure. - Fixed bugs related to image processing and model saving. - Enhanced test infrastructure to accommodate the new configuration approach. This refactor improves code maintainability and clarity by centralizing configuration management.
2.3 KiB
2.3 KiB
Acceptance Criteria
Training
- Dataset split: 70% train, 20% validation, 10% test (hardcoded in train.py).
- Training parameters: YOLOv11 medium, 120 epochs, batch size 11, image size 1280px, save_period=1.
- Corrupted labels (bounding box coordinates > 1.0) are filtered to
/azaion/data-corrupted/. - Model export to ONNX: 1280px resolution, batch size 4, NMS baked in.
- Trained model encrypted with AES-256-CBC before upload.
- Encrypted model split: small part ≤3KB or 20% of total → API server; remainder → CDN.
- Post-training: model uploaded to both API and CDN endpoints.
Augmentation
- Each validated image produces exactly 8 outputs (1 original + 7 augmented variants).
- Augmentation runs every 5 minutes, processing only unprocessed images.
- Bounding boxes clipped to [0, 1] range; boxes with area < 0.01% of image discarded.
- Processing is parallelized per image using ThreadPoolExecutor.
Annotation Ingestion
- Created/Edited annotations from Validators/Admins →
/azaion/data/. - Created/Edited annotations from Operators →
/azaion/data-seed/. - Validated (bulk) events → move from
/data-seed/to/data/. - Deleted (bulk) events → move to
/data_deleted/. - Queue consumer offset persisted to
offset.yamlafter each message.
Inference
- TensorRT inference: ~54s for 200s video, ~3.7GB VRAM.
- ONNX inference: ~81s for 200s video, ~6.3GB VRAM.
- Frame sampling: every 4th frame.
- Batch size: 4 (for both ONNX and TensorRT).
- Confidence threshold: 0.3 (hardcoded in inference/inference.py).
- NMS IoU threshold: 0.3 (hardcoded in inference/inference.py).
- Overlapping detection removal: IoU > 0.3 with lower confidence removed.
Security
- API authentication via JWT (email/password login).
- Model encryption: AES-256-CBC with static key.
- Resource encryption: AES-256-CBC with hardware-derived key (CPU+GPU+RAM+drive serial hash).
- CDN access: separate read/write S3 credentials.
- Split-model storage: prevents model theft from single storage compromise.
Data Format
- Annotation format: YOLO (class_id center_x center_y width height — all normalized 0–1).
- 17 base annotation classes × 3 weather modes = 51 active classes (80 total slots).
- Image format: JPEG.
- Queue message format: msgpack with positional integer keys.