# 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.yaml` after 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.