docs: add database entity relationship diagram (ERD.md)
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# Entity Relationship Diagram (ERD)
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This document specifies the SQLite database schema and entity relationships for the **reTraining** platform (`app.db`), as defined in [backend/db.py](file:///C:/Users/araar/Downloads/PT_SIAB_FULLTIME/Feedmill_Semarang/reTraining/backend/db.py).
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---
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## Architectural Storage Model
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The platform uses a **hybrid storage architecture**:
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* **SQLite Database (`data/app.db`)**: Stores entity metadata, relations, job queues, triage rules, review states, and metrics.
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* **Disk Filesystem (`data/projects/`)**: Stores image pixels (`.jpg`), YOLO labels (`.txt`), YAML dataset manifests (`data.yaml`), and trained model weights (`.pt`).
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---
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## Mermaid Entity Relationship Diagram
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```mermaid
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erDiagram
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PROJECTS ||--o{ PROJECT_CLASSES : "defines"
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PROJECTS ||--o{ BATCHES : "contains"
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PROJECTS ||--o{ DATASETS : "compiles"
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PROJECTS ||--o{ DATASET_ITEMS : "aggregates"
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PROJECTS ||--o{ BASE_DATASETS : "mounts"
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PROJECTS ||--o{ TRIAGE_RULES : "configures"
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PROJECTS ||--o{ MODEL_VERSIONS : "produces"
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PROJECTS ||--o{ JOBS : "executes"
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PROJECTS ||--o{ VIDEO_CLOCK : "indexes"
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PROJECTS ||--o{ COUNT_RUNS : "benchmarks"
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BATCHES ||--o{ FRAMES : "extracts"
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BATCHES ||--o{ JOBS : "triggers"
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FRAMES ||--o{ ANNOTATIONS : "annotates"
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FRAMES ||--o{ DATASET_ITEMS : "includes"
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ANNOTATIONS ||--o| ANNOTATION_OVERRIDES : "overrides"
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DATASETS ||--o{ DATASET_ITEMS : "contains"
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PROJECTS {
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integer id PK
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text slug UK
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text name
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text label_type "bbox | polygon"
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text base_model_path
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text base_model_kind "uploaded | pretrained | trained"
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text video_root
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integer val_every "default: 5"
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real created_at
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}
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PROJECT_CLASSES {
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integer id PK
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integer project_id FK
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integer class_id
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text name
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text prompt
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}
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BATCHES {
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integer id PK
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integer project_id FK
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text video_path
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text date_label
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text batch_label
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real start_sec
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real end_sec
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real fps
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text status "extracting | extracted | labeling | reviewing | approved | merged | failed"
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integer frame_count
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real created_at
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real merged_at
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}
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FRAMES {
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integer id PK
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integer batch_id FK
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integer idx
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text filename
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integer width
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integer height
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text review_status "pending | approved | rejected"
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}
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ANNOTATIONS {
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integer id PK
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integer frame_id FK
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integer class_id
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text geometry "JSON / coordinates"
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real score
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text source "auto | manual"
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real created_at
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}
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ANNOTATION_OVERRIDES {
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integer annotation_id PK, FK
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text verdict "keep | ignore | reclass"
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integer target_class
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real decided_at
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}
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DATASETS {
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integer id PK
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integer project_id FK
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text name
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text note
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text rule_version
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text rules_json
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real created_at
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}
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DATASET_ITEMS {
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integer id PK
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integer project_id FK
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integer dataset_id FK
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integer frame_id FK
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text split "train | val"
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text image_rel
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text label_rel
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real added_at
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}
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BASE_DATASETS {
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integer id PK
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integer project_id FK
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text name
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text source
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integer image_count
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integer box_count
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text classes "JSON array"
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real created_at
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}
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TRIAGE_RULES {
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integer id PK
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integer project_id FK
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text stage "default: dataprep"
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integer position
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text name
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text predicate
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text action "keep | ignore | reclass"
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integer target_class
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real created_at
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}
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MODEL_VERSIONS {
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integer id PK
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integer project_id FK
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integer version
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text weights_path
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text parent_model_path
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text metrics "JSON"
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text base_metrics "JSON"
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real created_at
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}
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JOBS {
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integer id PK
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integer project_id FK
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integer batch_id FK
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text type "extract | autolabel | merge | train | count | clock-scan | truck-scan"
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text status "queued | running | done | failed | cancelled"
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text params "JSON"
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integer progress
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integer total
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text message
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text error
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text log
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real created_at
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real started_at
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real finished_at
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}
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VIDEO_CLOCK {
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integer id PK
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integer project_id FK
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text video_rel
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text folder_date
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text started_at "YYYY-MM-DD HH:MM:SS"
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text working_day
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real confidence
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integer agreeing
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text source "ocr"
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text error
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real read_at
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}
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COUNT_RUNS {
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integer id PK
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integer project_id FK
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text video_rel
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text date_label
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text batch_label
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integer loading
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integer unloading
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integer net
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integer ground_truth
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integer frames
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real seconds
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text params "JSON"
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text model_path
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text error
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real counted_at
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}
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```
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---
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## Entity Descriptions
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### 1. Project & Class Configuration
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* **`projects`**: Core isolation entity. Configures the target label type (`bbox` vs `polygon`), base model reference, video archive root, and validation split step (`val_every`).
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* **`project_classes`**: Class definitions tied to a project. Stores integer `class_id`, class `name`, and natural language SAM3 zero-shot `prompt`.
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### 2. Video Extraction & Annotation Pipeline
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* **`batches`**: A trimmed segment from a raw CCTV video file. Holds time ranges, sampling FPS, frame counts, and extraction/review lifecycles.
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* **`frames`**: Extracted image stills belonging to a batch, tracking width, height, index, and operator approval status (`pending`, `approved`, `rejected`).
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* **`annotations`**: Bounding boxes or polygon geometries per frame with confidence scores, class IDs, and origin (`auto` from SAM3 vs `manual` from operator).
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* **`annotation_overrides`**: Per-annotation triage verdicts (`keep`, `ignore`, `reclass`) resulting from outlier inspection.
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### 3. Master Datasets & Data Preparation
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* **`datasets`**: Immutable dataset compilations (`v1`, `v2`, `v3`) capturing the snapshot rules and timestamps.
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* **`dataset_items`**: Mapping between a dataset and its constituent image frames, recording deterministic train/val splits (`split IN ('train', 'val')`) and relative filesystem paths.
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* **`base_datasets`**: External train-only datasets imported to supplement training data without affecting validation splits.
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* **`triage_rules`**: Ordered filter predicates applied during data prep to systematically prune bounding box anomalies (e.g. area, aspect ratio, confidence).
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### 4. Training, Jobs & Telemetry
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* **`model_versions`**: Trained YOLO checkpoints (`1`, `2`, `3`...) storing weights paths, parent models, and side-by-side metric evaluations ($\Delta\text{mAP50}$, precision, recall).
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* **`jobs`**: Asynchronous background job queue (`extract`, `autolabel`, `train`, `count`, etc.) managing progress counters, logs, and state transitions.
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### 5. Video Clock & Production Counting Benchmarks
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* **`video_clock`**: OCR timestamps extracted from CCTV video overlays to assign recordings to proper 24-hour work shifts (06:00 to 06:00 cutoff).
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* **`count_runs`**: Inference benchmarks running ByteTrack line-crossing counters against verified physical ground truth numbers.
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---
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## Database Indexes
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To maintain sub-second UI performance across thousands of frames and annotations, the following indices are maintained:
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* `idx_frames_batch` on `frames(batch_id, idx)`
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* `idx_annotations_frame` on `annotations(frame_id)`
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* `idx_batches_project` on `batches(project_id)`
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* `idx_dataset_items_project` on `dataset_items(project_id)`
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* `idx_triage_rules_project` on `triage_rules(project_id, stage, position)`
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* `idx_jobs_project` on `jobs(project_id, created_at)`
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* `idx_video_clock_project` on `video_clock(project_id, working_day)`
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* `idx_count_runs_project` on `count_runs(project_id, date_label)`
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