Andrew-AAAA
d170cff0e4
- Auto-generate model names: {arch}-{labelType}-{epochs}ep-{classNames}-{YYYYMMDD}
- Add PATCH /api/models/{id}/rename endpoint
- Inline rename UI on Models & Training page
- Download filename uses model name instead of v{N}
- DB migration: add name column to model_versions
- Update all docs to reflect new naming convention
262 lines
12 KiB
Markdown
262 lines
12 KiB
Markdown
# Entity Relationship Diagram (ERD)
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This document provides the complete Entity Relationship Diagram (ERD) and relational schema for the **reTraining** platform SQLite database (`app.db`), as implemented in [`backend/db.py`](file:///home/asus/feedmill_semarang_project/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 relational metadata, foreign keys, job queues, triage rules, review states, model versions, and benchmark run telemetry.
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* **Disk Filesystem (`data/projects/<slug>/`)**: Stores raw/extracted image pixels (`.jpg`), YOLO annotation labels (`.txt`), immutable dataset definitions (`data.yaml`), and trained neural network checkpoints (`.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 "AUTOINCREMENT"
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text slug UK "Unique project identifier"
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text name "Display title"
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text label_type "bbox | polygon"
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text base_model_path "Path to initial .pt model"
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text base_model_kind "uploaded | pretrained | trained"
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text video_root "Root path for video archive"
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integer val_every "Validation split step (default 5)"
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text secondary_model_path "Optional companion model"
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text secondary_model_name "Companion model label"
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text secondary_model_classes "Companion model class JSON"
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text augment "Augmentation parameters JSON"
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real created_at "Unix epoch timestamp"
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}
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PROJECT_CLASSES {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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integer class_id "YOLO integer class index"
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text name "Class display name"
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text prompt "SAM3 natural language zero-shot prompt"
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}
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BATCHES {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text video_path "Source video relative path"
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text date_label "Video date directory"
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text batch_label "Video file base name"
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real start_sec "Trim range start in seconds"
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real end_sec "Trim range end in seconds"
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real fps "Extraction sampling rate"
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text status "extracting | extracted | labeling | reviewing | approved | merged | failed"
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integer frame_count "Extracted frames count"
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real created_at "Unix epoch timestamp"
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real merged_at "Timestamp when batch merged to dataset"
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}
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FRAMES {
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integer id PK "AUTOINCREMENT"
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integer batch_id FK "References batches(id)"
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integer idx "Frame index within batch"
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text filename "Image file name (000001.jpg)"
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integer width "Frame pixel width"
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integer height "Frame pixel 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 "AUTOINCREMENT"
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integer frame_id FK "References frames(id)"
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integer class_id "Target YOLO class index"
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text geometry "Box or Polygon coordinates JSON"
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real score "SAM3 confidence score (0.0 - 1.0)"
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text source "auto | manual"
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real created_at "Unix epoch timestamp"
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}
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ANNOTATION_OVERRIDES {
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integer annotation_id PK, FK "References annotations(id)"
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text verdict "keep | ignore | reclass"
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integer target_class "New class index if reclassified"
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real decided_at "Unix epoch timestamp"
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}
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DATASETS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text name "Dataset version name (e.g. Master v1)"
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text note "Release description"
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text rule_version "Triage rule version identifier"
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text rules_json "Frozen triage rules JSON snapshot"
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real created_at "Unix epoch timestamp"
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}
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DATASET_ITEMS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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integer dataset_id FK "References datasets(id)"
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integer frame_id FK "References frames(id)"
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text split "train | val"
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text image_rel "Relative image path in dataset"
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text label_rel "Relative label path in dataset"
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real added_at "Unix epoch timestamp"
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}
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BASE_DATASETS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text name "External baseline dataset name"
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text source "Import origin description"
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integer image_count "Number of baseline images"
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integer box_count "Number of baseline bounding boxes"
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text classes "JSON array of class names"
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real created_at "Unix epoch timestamp"
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}
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TRIAGE_RULES {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text stage "Filter stage (default: dataprep)"
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integer position "Rule execution order position"
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text name "Rule human readable name"
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text predicate "Evaluation expression (area, aspect, conf)"
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text action "keep | ignore | reclass"
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integer target_class "Target class index if reclass"
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real created_at "Unix epoch timestamp"
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}
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MODEL_VERSIONS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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integer version "Incrementing version integer"
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text name "Descriptive model name (arch-labelType-epochs-classes-date)"
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text weights_path "Path to trained best.pt weights"
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text parent_model_path "Path to base model used as starting point"
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text metrics "Trained model evaluation metrics JSON"
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text base_metrics "Base model evaluation metrics JSON"
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text rule_version "Triage rule version used for training"
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text augment "Augmentation settings JSON used"
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real created_at "Unix epoch timestamp"
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}
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JOBS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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integer batch_id FK "References batches(id)"
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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 "Job parameters JSON"
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integer progress "Current completed units"
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integer total "Total units of work"
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text message "Human-readable status update"
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text error "Failure message or traceback"
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text log "Detailed execution log lines"
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real created_at "Unix epoch timestamp"
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real started_at "Job start timestamp"
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real finished_at "Job completion timestamp"
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}
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VIDEO_CLOCK {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text video_rel "Relative path to video file"
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text folder_date "Date folder extracted from path"
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text started_at "Burned-in OCR clock string (YYYY-MM-DD HH:MM:SS)"
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text working_day "Operational shift day (06:00 - 05:59 cutoff)"
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real confidence "OCR reading confidence"
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integer agreeing "Number of sample frames agreeing"
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text source "ocr | manual"
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text error "OCR parsing error message"
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real read_at "Timestamp when OCR scan was executed"
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integer truck_hits "Frames with truck detected"
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integer truck_samples "Total sampled frames for truck check"
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text truck_model "Model used for truck scan"
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real truck_checked_at "Timestamp of truck scan"
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}
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COUNT_RUNS {
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integer id PK "AUTOINCREMENT"
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integer project_id FK "References projects(id)"
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text video_rel "Relative path to video evaluated"
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text date_label "Video date directory"
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text batch_label "Video file base name"
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integer loading "Counted loading direction crossings"
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integer unloading "Counted unloading direction crossings"
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integer net "Calculated net count (loading - unloading)"
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integer ground_truth "Physical verified hand-tally count"
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integer frames "Total video frames evaluated"
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real seconds "Inference elapsed time in seconds"
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text params "Counting line & ByteTrack parameters JSON"
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text model_path "YOLO model weights path used"
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text error "Error message if benchmark failed"
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real counted_at "Unix epoch timestamp"
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}
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```
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---
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## Entity Descriptions
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### 1. Workspace & Multi-Project Isolation
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* **`projects`**: Top-level entity isolating datasets, classes, models, and CCTV archives. Enforces geometry type (`bbox` vs `polygon`) and stores the active base model checkpoint.
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* **`project_classes`**: YOLO class definitions for the project. Pairs each integer `class_id` with natural language text prompts utilized by SAM3 zero-shot auto-annotation.
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### 2. Video Ingest & Annotation Lifecycle
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* **`batches`**: A trimmed segment of raw industrial CCTV footage. Captures start/end timestamps, sampling FPS, frame counts, and progression from extraction to dataset merge.
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* **`frames`**: Discrete JPEG stills extracted from a batch. Tracks individual review status (`pending`, `approved`, `rejected`).
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* **`annotations`**: Object bounding boxes or polygon masks per frame with SAM3 / manual confidence scores and class assignments.
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* **`annotation_overrides`**: Outlier inspection decisions (`keep`, `ignore`, `reclass`) resulting from interactive 2D scatter plot triage.
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### 3. Master Datasets & Data Preparation
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* **`datasets`**: Immutable, versioned master datasets (`v1`, `v2`, `v3`). Captures a permanent JSON snapshot of triage rules (`rules_json`) applied at compilation time.
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* **`dataset_items`**: Junction entity binding frames into dataset versions with deterministic SHA-1 validation split partitioning (`train` vs `val`).
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* **`base_datasets`**: External pre-labeled baseline datasets mounted as train-only supplements without polluting validation benchmarks.
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* **`triage_rules`**: Ordered filtering predicates (e.g., box area, aspect ratio, confidence thresholds) applied during data preparation.
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### 4. Continuous YOLO Retraining & Background Workers
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* **`model_versions`**: Fine-tuned YOLO weights checkpoints. Stores side-by-side performance deltas ($\Delta\text{mAP50}$, $\Delta\text{Precision}$, $\Delta\text{Recall}$) evaluated against the identical frozen validation split.
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* **`jobs`**: Centralized SQLite job queue for asynchronous background tasks (`extract`, `autolabel`, `train`, `count`, etc.) with atomic status management and progress streaming.
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### 5. Shift OCR Indexing & Production Line Counter
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* **`video_clock`**: OCR timestamps extracted from burned-in CCTV camera overlays, mapping recordings to 24-hour manufacturing shifts (06:00 AM to 05:59 AM next day) and detecting truck arrival presence.
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* **`count_runs`**: Real-time production inference benchmarks running ByteTrack line-crossing counters against verified ground truth tallies.
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---
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## Performance & Database Indexes
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The SQLite database enforces the following indexes to maintain sub-millisecond query performance:
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| Index Name | Table | Indexed Columns | Purpose |
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| `idx_frames_batch` | `frames` | `(batch_id, idx)` | Rapid frame lookup and sequential canvas scrubbing |
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| `idx_annotations_frame` | `annotations` | `(frame_id)` | Sub-millisecond bounding box loading per canvas frame |
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| `idx_batches_project` | `batches` | `(project_id)` | Fast batch library filtering by project |
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| `idx_dataset_items_project`| `dataset_items` | `(project_id)` | Dataset compilation and split integrity checks |
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| `idx_triage_rules_project` | `triage_rules` | `(project_id, stage, position)` | Fast ordered triage predicate evaluation |
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| `idx_jobs_project` | `jobs` | `(project_id, created_at)` | UI job queue monitoring and polling |
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| `idx_video_clock_project` | `video_clock` | `(project_id, working_day)` | Shift-based video library filtering |
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| `idx_count_runs_project` | `count_runs` | `(project_id, date_label)` | Production counting benchmark reporting |
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