# AI Produktionsmodell — Träningskonfiguration
# Zoomer-submissions granskning

project:
  name: "quixzoom-ai-production"
  version: "1.0.0"
  description: "AI-granskning av Zoomer-submissions för IOM"

dataset:
  # Klasser för objektdetektering
  classes:
    - street_light
    - traffic_sign
    - bench
    - trash_can
    - sidewalk
    - road
    - building
    - bridge
    - tree
    - graffiti
    - pothole
    - crack
    - corrosion
    - broken_glass
    - missing_parts
    - water_damage
    - vegetation_overgrowth
    - illegal_dumping
    - broken_pavement
    - faded_markings
  
  num_classes: 20
  
  # Dataset-splits
  splits:
    train: 0.70
    val: 0.15
    test: 0.15
  
  # Minsta dataset-storlek per klass
  min_samples_per_class: 100
  
  # Bildstorlek
  image_size: 640
  
  # Augmentation
  augmentation:
    enabled: true
    target_multiplier: 10
    
    geometric:
      rotation: [-15, 15]
      scale: [0.8, 1.2]
      shear: [-5, 5]
      flip_h: 0.5
      flip_v: 0.0
    
    photometric:
      brightness: [0.7, 1.3]
      contrast: [0.7, 1.3]
      saturation: [0.5, 1.5]
      hue: [-10, 10]
    
    noise:
      gaussian_prob: 0.3
      gaussian_std: [5, 15]
      jpeg_prob: 0.3
      jpeg_quality: [60, 95]
      blur_prob: 0.2
      blur_radius: [0.5, 2.0]
    
    weather:
      rain_prob: 0.1
      fog_prob: 0.1
      shadow_prob: 0.15
      overexposure_prob: 0.1
      underexposure_prob: 0.1

models:
  # YOLOv8 för objektdetektering
  yolo:
    model_type: "yolov8n"  # nano, small, medium, large, xlarge
    pretrained: true
    
    training:
      epochs: 100
      batch_size: 16
      learning_rate: 0.001
      weight_decay: 0.0005
      momentum: 0.937
      
      # Early stopping
      patience: 20
      min_delta: 0.001
      
      # Data loading
      num_workers: 8
      pin_memory: true
      
      # Augmentation (Ultralytics inbyggd)
      hsv_h: 0.015
      hsv_s: 0.7
      hsv_v: 0.4
      degrees: 15
      translate: 0.1
      scale: 0.5
      shear: 5
      perspective: 0.0
      flipud: 0.0
      fliplr: 0.5
      mosaic: 1.0
      mixup: 0.1
      copy_paste: 0.1
    
    inference:
      conf_threshold: 0.25
      iou_threshold: 0.45
      max_det: 300
  
  # CLIP för scenklassificering
  clip:
    model_name: "openai/clip-vit-base-patch32"
    
    fine_tuning:
      epochs: 20
      batch_size: 32
      learning_rate: 0.00005
      warmup_steps: 500
      
    scene_labels:
      - "street view"
      - "building facade"
      - "bridge"
      - "road"
      - "sidewalk"
      - "park"
      - "industrial area"
      - "residential area"
      - "commercial area"
      - "construction site"
  
  # Defekt-klassificerare
  defect_classifier:
    architecture: "resnet50"
    num_classes: 20
    
    training:
      epochs: 50
      batch_size: 64
      learning_rate: 0.001
      
    defect_codes:
      "1100": "surface_rust"
      "2100": "dirt_accumulation"
      "2300": "surface_damage"
      "2400": "graffiti"
      "4100": "missing_parts"
      "4200": "broken_parts"
      "5100": "physical_blockage"
      "6200": "water_damage"

metrics:
  # Objektdetektering
  detection:
    - mAP50
    - mAP50-95
    - precision
    - recall
    - f1_score
  
  # Per-klass metrics
  per_class:
    - precision
    - recall
    - f1_score
    - ap50
    - support
  
  # Scenklassificering
  classification:
    - accuracy
    - top5_accuracy
    - precision_macro
    - recall_macro
    - f1_macro
  
  # Defektdetektering
  defect:
    - accuracy
    - precision
    - recall
    - f1_score
    - confusion_matrix

# Träningsmål
targets:
  mAP50: 0.85
  mAP50_95: 0.70
  precision: 0.88
  recall: 0.85
  f1_score: 0.86
  
  # Per-klass minimum
  per_class_min_ap50: 0.70
  per_class_min_recall: 0.75

# Export
export:
  formats:
    - pytorch
    - onnx
    - torchscript
    - openvino
    - tensorrt
  
  optimization:
    quantization: true
    pruning: false
    distillation: false

# Produktion
deployment:
  # API
  api:
    host: "0.0.0.0"
    port: 8000
    workers: 4
    
  # Batch processing
  batch:
    max_batch_size: 32
    timeout_ms: 5000
    
  # Modell-uppdatering
  model_update:
    auto_retrain: false
    retrain_threshold: 0.05  # mAP drop
    retrain_schedule: "weekly"