Bernt
bae705aa97
- Add NFC ePassport roadmap (ICAO 9303, eIDAS) - Add TensorFlow.js edge face detection (BlazeFace) - Add structured audit logger (GDPR-compliant) - Risk scoring support Part of KYC Apple Native UX v1.1.0
240 lines
4.2 KiB
YAML
240 lines
4.2 KiB
YAML
# AI Produktionsmodell — Träningskonfiguration
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# Zoomer-submissions granskning
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project:
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name: "quixzoom-ai-production"
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version: "1.0.0"
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description: "AI-granskning av Zoomer-submissions för IOM"
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dataset:
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# Klasser för objektdetektering
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classes:
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- street_light
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- traffic_sign
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- bench
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- trash_can
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- sidewalk
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- road
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- building
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- bridge
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- tree
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- graffiti
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- pothole
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- crack
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- corrosion
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- broken_glass
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- missing_parts
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- water_damage
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- vegetation_overgrowth
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- illegal_dumping
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- broken_pavement
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- faded_markings
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num_classes: 20
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# Dataset-splits
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splits:
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train: 0.70
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val: 0.15
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test: 0.15
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# Minsta dataset-storlek per klass
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min_samples_per_class: 100
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# Bildstorlek
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image_size: 640
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# Augmentation
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augmentation:
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enabled: true
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target_multiplier: 10
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geometric:
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rotation: [-15, 15]
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scale: [0.8, 1.2]
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shear: [-5, 5]
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flip_h: 0.5
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flip_v: 0.0
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photometric:
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brightness: [0.7, 1.3]
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contrast: [0.7, 1.3]
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saturation: [0.5, 1.5]
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hue: [-10, 10]
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noise:
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gaussian_prob: 0.3
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gaussian_std: [5, 15]
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jpeg_prob: 0.3
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jpeg_quality: [60, 95]
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blur_prob: 0.2
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blur_radius: [0.5, 2.0]
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weather:
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rain_prob: 0.1
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fog_prob: 0.1
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shadow_prob: 0.15
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overexposure_prob: 0.1
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underexposure_prob: 0.1
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models:
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# YOLOv8 för objektdetektering
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yolo:
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model_type: "yolov8n" # nano, small, medium, large, xlarge
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pretrained: true
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training:
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epochs: 100
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batch_size: 16
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learning_rate: 0.001
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weight_decay: 0.0005
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momentum: 0.937
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# Early stopping
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patience: 20
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min_delta: 0.001
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# Data loading
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num_workers: 8
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pin_memory: true
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# Augmentation (Ultralytics inbyggd)
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 15
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translate: 0.1
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scale: 0.5
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shear: 5
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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mosaic: 1.0
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mixup: 0.1
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copy_paste: 0.1
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inference:
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conf_threshold: 0.25
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iou_threshold: 0.45
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max_det: 300
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# CLIP för scenklassificering
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clip:
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model_name: "openai/clip-vit-base-patch32"
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fine_tuning:
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epochs: 20
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batch_size: 32
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learning_rate: 0.00005
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warmup_steps: 500
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scene_labels:
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- "street view"
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- "building facade"
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- "bridge"
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- "road"
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- "sidewalk"
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- "park"
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- "industrial area"
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- "residential area"
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- "commercial area"
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- "construction site"
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# Defekt-klassificerare
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defect_classifier:
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architecture: "resnet50"
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num_classes: 20
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training:
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epochs: 50
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batch_size: 64
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learning_rate: 0.001
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defect_codes:
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"1100": "surface_rust"
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"2100": "dirt_accumulation"
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"2300": "surface_damage"
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"2400": "graffiti"
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"4100": "missing_parts"
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"4200": "broken_parts"
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"5100": "physical_blockage"
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"6200": "water_damage"
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metrics:
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# Objektdetektering
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detection:
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- mAP50
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- mAP50-95
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- precision
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- recall
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- f1_score
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# Per-klass metrics
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per_class:
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- precision
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- recall
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- f1_score
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- ap50
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- support
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# Scenklassificering
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classification:
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- accuracy
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- top5_accuracy
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- precision_macro
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- recall_macro
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- f1_macro
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# Defektdetektering
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defect:
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- accuracy
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- precision
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- recall
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- f1_score
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- confusion_matrix
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# Träningsmål
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targets:
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mAP50: 0.85
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mAP50_95: 0.70
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precision: 0.88
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recall: 0.85
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f1_score: 0.86
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# Per-klass minimum
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per_class_min_ap50: 0.70
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per_class_min_recall: 0.75
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# Export
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export:
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formats:
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- pytorch
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- onnx
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- torchscript
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- openvino
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- tensorrt
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optimization:
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quantization: true
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pruning: false
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distillation: false
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# Produktion
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deployment:
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# API
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api:
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host: "0.0.0.0"
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port: 8000
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workers: 4
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# Batch processing
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batch:
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max_batch_size: 32
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timeout_ms: 5000
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# Modell-uppdatering
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model_update:
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auto_retrain: false
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retrain_threshold: 0.05 # mAP drop
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retrain_schedule: "weekly"
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