86 lines
1.5 KiB
YAML
86 lines
1.5 KiB
YAML
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# ATM Anomaly Detection Training Configuration
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# Model
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model:
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base: yolov8n.pt # Options: yolov8n, yolov8s, yolov8m, yolov8l, yolov8x
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pretrained: true
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classes: 14 # Number of anomaly classes
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# Training
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training:
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epochs: 100
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batch_size: 16
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image_size: 640
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learning_rate: 0.001
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optimizer: AdamW
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weight_decay: 0.0005
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momentum: 0.937
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# Augmentation
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augmentation:
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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: 5.0
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translate: 0.1
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scale: 0.5
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shear: 2.0
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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: 0.0
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copy_paste: 0.0
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# Loss
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box_loss_gain: 7.5
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cls_loss_gain: 0.5
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dfl_loss_gain: 1.5
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# Data
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data:
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train: data/splits/train
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val: data/splits/val
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test: data/splits/test
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# Class names (must match database schema)
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names:
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0: physical_damage
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1: vandalism
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2: graffiti
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3: dirt_debris
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4: obstruction
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5: skimming_device
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6: suspicious_attachment
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7: out_of_service
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8: screen_damage
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9: cash_jam
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10: receipt_jam
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11: lighting_failure
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12: camera_blind
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13: network_down
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# Validation
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validation:
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conf_threshold: 0.25
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iou_threshold: 0.45
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max_detections: 300
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# Output
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output:
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checkpoint_dir: models/checkpoints
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export_dir: models/exports
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log_dir: logs
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# Hardware
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hardware:
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device: auto # auto, cpu, cuda:0
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workers: 8
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# Logging
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logging:
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project: atm_anomaly
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name: experiment_001
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save_period: 10
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plot: true
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