train:
  epochs: 200
  batch-size: 14 #
  cfg: ./cfg/yolov3-spp-19cls.cfg
  data: ./data/widok_01_19.data
  multi-scale: false
  img-size: '768 1280'
  rect: false
  resume: false
  nosave: false
  notest: false
  evolve: false
  bucket:
  cache-images: false
  weights: /home/tomekb/yolov3/weights/yolov3-spp-ultralytics.pt
  device: 1
  adam: true
  single-cls: false
  snapshot-every: 50
  freeze-layers: true

  # inne hiperparametry
  other-hyps:
    giou: 3.53  # giou loss gain
    cls: 37.4  # cls loss gain
    cls_pw: 1.0  # cls BCELoss positive_weight
    obj: 64.3  # obj loss gain (*=img_size/320 if img_size != 320)
    obj_pw: 1.0  # obj BCELoss positive_weight
    iou_t: 0.20  # iou training threshold
    lr0: 0.01  # initial learning rate (SGD=5E-3 Adam=5E-4)
    lrf: 0.0005  # final learning rate (with cos scheduler)
    momentum: 0.937  # SGD momentum
    weight_decay: 0.0005  # optimizer weight decay
    fl_gamma: 0.0  # focal loss gamma (efficientDet default is gamma=1.5)
    hsv_h: 0.0138  # image HSV-Hue augmentation (fraction)
    hsv_s: 0.678  # image HSV-Saturation augmentation (fraction)
    hsv_v: 0.36  # image HSV-Value augmentation (fraction)
    degrees: 0 # 1.98 * 0  # image rotation (+/- deg)
    translate: 0 # 0.05 * 0  # image translation (+/- fraction)
    scale: 0  #0 .05 * 0  # image scale (+/- gain)
    shear: 0 # 0.641 * 0  # image shear (+/- deg)

experiments:
  dir: ./experiments
detect:
  source: ./data/widok_01_19/widok_01_19_test_labels.txt
  test-img-size: 1024
  conf-thres: 0.3
  iou-thres: 0.6
  classes:
  agnostic-nms:
  augment:
confussion-matrix:
  labels-dir: ./data/widok_01_19/widok_01_19_labels
bayes:
  todo: todo