# Hyperparameters for training
# To set range- 
# Provide min and max values as:
#      parameter:
#         
#         min: scalar
#         max: scalar
# OR
#
# Set a specific list of search space-
#     parameter: 
#         values: [scalar1, scalar2, scalar3...]
#         
# You can use grid, bayesian and hyperopt search strategy 
# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration

program: utils/loggers/wandb/sweep.py
method: random
metric:
  name: metrics/mAP_0.5
  goal: maximize

parameters:
  # hyperparameters: set either min, max range or values list
  data:
    value: "data/coco128.yaml"
  batch_size:
    values: [64]
  epochs:
    values: [10]

  lr0:
    distribution: uniform
    min: 1e-5
    max: 1e-1
  lrf:
    distribution: uniform
    min: 0.01
    max: 1.0
  momentum:
    distribution: uniform
    min: 0.6
    max: 0.98
  weight_decay:
    distribution: uniform
    min: 0.0
    max: 0.001
  warmup_epochs:
    distribution: uniform
    min: 0.0
    max: 5.0
  warmup_momentum:
    distribution: uniform
    min: 0.0
    max: 0.95
  warmup_bias_lr:
    distribution: uniform
    min: 0.0
    max: 0.2
  box:
    distribution: uniform
    min: 0.02
    max: 0.2
  cls:
    distribution: uniform
    min: 0.2
    max: 4.0
  cls_pw:
    distribution: uniform
    min: 0.5
    max: 2.0
  obj:
    distribution: uniform
    min: 0.2
    max: 4.0
  obj_pw:
    distribution: uniform
    min: 0.5
    max: 2.0
  iou_t:
    distribution: uniform
    min: 0.1
    max: 0.7
  anchor_t:
    distribution: uniform
    min: 2.0
    max: 8.0
  fl_gamma:
    distribution: uniform
    min: 0.0
    max: 0.1
  hsv_h:
    distribution: uniform
    min: 0.0
    max: 0.1
  hsv_s:
    distribution: uniform
    min: 0.0
    max: 0.9
  hsv_v:
    distribution: uniform
    min: 0.0
    max: 0.9
  degrees:
    distribution: uniform
    min: 0.0
    max: 45.0
  translate:
    distribution: uniform
    min: 0.0
    max: 0.9
  scale:
    distribution: uniform
    min: 0.0
    max: 0.9
  shear:
    distribution: uniform
    min: 0.0
    max: 10.0
  perspective:
    distribution: uniform
    min: 0.0
    max: 0.001
  flipud:
    distribution: uniform
    min: 0.0
    max: 1.0
  fliplr:
    distribution: uniform
    min: 0.0
    max: 1.0
  mosaic:
    distribution: uniform
    min: 0.0
    max: 1.0
  mixup:
    distribution: uniform
    min: 0.0
    max: 1.0
  copy_paste:
    distribution: uniform
    min: 0.0
    max: 1.0