# global system:
fed_algo: "FedAvg"                # federated learning algorithm
model_name: "yolo_v11_n"          # yolo_v11_n, yolo_v11_t, yolo_v11_s, yolo_v11_m, yolo_v11_l, yolo_v11_x
i_seed: 202509                    # initial random seed

num_client: 5                     # total number of clients
num_round: 5                      # total number of communication rounds
num_local_class: 80               # number of classes per client

res_root: "results"               # root directory for results
dataset_path: "/mnt/DATA/COCO128/"
# train_txt: "train.txt"            # path to training set txt file
# val_txt: "val.txt"                # path to validation set txt file
# test_txt: "test.txt"              # path to test set txt file                 

local_batch_size: 32              # local training batch size
val_batch_size: 4                 # validation batch size

num_workers: 4                    # number of data loader workers
min_data: 128                     # minimum number of images per client
max_data: 128                     # maximum number of images per client
partition_mode: "overlap"         # "overlap" or "disjoint"
connection_ratio: 1               # connection ratio, e.g., 1.0 means all clients

# local training:
min_lr: 0.000100000000            # initial learning rate
max_lr: 0.010000000000            # maximum learning rate
momentum: 0.9370000000            # SGD momentum/Adam beta1
weight_decay: 0.000500            # optimizer weight decay

warmup_epochs: 3.00000            # warmup epochs
box: 7.500000000000000            # box loss gain
cls: 0.500000000000000            # cls loss gain
dfl: 1.500000000000000            # dfl loss gain
hsv_h: 0.0150000000000            # image HSV-Hue augmentation (fraction)
hsv_s: 0.7000000000000            # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4000000000000            # image HSV-Value augmentation (fraction)
degrees: 0.00000000000            # image rotation (+/- deg)
translate: 0.100000000            # image translation (+/- fraction)
scale: 0.5000000000000            # image scale (+/- gain)
shear: 0.0000000000000            # image shear (+/- deg)
flip_ud: 0.00000000000            # image flip up-down (probability)
flip_lr: 0.50000000000            # image flip left-right (probability)
mosaic: 1.000000000000            # image mosaic (probability)
mix_up: 0.000000000000            # image mix-up (probability)
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush