127 lines
		
	
	
		
			3.5 KiB
		
	
	
	
		
			YAML
		
	
	
	
	
	
			
		
		
	
	
			127 lines
		
	
	
		
			3.5 KiB
		
	
	
	
		
			YAML
		
	
	
	
	
	
# global system:
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fed_algo: "FedAvg"                # federated learning algorithm
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model_name: "yolo_v11_n"          # yolo_v11_n, yolo_v11_t, yolo_v11_s, yolo_v11_m, yolo_v11_l, yolo_v11_x
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i_seed: 202509                    # initial random seed
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num_client: 5                     # total number of clients
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num_round: 5                      # total number of communication rounds
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num_local_class: 80               # number of classes per client
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res_root: "results"               # root directory for results
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dataset_path: "/mnt/DATA/COCO128/"
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# train_txt: "train.txt"            # path to training set txt file
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# val_txt: "val.txt"                # path to validation set txt file
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# test_txt: "test.txt"              # path to test set txt file                 
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local_batch_size: 32              # local training batch size
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val_batch_size: 4                 # validation batch size
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num_workers: 4                    # number of data loader workers
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min_data: 128                     # minimum number of images per client
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max_data: 128                     # maximum number of images per client
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partition_mode: "overlap"         # "overlap" or "disjoint"
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connection_ratio: 1               # connection ratio, e.g., 1.0 means all clients
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# local training:
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min_lr: 0.000100000000            # initial learning rate
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max_lr: 0.010000000000            # maximum learning rate
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momentum: 0.9370000000            # SGD momentum/Adam beta1
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weight_decay: 0.000500            # optimizer weight decay
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warmup_epochs: 3.00000            # warmup epochs
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box: 7.500000000000000            # box loss gain
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cls: 0.500000000000000            # cls loss gain
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dfl: 1.500000000000000            # dfl loss gain
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hsv_h: 0.0150000000000            # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7000000000000            # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4000000000000            # image HSV-Value augmentation (fraction)
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degrees: 0.00000000000            # image rotation (+/- deg)
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translate: 0.100000000            # image translation (+/- fraction)
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scale: 0.5000000000000            # image scale (+/- gain)
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shear: 0.0000000000000            # image shear (+/- deg)
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flip_ud: 0.00000000000            # image flip up-down (probability)
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flip_lr: 0.50000000000            # image flip left-right (probability)
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mosaic: 1.000000000000            # image mosaic (probability)
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mix_up: 0.000000000000            # image mix-up (probability)
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names:
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  0: person
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  1: bicycle
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  2: car
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  3: motorcycle
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  4: airplane
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  5: bus
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  6: train
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  7: truck
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  8: boat
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  9: traffic light
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  10: fire hydrant
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  11: stop sign
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  12: parking meter
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  13: bench
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  14: bird
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  15: cat
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  16: dog
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  17: horse
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  18: sheep
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  19: cow
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  20: elephant
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  21: bear
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  22: zebra
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  23: giraffe
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  24: backpack
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  25: umbrella
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  26: handbag
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  27: tie
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  28: suitcase
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  29: frisbee
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  30: skis
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  31: snowboard
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  32: sports ball
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  33: kite
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  34: baseball bat
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  35: baseball glove
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  36: skateboard
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  37: surfboard
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  38: tennis racket
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  39: bottle
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  40: wine glass
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  41: cup
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  42: fork
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  43: knife
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  44: spoon
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  45: bowl
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  46: banana
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  47: apple
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  48: sandwich
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  49: orange
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  50: broccoli
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  51: carrot
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  52: hot dog
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  53: pizza
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  54: donut
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  55: cake
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  56: chair
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  57: couch
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  58: potted plant
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  59: bed
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  60: dining table
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  61: toilet
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  62: tv
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  63: laptop
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  64: mouse
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  65: remote
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  66: keyboard
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  67: cell phone
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  68: microwave
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  69: oven
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  70: toaster
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  71: sink
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  72: refrigerator
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  73: book
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  74: clock
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  75: vase
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  76: scissors
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  77: teddy bear
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  78: hair drier
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  79: toothbrush
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