48 lines
2.4 KiB
YAML
48 lines
2.4 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: 100 # total number of clients
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num_round: 500 # total number of communication rounds
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num_local_class: 1 # number of classes per client
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res_root: "results" # root directory for results
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dataset_path: "/home/image1325/ssd1/dataset/uav/"
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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: 16 # validation batch size
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num_workers: 4 # number of data loader workers
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min_data: 640 # minimum number of images per client
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max_data: 720 # 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: uav
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