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Fedavg and YOLOv11 training

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TY1667
2025-10-02 16:26:27 +08:00
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config/coco_cfg.yaml Normal file
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# 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: 64 # 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: "/home/image1325/ssd1/dataset/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

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