46 lines
1.2 KiB
YAML
46 lines
1.2 KiB
YAML
# config
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# model
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## support SegNet_AlexNet, SegNet_VGG, SegNet_ResNet18, SegNet_ResNet34, SegNet_ResNet50, SegNet_ResNet101, SegNet_ResNet152
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## FPN_ResNet18, FPN_ResNet50, FPN_ResNet101, FPN_ResNet34, FPN_ResNet152
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## FCN_AlexNet, FCN_VGG, FCN_ResNet18, FCN_ResNet50, FCN_ResNet101, FCN_ResNet34, FCN_ResNet152
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## UNet_VGG
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model_name: FCN # choose from FPN, FCN, SegNet, UNet
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backbone: AlexNet # choose from AlexNet, VGG, ResNet18, ResNet50, ResNet101
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# dataset path
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data_root: samples/data/
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boundary: one_point # choose from rm_wall, one_point, all_walls
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# train/val set
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train_list: train/train_val.txt
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# test set
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## choose the test set: test_0.txt, test_1.txt, test_2.txt, test_3.txt,test_4.txt,test_5.txt,test_6.txt
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test_list: test/test_0.txt
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# metric for testing
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## choose from "mae_global", "mae_boundary", "mae_component",
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## "value_and_pos_error_of_maximum_temperature", "max_tem_spearmanr", "global_image_spearmanr"
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metric: mae_boundary
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# dataset format: mat or h5
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data_format: mat
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batch_size: 2
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max_epochs: 50
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lr: 0.001
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# number of gpus to use
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gpus: 1
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val_check_interval: 1.0
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# num_workers in dataloader
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num_workers: 4
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# preprocessing of data
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## input
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mean_layout: 0
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std_layout: 1000
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## output
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mean_heat: 298
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std_heat: 50 |