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InSPyReNet_SwinB_DIS5K_LR.yaml
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Model:
name: "InSPyReNet_SwinB"
depth: 64
pretrained: True
base_size: [384, 384]
threshold: 512
Train:
Dataset:
type: "RGB_Dataset"
root: "data/Train_Dataset"
sets: ['DIS-TR']
transforms:
static_resize:
size: [384, 384]
random_scale_crop:
range: [0.75, 1.25]
random_flip:
lr: True
ud: False
random_rotate:
range: [-10, 10]
random_image_enhance:
methods: ['contrast', 'sharpness', 'brightness']
tonumpy: NULL
normalize:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
totensor: NULL
Dataloader:
batch_size: 6
shuffle: True
num_workers: 8
pin_memory: False
Optimizer:
type: "Adam"
lr: 1.0e-05
weight_decay: 0.0
mixed_precision: False
Scheduler:
type: "PolyLr"
epoch: 180
gamma: 0.9
minimum_lr: 1.0e-07
warmup_iteration: 12000
Checkpoint:
checkpoint_epoch: 1
checkpoint_dir: "snapshots/InSPyReNet_SwinB_DIS5K_LR"
Debug:
keys: ['saliency', 'laplacian']
Test:
Dataset:
type: "RGB_Dataset"
root: "data/Test_Dataset"
sets: ['DIS-VD', 'DIS-TE1', 'DIS-TE2', 'DIS-TE3', 'DIS-TE4']
transforms:
dynamic_resize:
L: 1280
tonumpy: NULL
normalize:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
totensor: NULL
Dataloader:
num_workers: 8
pin_memory: True
Checkpoint:
checkpoint_dir: "snapshots/InSPyReNet_SwinB_DIS5K_LR"
Eval:
gt_root: "data/Test_Dataset"
pred_root: "snapshots/InSPyReNet_SwinB_DIS5K_LR"
result_path: "results"
datasets: ['DIS-VD', 'DIS-TE1', 'DIS-TE2', 'DIS-TE3', 'DIS-TE4']
metrics: ['Sm', 'mae', 'adpEm', 'maxEm', 'avgEm', 'adpFm', 'maxFm', 'avgFm', 'wFm', 'mBA']