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‎README.md‎

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# InSPyReNet: Inverse Saliency Pyramid Reconstruction Network for Salient Object Detection
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<p align="center">
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<img src="./figures/Title.png" alt="Logo" width="200" height="auto">
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</p>
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<p align="center">
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<img src="./figures/figure1.png" alt="Logo" width="300" height="auto">

‎configs/InSPyReNetD_Res2Net50.yaml‎

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Model:
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name: "InSPyReNetD_Res2Net50"
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depth: 64
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pretrained: True
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Train:
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Dataset:
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type: "RGB_Dataset"
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root: "data/RGB_Dataset/Train_Dataset"
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sets: ['DUTS-TR']
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transforms:
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resize:
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size: [384, 384]
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random_scale_crop:
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range: [0.75, 1.25]
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random_flip:
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lr: True
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ud: False
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random_rotate:
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range: [-10, 10]
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random_image_enhance:
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methods: ['contrast', 'sharpness', 'brightness']
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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Dataloader:
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batch_size: 6
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shuffle: True
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num_workers: 8
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pin_memory: True
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Optimizer:
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type: "Adam"
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lr: 1.0e-05
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weight_decay: 0.0
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mixed_precision: False
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Scheduler:
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type: "PolyLr"
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epoch: 60
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gamma: 0.9
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minimum_lr: 1.0e-07
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warmup_iteration: 12000
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Checkpoint:
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checkpoint_epoch: 1
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checkpoint_dir: "snapshots/InSPyReNetD_Res2Net50"
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Debug:
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keys: ['gaussian', 'laplacian']
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HyperTune:
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HyperParameter: ['Train', 'Optimizer', 'lr']
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Range: [1.0e-6, 2.4e-5, 24]
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Test:
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Dataset:
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type: "RGB_Dataset"
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root: "data/RGB_Dataset/Test_Dataset"
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sets: ['DUTS-TE', 'DUT-OMRON', 'ECSSD', 'HKU-IS', 'PASCAL-S']
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transforms:
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# resize:
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# size: [384, 384]
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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Dataloader:
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num_workers: 8
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pin_memory: True
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Checkpoint:
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checkpoint_dir: "snapshots/InSPyReNetD_Res2Net50"
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Eval:
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gt_root: "data/RGB_Dataset/Test_Dataset"
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pred_root: "snapshots/InSPyReNetD_Res2Net50"
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result_path: "results"
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datasets: ['DUTS-TE', 'DUT-OMRON', 'ECSSD', 'HKU-IS', 'PASCAL-S']
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metrics: ['Sm', 'mae', 'adpEm', 'maxEm', 'avgEm', 'adpFm', 'maxFm', 'avgFm', 'wFm']

‎configs/InSPyReNetD_SwinB.yaml‎

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Model:
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name: "InSPyReNetD_SwinB"
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depth: 64
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pretrained: True
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base_size: [384, 384]
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Train:
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Dataset:
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type: "RGBD_Dataset"
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root: "data/RGBD_Dataset/Train_Dataset"
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sets: ['NJU2K+NLPR']
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transforms:
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resize:
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size: [384, 384]
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random_scale_crop:
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range: [0.75, 1.25]
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random_flip:
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lr: True
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ud: False
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random_rotate:
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range: [-10, 10]
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random_image_enhance:
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methods: ['contrast', 'sharpness', 'brightness']
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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Dataloader:
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batch_size: 6
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shuffle: True
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num_workers: 8
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pin_memory: False
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Optimizer:
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type: "Adam"
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lr: 1.0e-05
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weight_decay: 0.0
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mixed_precision: False
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Scheduler:
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type: "PolyLr"
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epoch: 300
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gamma: 0.9
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minimum_lr: 1.0e-07
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warmup_iteration: 12000
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Checkpoint:
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checkpoint_epoch: 1
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checkpoint_dir: "snapshots/InSPyReNetD_SwinB"
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Debug:
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keys: ['gaussian', 'laplacian']
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HyperTune:
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HyperParameter: ['Train', 'Optimizer', 'lr']
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Range: [1.0e-6, 2.4e-5, 24]
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Test:
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Dataset:
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type: "RGBD_Dataset"
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root: "data/RGBD_Dataset/Test_Dataset"
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sets: ['DES', 'LFSD', 'NJU2K', 'NLPR', 'SIP', 'STERE']
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transforms:
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resize:
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size: [384, 384]
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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Dataloader:
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num_workers: 8
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pin_memory: True
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Checkpoint:
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checkpoint_dir: "snapshots/InSPyReNetD_SwinB"
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Eval:
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gt_root: "data/RGBD_Dataset/Test_Dataset"
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pred_root: "snapshots/InSPyReNetD_SwinB"
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result_path: "results"
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datasets: ['DES', 'LFSD', 'NJU2K', 'NLPR', 'SIP', 'STERE']
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metrics: ['Sm', 'mae', 'adpEm', 'maxEm', 'avgEm', 'adpFm', 'maxFm', 'avgFm', 'wFm']

‎configs/InSPyReNet_Res2Net101.yaml‎

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name: "InSPyReNet_Res2Net101"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 192
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Train:
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Dataset:
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/InSPyReNet_Res2Net50.yaml‎

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name: "InSPyReNet_Res2Net50"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 192
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Train:
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Dataset:
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/InSPyReNet_SwinB.yaml‎

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name: "InSPyReNet_SwinB"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 96
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base_size: [384, 384]
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Dataset:
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sets: ['DUTS-TE', 'DUT-OMRON', 'ECSSD', 'HKU-IS', 'PASCAL-S']
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transforms:
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resize:
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size: [384, 384]
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size: NULL #[384, 384]
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pm : False
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/InSPyReNet_SwinL.yaml‎

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name: "InSPyReNet_SwinL"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 192
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Train:
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Dataset:
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/InSPyReNet_SwinS.yaml‎

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name: "InSPyReNet_SwinS"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 192
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Train:
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Dataset:
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/InSPyReNet_SwinT.yaml‎

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name: "InSPyReNet_SwinT"
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depth: 64
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pretrained: True
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PM:
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patch_size: 384
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stride: 192
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base_size: [384, 384]
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PM:
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patch_size: 384
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stride: 192
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Dataset:
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

‎configs/SotA/BASNet.yaml‎

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name: "BASNet"
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depth: None
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pretrained: True
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patch_size: 384
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stride: 96
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transforms:
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resize:
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size: [384, 384]
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size: NULL #[384, 384]
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pm: False
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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totensor: NULL
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transforms_PM:
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dynamic_resize:
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patch_size: 384
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stride: 192
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resize:
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size: NULL
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pm : True
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tonumpy: NULL
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normalize:
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mean: [0.485, 0.456, 0.406]

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