Model: name: "InSPyReNet_Res2Net50" depth: 64 pretrained: True base_size: [384, 384] threshold: NULL Train: Dataset: type: "RGB_Dataset" root: "data/Train_Dataset" sets: ['DUTS-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: True Optimizer: type: "Adam" lr: 1.0e-05 weight_decay: 0.0 mixed_precision: False Scheduler: type: "PolyLr" epoch: 60 gamma: 0.9 minimum_lr: 1.0e-07 warmup_iteration: 12000 Checkpoint: checkpoint_epoch: 1 checkpoint_dir: "snapshots/InSPyReNet_Res2Net50" Debug: keys: ['saliency', 'laplacian'] Test: Dataset: type: "RGB_Dataset" root: "data/Test_Dataset" sets: ['DUTS-TE', 'DUT-OMRON', 'ECSSD', 'HKU-IS', 'PASCAL-S', 'DAVIS-S', 'HRSOD', 'UHRSD-TE'] transforms: static_resize: size: [384, 384] 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_Res2Net50" Eval: gt_root: "data/Test_Dataset" pred_root: "snapshots/InSPyReNet_Res2Net50" result_path: "results" datasets: ['DUTS-TE', 'DUT-OMRON', 'ECSSD', 'HKU-IS', 'PASCAL-S', 'DAVIS-S', 'HRSOD', 'UHRSD-TE'] metrics: ['Sm', 'mae', 'adpEm', 'maxEm', 'avgEm', 'adpFm', 'maxFm', 'avgFm', 'wFm', 'mBA']