Upload tall.yaml

tall.yaml

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+# model setting
+pretrained: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth  # path to a pre-trained model, if using one
+model_name: tall   # model name
+
+mask_grid_size: 16
+num_classes: 2
+embed_dim: 128
+mlp_ratio: 4.0
+patch_size: 4
+window_size: [14, 14, 14, 7]
+depths: [2, 2, 18, 2]
+num_heads: [4, 8, 16, 32]
+ape: true # use absolution position embedding
+thumbnail_rows: 2
+drop_rate: 0
+drop_path_rate: 0.1
+
+# dataset
+all_dataset: [FaceForensics++, FF-F2F, FF-DF, FF-FS, FF-NT, FaceShifter, DeepFakeDetection, Celeb-DF-v1, Celeb-DF-v2, DFDCP, DFDC, DeeperForensics-1.0, UADFV]
+train_dataset: [FaceForensics++]
+test_dataset: [Celeb-DF-v2]
+
+compression: c23  # compression-level for videos
+train_batchSize: 64   # training batch size
+test_batchSize: 64   # test batch size
+workers: 4   # number of data loading workers
+frame_num: {'train': 32, 'test': 32}   # number of frames to use per video in training and testing
+resolution: 224   # resolution of output image to network
+with_mask: false   # whether to include mask information in the input
+with_landmark: false   # whether to include facial landmark information in the input
+video_mode: True  # whether to use video-level data
+clip_size: 4  # number of frames in each clip, should be square number of an integer
+dataset_type: tall
+
+# data augmentation
+use_data_augmentation: false  # Add this flag to enable/disable data augmentation
+data_aug:
+  flip_prob: 0.5
+  rotate_prob: 0.5
+  rotate_limit: [-10, 10]
+  blur_prob: 0.5
+  blur_limit: [3, 7]
+  brightness_prob: 0.5
+  brightness_limit: [-0.1, 0.1]
+  contrast_limit: [-0.1, 0.1]
+  quality_lower: 40
+  quality_upper: 100
+
+# mean and std for normalization
+mean: [0.485, 0.456, 0.406]
+std: [0.229, 0.224, 0.225]
+
+# optimizer config
+optimizer:
+  # choose between 'adam' and 'sgd'
+  type: adam
+  adam:
+    lr: 0.00002  # learning rate
+    beta1: 0.9  # beta1 for Adam optimizer
+    beta2: 0.999 # beta2 for Adam optimizer
+    eps: 0.00000001  # epsilon for Adam optimizer
+    weight_decay: 0.0005  # weight decay for regularization
+    amsgrad: false
+  sgd:
+    lr: 0.0002  # learning rate
+    momentum: 0.9  # momentum for SGD optimizer
+    weight_decay: 0.0005  # weight decay for regularization
+
+# training config
+lr_scheduler: null   # learning rate scheduler
+nEpochs: 100   # number of epochs to train for
+start_epoch: 0   # manual epoch number (useful for restarts)
+save_epoch: 1   # interval epochs for saving models
+rec_iter: 100   # interval iterations for recording
+logdir: ./logs   # folder to output images and logs
+manualSeed: 1024   # manual seed for random number generation
+save_ckpt: true   # whether to save checkpoint
+save_feat: true   # whether to save features
+
+# loss function
+loss_func: cross_entropy   # loss function to use
+losstype: null
+
+# metric
+metric_scoring: auc   # metric for evaluation (auc, acc, eer, ap)
+
+# cuda
+cuda: true   # whether to use CUDA acceleration
+cudnn: true   # whether to use CuDNN for convolution operations