| # log dir | |
| log_dir: /mntcephfs/lab_data/zhiyuanyan/benchmark_results/logs_final/spsl_4frames | |
| # model setting | |
| pretrained: ../weights/xception_best.pth # path to a pre-trained model, if using one | |
| # pretrained: /home/tianshuoge/resnet34-b627a593.pth # path to a pre-trained model, if using one | |
| model_name: spsl # model name | |
| backbone_name: xception # backbone name | |
| #backbone setting | |
| backbone_config: | |
| mode: original # shallow_xception | |
| num_classes: 2 | |
| inc: 4 | |
| dropout: false | |
| # 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: [FF-FS] | |
| test_dataset: [FaceForensics++, FF-F2F, FF-DF, FF-FS, FF-NT] | |
| compression: c23 # compression-level for videos | |
| train_batchSize: 32 # training batch size | |
| test_batchSize: 32 # test batch size | |
| workers: 8 # number of data loading workers | |
| frame_num: {'train': 4, 'test': 32} # number of frames to use per video in training and testing | |
| resolution: 256 # 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 | |
| save_ckpt: true # whether to save checkpoint | |
| save_feat: true # whether to save features | |
| # data augmentation | |
| use_data_augmentation: true # 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: [0.5, 0.5, 0.5] | |
| std: [0.5, 0.5, 0.5] | |
| # optimizer config | |
| optimizer: | |
| # choose between 'adam' and 'sgd' | |
| type: adam | |
| adam: | |
| lr: 0.0002 # 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: 10 # 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: false # whether to save checkpoint | |
| # 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 | |