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9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## Configuration Files
#### Model Configuration
- We use a yaml file to specify the hyperparameters of a model. All the training logs will be placed in `${project_root}/runs/${model_name}/${experiment_id}`. An example are shown below.
```yaml
model:
name: Recce # Model Name
num_classes: 1
config:
lambda_1: 0.1 # balancing weight for L_r
lambda_2: 0.1 # balancing weight for L_m
distribute:
backend: nccl
optimizer:
name: adam
lr: 0.0002
weight_decay: 0.00001
scheduler:
name: StepLR
step_size: 22500
gamma: 0.5
resume: False
resume_best: False
id: FF++c40 # Specify a unique experiment id.
loss: binary_ce # Loss type, either 'binary_ce' or 'cross_entropy'.
metric: Acc # Main metric, either 'Acc', 'AUC', or 'LogLoss'.
debug: False
device: "cuda:1" # NOTE: Used only when testing, annotation this line when training.
ckpt: best_model_1000 # NOTE: Used only when testing to specify a checkpoint id, annotating this line when training.
data:
train_batch_size: 32
val_batch_size: 64
test_batch_size: 64
name: FaceForensics
file: "./config/dataset/faceforensics.yml" # config file for a dataset
train_branch: "train_cfg"
val_branch: "test_cfg"
test_branch: "test_cfg"
```
- We set different hyper-parameters for the learning rate scheduler according to the used dataset as follows:
- FaceForensics++: The learning rate is decayed by 0.5 every 10 epochs.
- Celeb-DF: The learning rate is decayed by 0.5 every 10 epochs.
- WildDeepfake: The learning rate is decayed by 0.9 every 3000 iterations.
- DFDC: The learning rate is decayed by 0.5 every 3 epochs.
#### Dataset Configuration
- We also use a yaml file to specify the dataset to load for the experiment. These files are placed under `config/dataset/` subfold.
- Briefly, you should change the `root` parameter according to your storage path. |