Here we describe the different parameters set in each configuration file:
- frame_dir: Directory where frames are stored.
- save_dir: Directory to save dataset information.
- store_dir: Directory to save model checkpoints, predictions, etc.
- store_mode: 'store' if it's the first time running the script to prepare and store dataset information, or 'load' to load previously stored information.
- batch_size: Batch size.
- clip_len: Length of the clips in number of frames.
- crop_dim: Dimension to crop the frames (if needed).
- dataset: Name of the dataset ('finediving', 'fs_comp', or 'fs_perf').
- radi_displacement: Radius of displacement used.
- epoch_num_frames: Number of frames used per epoch.
- feature_arch: Feature extractor architecture ('rny002_gsf' or 'rny008_gsf').
- learning_rate: Learning rate.
- mixup: Boolean indicating whether to use mixup or not.
- modality: Input modality used ('rgb').
- num_classes: Number of classes for the current dataset.
- num_epochs: Number of epochs for training.
- warm_up_epochs: Number of warm-up epochs.
- start_val_epoch: Epoch where validation evaluation starts.
- temporal_arch: Temporal architecture used ('ed_sgp_mixer').
- n_layers: Number of blocks/layers used for the temporal architecture.
- sgp_ks: Kernel size of the SGP and SGP-Mixer layers.
-
sgp_r:
$r$ factor in SGP and SGP-Mixer layers. - test_only: Boolean indicating if only inference is performed or training + inference.
- criterion: Criterion used for validation evaluation ('map', 'loss').
- num_workers: Number of workers.