config_default_example.yaml

# Preprocessing
clip_length:       16                    # Number of frames within a clip 
clip_offset:       0                     # Frame offset between beginning of video and clip (1st clip only) 
clip_stride:       1                     # Frame offset between successive clips, must be >= 1 
crop_shape:        [112,112]             # (Height, Width) of frame  
crop_type:         Random                # Type of cropping operation (Random, Central and None)  
final_shape:       [112,112]             # (Height, Width) of input to be given to CNN
num_clips:         -1                    # Number clips to be generated from a video (<0: uniform sampling, 0: Divide entire video into clips, >0: Defines number of clips) 
random_offset:     0                     # Boolean switch to generate a clip length sized clip from a video 
resize_shape:      [128,171]             # (Height, Width) to resize original data 
subtract_mean:     ''                    # Subtract mean (R,G,B) from all frames during preprocessing

# Experiment Setup 
acc_metric:        Accuracy              # Accuracy metric 
batch_size:        15                     # Numbers of videos in a mini-batch 
dataset:           HMDB51                # Name of dataset 
debug:             0                     # If True, do not plot, save, or create data files 
epoch:             30                    # Total number of epochs 
exp:               exp                   # Experiment name
gamma:             0.1                   # Multiplier with which to change learning rate
grad_max_norm:     0                     # Norm for gradient clipping
json_path:         /z/dat/HMDB51/        # Path to the json file for the given dataset
labels:            51                    # Number of total classes in the dataset
load_type:         train                 # Environment selection, to include only training/training and validation/testing dataset
loss_type:         M_XENTROPY            # Loss function
lr:                0.0001                # Learning rate
milestones:        [10, 20]              # Epoch values to change learning rate     
model:             C3D                   # Name of model to be loaded  
momentum:          0.9                   # Momentum value in optimizer
num_workers:       2                     # Number of CPU worker used to load data
opt:               sgd                   # Name of optimizer
preprocess:        default               # String argument to select preprocessing type
pretrained:        1                     # Load pretrained network 
pseudo_batch_loop: 1                     # Pseudo-batch size multiplier to mimic large minibatches 
rerun:             1                     # Number of trials to repeat an experiment
save_dir:          './results'           # Path to results directory
seed:              999                   # Seed for reproducibility 
weight_decay:      0.0005                # Weight decay
resume:            0                     # Flag to resume training or switch to alternate objective after loading