types.yaml

# Types to run maskAL.yaml file

# folders
weightsroot: 'string'
resultsroot: 'string'
dataroot: 'string'
initial_train_dir: 'string'
traindir: 'string'
valdir: 'string'
testdir: 'string'

# data options
use_initial_train_dir: 'boolean'
duplicate_initial_model_and_data: 'boolean'
initial_train_file: 'string'

# network parameters
network_config: 'string'
pretrained_weights: 'string'

# training-parameters
cuda_visible_devices: 'string'
classes: 'list'
transfer_learning_on_previous_models: 'boolean'
learning_rate: 'float'
warmup_iterations: 'integer'
train_iterations_base: 'integer'
train_iterations_step_size: 'integer'
step_image_number: 'integer'
eval_period: 'integer'
checkpoint_period: 'integer'
weight_decay: 'float'
learning_policy: 'string'
step_ratios: ['float', 'list']
gamma: 'float'
train_batch_size: 'integer'
num_workers: 'integer'

# train-sampler
train_sampler: 'string'
minority_classes: ['string', 'list']
repeat_factor_smallest_class: 'float'

# evaluation-parameters
confidence_threshold: 'float'
nms_threshold: 'float'

# active-learning sampling
experiment_name: 'string'
strategy: ['string', 'list']
mode: ['string', 'list']
initial_datasize: 'integer'
pool_size: ['integer', 'list']
loops: 'integer'
dropout_probability: 'float'
mcd_iterations: 'integer'
iou_thres: 'float'
auto_annotate: 'boolean'
export_format: 'string'
supervisely_meta_json: 'string'

# incremental learning
incremental_learning: 'boolean'
sampling_percentage_per_subset: 'integer'