seed: -1
device: cuda
use_wandb: False
wandb_project: project_name  # enter your project name here
wandb_entity: project_entity  # enter your entity here
results_folder: results  # if not use wandb, this is the folder where the results will be saved

resx: 768
resy: 432
example_config: "car-turn_winter.yaml"

save_model_starting_epoch: 900
multiply_foreground_alpha: True

flip_p: 0.5  # probability of applying flip before cnn
n_aug: 6  # set to -1 to disable augmentation before CLIP
clip_affine_transform_fill: 0  # 0 for black, 1 for white
clip_model_name: "ViT-B/32"  # ViT-B/16 | ViT-B/32 | ViT-L/14

text_criterion: spherical  # spherical | cosine | scaled_cosine (*1.2)

bootstrap_text: ""
bootstrap_scheduler: none
bootstrap_epoch: -1  # epoch to stop penalizing sparsity
use_negative_bootstrap: False  # whether to use negative relevance
lambda_bootstrap_min: 0
bootstrap_negative_text: []  # negative alpha - will ignore this
bootstrap_negative_map_threshold: 0.6  # penalizing only locations with high values in relevancy
bootstrapping_min_cover: 1
relevancy_num_layers: 10

lambda_screen: 1
lambda_sparsity: 0.1  # lambda_sparsity * ( lambda_alpha_l0 * L0_loss + lambda_alpha_l1 * L1_loss )
lambda_alpha_l0: 0.005
lambda_alpha_l1: 0.01
lambda_structure: 3
lambda_bootstrap: 10
lambda_clip: 1  # lambda_clip * ( lambda_comp_clip * L_comp + lambda_layer_clip * L_layer )
lambda_composition: 1

n_epochs: 3000
gamma: 0.999
min_lr: 0.00001
lr: 0.0025
optimizer: madgrad  # [adam | radam | rmsprop | sgd]

# the following is relevant only for dip_backbones backbone
skip_n33d: 128
skip_n33u: 128
skip_n11: 4
num_scales: 7

log_images_freq: 500
center_frame_distance: 2

input_entire_atlas: True
entire_atlas_every: 75
return_atlas_alpha: False

grid_atlas_resolution: 2000
align_corners: False

crops_min_cover: 0.95  # 0.95 for foreground, 0.8 for background

masks_border_expansion: 30
mask_alpha_threshold: 0.95