dataset = "lizard"
data_path_liz = ""
data_path_mit = ""
hard_labels = ""
encoder = "convnextv2_large.fcmae_ft_in22k_in1k"
out_channels_cls = 8
inst_channels = 5
pretrained = true
batch_size = 24
validation_batch_size = 64
weight_decay = 0.0001
learning_rate = 0.0001
min_learning_rate = 1e-8
training_steps = 200000
validation_step = 1000
checkpoint_step = 10000
fl_gamma = 2
loss_lambda = 0.02
tta = 16
eval_optim_metric = "mpq"
n_rounds = 5
save = false
f1_metric_ccrop = 248
match_euc_dist = 6
eval_criteria = "lizard|alt"
max_hole_size = 50
checkpoint_path = ""
experiment = "lizard_convnextv2_large"
seed = 42
fold = 0
test_as_val = true
optim_metric = "mpq"
num_workers = 4
use_amp = true
color_scale = 0.2

[aug_params_fast.mirror]
prob_x = 0.5
prob_y = 0.5
prob = 0.5

[aug_params_fast.translate]
max_percent = 0.05
prob = 0.2

[aug_params_fast.scale]
min = 0.8
max = 1.2
prob = 0.2

[aug_params_fast.zoom]
min = 0.8
max = 1.2
prob = 0.2

[aug_params_fast.rotate]
max_degree = 179
prob = 0.75

[aug_params_fast.shear]
max_percent = 0.1
prob = 0.2

[aug_params_fast.elastic]
alpha = [ 120, 120,]
sigma = 8
prob = 0.5