script_name=$(basename "$0")
device_num="0"
batch_size=32
lr=0.0005
# args.lr = args.lr * total_batch_size / 256
epochs=150
k_folds=5
OUTPUT_DIR="YOUR PATH"
# path to pretrain model
MODEL_PATH='YOUR PATH opt'
DATA_PATH='YOUR PATH'
cd YOUR PATH
nohup python K_folds_finetuning.py \
--hsta_num 4 \
--depth_choice_num 2 \
--model cross_uni_hsta_base_224 \
--num_frames 6 \
--fill_by_zeros_or_img img \
--no_save_ckpt \
--use_emothion_or_objective_class_as_label objective_class \
--use_weight_loss False \
--data_set casme3 \
--nb_classes 7 \
--data_path ${DATA_PATH} \
--log_dir ${OUTPUT_DIR} \
--k_folds ${k_folds} \
--output_dir ${OUTPUT_DIR} \
--batch_size ${batch_size} \
--device_num ${device_num} \
--input_size 224 \
--short_side_size 224 \
--save_ckpt_freq 200 \
--sampling_rate 4 \
--opt adamw \
--lr ${lr} \
--val_freq 1 \
--opt_betas 0.9 0.999 \
--weight_decay 0.05 \
--sh_name ${script_name} \
--epochs ${epochs} \
--test_num_segment 2 \
--test_num_crop 3 \
--prefetch_generator \
--fc_drop_rate 0.4 \
--drop 0.2 \
--drop_path 0.2 \
--layer_decay 0.90 \
--no_noise \
--model_header_mode 2 >> "YOUR PATH"${script_name}.txt 2>&1 &


 




















