scripts/kinetics/videomae_vit_base_patch16_224_tubemasking_ratio_0.9_epoch_800/finetune.sh
# Set the path to save checkpoints
OUTPUT_DIR='YOUR_PATH/k400_videomae_pretrain_base_patch16_224_frame_16x4_tube_mask_ratio_0.9_e800/eval_lr_1e-3_epoch_100'
# path to Kinetics set (train.csv/val.csv/test.csv)
DATA_PATH='YOUR_PATH/list_kinetics-400'
# path to pretrain model
MODEL_PATH='YOUR_PATH/k400_videomae_pretrain_base_patch16_224_frame_16x4_tube_mask_ratio_0.9_e800/checkpoint-799.pth'

# batch_size can be adjusted according to number of GPUs
# this script is for 64 GPUs (8 nodes x 8 GPUs)
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8 \
    --master_port 12320 --nnodes=8  --node_rank=$1 --master_addr=$2 \
    run_class_finetuning.py \
    --model vit_base_patch16_224 \
    --data_set Kinetics-400 \
    --nb_classes 400 \
    --data_path ${DATA_PATH} \
    --finetune ${MODEL_PATH} \
    --log_dir ${OUTPUT_DIR} \
    --output_dir ${OUTPUT_DIR} \
    --batch_size 8 \
    --num_sample 1 \
    --input_size 224 \
    --short_side_size 224 \
    --save_ckpt_freq 10 \
    --num_frames 16 \
    --sampling_rate 4 \
    --opt adamw \
    --lr 1e-3 \
    --opt_betas 0.9 0.999 \
    --weight_decay 0.05 \
    --epochs 100 \
    --dist_eval \
    --test_num_segment 5 \
    --test_num_crop 3 \
    --enable_deepspeed 