Pretraining VideoMAE on HMDB51 about videomae

[jianyulei1]

Hi chen,

Can you share your settings for reference？My training doesn 't work, I ' d like to refer to your settings for UCF101 and HMDB51.

[chenliz1]

Hi Jianyu,

Here's my seetings for HMDB51 on 8 GPUs 1 Server:

OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8
--master_port 12320
run_mae_pretraining.py
--data_path ${DATA_PATH}
--mask_type tube
--mask_ratio 0.9
--model pretrain_videomae_base_patch16_224
--decoder_depth 4
--batch_size 24
--num_frames 16
--sampling_rate 2
--opt adamw
--opt_betas 0.9 0.95
--warmup_epochs 40
--save_ckpt_freq 100
--epochs 4800
--log_dir ${OUTPUT_DIR}
--output_dir ${OUTPUT_DIR}
--lr 3e-4

The setting for UCF101 is similar, only need to modify the epochs number to 3200 and sampling_rate to 4.

[jianyulei1]

Thank you for your reply, can you share the fine-tuning settings for HMDB51 and UCF101 with me? Thank you very much.

…

------------------ 原始邮件 ------------------ 发件人: "MCG-NJU/VideoMAE" ***@***.***>; 发送时间: 2022年7月27日(星期三) 凌晨5:34 ***@***.***>; ***@***.******@***.***>; 主题: Re: [MCG-NJU/VideoMAE] Pretraining VideoMAE on HMDB51 (Issue #38) Hi Jianyu, Here's my seetings for HMDB51 on 8 GPUs 1 Server: OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8 --master_port 12320 run_mae_pretraining.py --data_path ${DATA_PATH} --mask_type tube --mask_ratio 0.9 --model pretrain_videomae_base_patch16_224 --decoder_depth 4 --batch_size 24 --num_frames 16 --sampling_rate 2 --opt adamw --opt_betas 0.9 0.95 --warmup_epochs 40 --save_ckpt_freq 100 --epochs 4800 --log_dir ${OUTPUT_DIR} --output_dir ${OUTPUT_DIR} --lr 3e-4 The setting for UCF101 is similar, only need to modify the epochs number to 3200 and sampling_rate to 4. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***>

[chenliz1]

For UCF101:

OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8
--master_port 12320
run_class_finetuning.py
--model vit_base_patch16_224
--data_set UCF101
--nb_classes 101
--data_path ${DATA_PATH}
--finetune ${MODEL_PATH}
--log_dir ${OUTPUT_DIR}
--output_dir ${OUTPUT_DIR}
--batch_size 8
--update_freq 2
--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
# --eval

[jianyulei1]

Thank you for your reply, if I want to set batch size to 8, LR need to be changed?

[chenliz1]

Thank you for your reply, if I want to set batch size to 8, LR need to be changed?
For the finetuning, I set --batch_size 8 and --update_freq 2, so theoretically I am training the model with 128 batches on 8 GPUs. But if you want to use a different total batch size, you may need to tune the LR for better results.

[youcaiSUN]

Hi, @chenliz1, could you share the time it takes to pre-train on HMDB51 and UCF101 in your setting? Besides, what is your reproduced result on UCF101? Thanks!

[Morning-YU]

Hi, @chenliz1 , can you tell me how to process UCF101 dataset？What the data format looks like？Looking forward to your reply！

[yztongzhan]

Hi @chenliz1 @jianyulei1 @youcaiSUN @Morning-YU ! The pre-trained models and scripts on UCF101 are available! Scripts on HMDB51 are coming soon!