ssv2 dataset acc
an1018 opened this issue · 2 comments
an1018 commented
I'm using some videos of ssv2 dataset, top1 acc and top2 acc are extremely low. What could be the reason for this?
This is the eval command:
OUTPUT_DIR='output_ssv2/eval_lr_5e-4_epoch_50/'
DATA_PATH='dataset/ssv2/csv'
MODEL_PATH='model_pretrained/checkpoint_finetune.pth'
OMP_NUM_THREADS=1 python3.7 -m torch.distributed.launch --nproc_per_node=8 \
--master_port 1233 --nnodes=1 --node_rank=0 --master_addr=127.0.0.1 \
run_class_finetuning.py \
--model vit_base_patch16_224 \
--data_set SSV2 \
--nb_classes 174 \
--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 \
--opt adamw \
--lr 5e-4 \
--opt_betas 0.9 0.999 \
--weight_decay 0.05 \
--epochs 50 \
--dist_eval \
--test_num_segment 2 \
--test_num_crop 3 \
--eval
correspoding log:
Auto resume checkpoint:
Test: [ 0/126] eta: 0:12:04 loss: 4.2886 (4.2886) acc1: 0.0000 (0.0000) acc5: 25.0000 (25.0000) time: 5.7482 data: 5.2494 max mem: 2425
Test: [ 10/126] eta: 0:01:40 loss: 4.9653 (4.9090) acc1: 0.0000 (5.6818) acc5: 12.5000 (21.5909) time: 0.8651 data: 0.4904 max mem: 2425
Test: [ 20/126] eta: 0:01:08 loss: 4.9653 (4.9404) acc1: 0.0000 (6.5476) acc5: 12.5000 (18.4524) time: 0.3888 data: 0.0075 max mem: 2425
Test: [ 30/126] eta: 0:00:52 loss: 4.9902 (4.9069) acc1: 0.0000 (7.2581) acc5: 25.0000 (20.5645) time: 0.3750 data: 0.0005 max mem: 2425
Test: [ 40/126] eta: 0:00:42 loss: 4.8552 (4.8680) acc1: 0.0000 (7.9268) acc5: 25.0000 (19.5122) time: 0.3402 data: 0.0005 max mem: 2425
Test: [ 50/126] eta: 0:00:35 loss: 4.7310 (4.8424) acc1: 0.0000 (7.8431) acc5: 12.5000 (21.3235) time: 0.3252 data: 0.0004 max mem: 2425
Test: [ 60/126] eta: 0:00:28 loss: 4.8939 (4.8792) acc1: 0.0000 (7.3770) acc5: 12.5000 (20.4918) time: 0.3202 data: 0.0005 max mem: 2425
Test: [ 70/126] eta: 0:00:23 loss: 4.8939 (4.8704) acc1: 0.0000 (7.0423) acc5: 12.5000 (20.9507) time: 0.3240 data: 0.0005 max mem: 2425
Test: [ 80/126] eta: 0:00:18 loss: 4.9370 (4.8875) acc1: 0.0000 (7.0988) acc5: 12.5000 (20.3704) time: 0.3198 data: 0.0003 max mem: 2425
Test: [ 90/126] eta: 0:00:14 loss: 4.9902 (4.8779) acc1: 0.0000 (7.4176) acc5: 25.0000 (20.7418) time: 0.3080 data: 0.0002 max mem: 2425
Test: [100/126] eta: 0:00:10 loss: 4.8552 (4.8711) acc1: 0.0000 (7.5495) acc5: 25.0000 (20.5446) time: 0.3047 data: 0.0002 max mem: 2425
Test: [110/126] eta: 0:00:06 loss: 4.7596 (4.8756) acc1: 0.0000 (7.0946) acc5: 12.5000 (20.3829) time: 0.3058 data: 0.0002 max mem: 2425
Test: [120/126] eta: 0:00:02 loss: 4.8679 (4.8649) acc1: 0.0000 (7.5413) acc5: 12.5000 (20.8678) time: 0.3048 data: 0.0002 max mem: 2425
Test: [125/126] eta: 0:00:00 loss: 4.8939 (4.8648) acc1: 0.0000 (7.3413) acc5: 12.5000 (20.8333) time: 0.3049 data: 0.0002 max mem: 2425
Test: Total time: 0:00:47 (0.3735 s / it)
* Acc@1 8.408 Acc@5 21.156 loss 4.799
Start merging results...
Reading individual output files
Computing final results
1344
Accuracy of the network on the 8064 test videos: Top-1: 9.30%, Top-5: 21.80%
yztongzhan commented
binbinjiang0505 commented
@an1018 请问您现在解决了这个问题吗,我在想是不是调小了batch_size,需要调整学习率呢