Closed jwfanDL closed 2 years ago
Hi @JwFbupt! Thanks for your question! Please provide your command here, so I could help you.
rlaunch --cpu=48 --gpu=8 --memory=$((1024*140)) --max-wait-time=12h --charged-group=v_event -- python -m torch.distributed.launch --master_port 12347 --nproc_per_node=8 \ main.py somethingv2 RGB --arch resnet50 --num_segments 8 --gd 20 --lr 0.01 \ --lr_scheduler step --lr_steps 30 45 55 --epochs 60 --batch-size 8 \ --wd 5e-4 --dropout 0.5 --consensus_type=avg --eval-freq=1 -j 4 --npb
Hi @JwFbupt! The command is right. Could you share the training log? For your convenience, we provide the training log and tensorboard file for TDN-R50-Segment-8 on SSV2. We hope these could be helpful for you.
Hi @JwFbupt! Could you share your training log? Or, have you addressed this issue now? I will close this issue if you have no reply in two weeks.
Hi @yztongzhan I'm sorry that I did not reply you in time. It is still a problem for us. Here is the training log log.txt TDN__somethingv2_RGB_resnet50_avg_segment8_e60.zip
It seems no problem in the training log. Could you reproduce the results of other method like TSM and TEA on your SSV2?
Besides, I want to know if you modify the validation code by yourself?
I have reproduced TSM on somethingv2, and obtained promising results. In this version, we did not modify any codes. Thank you for your reply, and I will try to solve the problem.
hi @yztongzhan, we have reproduce the results of TEA as well as TSM on sthv2. I would like to know, whether the official model use mixup method or flip on sthv2
We use flip aug for SSV2 training, which also support in MMaction2 for such TSM. But we want to point out that our label generation script
is different from MMaction2, you need to check our label carefully.
I hope these could be useful for you.
Hi~ I have trained a TDN(ResNet50+8frame) on something-v1 as well as something-v2. However, there exists some gaps between the results we do and the reported one. On something-v2, best Prec@1 is only 61.567. We follow the command and process that you mentioned in README.md. I wonder that maybe some hyper-parameters settings in our experiments is not the same as your experiments.