Closed ardarslan closed 12 months ago
Hi, I need to reproduce your results to continue my thesis, I would really appreciate any reply. Thank you!
Hi, sorry for the late reply. I was just busy with something else.
Finally, we use EgoVLP+InternVideo+SlowFast+Omnivore features.
And I have three comments:
Hope it helps.
Hi, thank you for your reply, and no problem! The scores I reported above were from using the checkpoint from the 15th epoch. I also tried using the 11th and the 12th epochs.
15th: {"Recall@1x,tIoU=0.5": 43.15983417779825, "average_mAP": 26.092527253173575} 11th: {"Recall@1x,tIoU=0.5": 45.50898203592814, "average_mAP": 27.106595524719427} 12th {"Recall@1x,tIoU=0.5": 44.58774758175956, "average_mAP": 26.669235981716433}
I used all the features in these three submissions. I would be very glad if you could share the other configs and infer_ensemble.py.
Hi again, I was wondering if there is any update on this issue. Thanks!
Sorry for the late reply. I have updated it. You can use infer_ensemble.py to do ensembling. The expected ensembling average_mAP is around 29.
No problem, thanks a lot for all your help!
Hello,
In your paper, you mentioned that your final submission for the MQ Task on eval.ai is produced by ensembling predictions from three models. Could you please provide the config files for these three models? I am also confused about the combination of the features I should use. Is it EgoVLP+InternVideo or EgoVLP+InternVideo+SlowFast+Omnivore?
Edit: When I followed the instructions under "Train on MQ (Train + val)" and "Submission (to Leaderboard test-set server)" in the README file, I got the following scores on eval.ai for the test split:
{"Recall@1x,tIoU=0.5": 43.15983417779825, "average_mAP": 26.092527253173575}