Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

Attention: please create a new virtual environment for OpenMMLab 2.0.

I wish to run Colar on online scenario with only RGB input (without flow input). How can I reproduce the result using the code provided here? and do you have pre-trained weight for it?

Thank you for your excellent work! I would like to ask a question which is how can we get the exemplars of all categories, it seems like not be implemented in the codes. Thank you!

I trained your model on my device with THUMOS dataset without any modification, but the mAP is only 65.39. Since there is a gap between this result and the validation result (66.91) of the model you posted, I wonder if you are using some tricks. If not, can it be understood that the gap is only due to equipment differences.

[Epoch-7] [IDU-kinetics] mAP: 0.6539
BaseballPitch: 0.4485
BasketballDunk: 0.8277
Billiards: 0.2734
CleanAndJerk: 0.7331
CliffDiving: 0.8972
CricketBowling: 0.4617
CricketShot: 0.3120
Diving: 0.8743
FrisbeeCatch: 0.4104
GolfSwing: 0.7853
HammerThrow: 0.8585
HighJump: 0.7666
JavelinThrow: 0.7920
LongJump: 0.8093
PoleVault: 0.9041
Shotput: 0.6848
SoccerPenalty: 0.4923
TennisSwing: 0.6288
ThrowDiscus: 0.6688
VolleyballSpiking: 0.4487

Thanks for sharing the code. Would you please provide the features of two other benchmarks or give some guidance to get the features.

Hi，I am searching the code of the paper ”Structured Attention Composition for Temporal Action Localization“ ，and the url provided by arXiv is linked to this project. But it seems no relation between the code and the paper.

Hi,
Thank you for sharing the code. When I was reading your papers, I met some problems~
In Tabel 4, I found that you write that given a one-minute video, the running time of extracting the RGB feature is 2.3 seconds; I wonder how you calculate the time? If it was the time cost by extracting feature that used Res-200?

Hi @VividLe,

I have a honda driving dataset that has images (3fps) and I would like to use only the RGB features or visual features of the honda dataset. How would it be possible?
I couldn't find the optical flow feature and am not sure how to get the dataset.
My task is to reconstruct one modality from the other one using a share multimodal representation (sensor and videos(images frames)

Thanks

Excuse me, what is the extraction code of Baidu Cloud?