Hello, thanks for your great work!

Can you provide a config.py file for the training on PASCAL VOC dataset.

Thanks

Hi, 我最近在研究label assign，实验发现使用默认设置下的AutoAssign在VOC上的表现会低于GFL 3个点以上的AP。请问你们有在VOC上尝试过吗？我使用的是mmdet官方repo中的AutoAssign，最大epoch为4，学习率下降在第3 epoch之后，这是mmdet VOC的标准设置。GFL可取得51.8的AP，但AutoAssign只能取得48.4。请问有什么超参会对性能影响较大？

I downloaded the pretrained model provided. During inference I'm getting the following warning. It results worse AP ~29. Also, could you upload the other models with different backbones that you share on the paper?

WARNING [02/12 13:37:59 c2.checkpoint.checkpoint]: 'backbone.top_block.p6.weight' has shape (256, 2048, 3, 3) in the checkpoint but (256, 256, 3, 3) in the model! Skipped.
[02/12 13:37:59 c2.checkpoint.checkpoint]: Some model parameters are not in the checkpoint:
backbone.top_block.p6.weight

I used AutoAssign to train my data set, but the speed of training was too slow.Is that normal?

Thank you for your outstanding work!
I recently ran this code and got a result of mAP40.2, Is this the normal fluctuation range ?

Thank you for your excellent work. I have a question, what does "loss_norm" mean, this seems not mentioned in the original paper

Hi, thanks for your nice work.

I'm extremely interesting in the ImpObj branch in your paper, but you code is based on cvpods base,
so how could I debug code ？

Thanks ！