Your implementation of domain adaptive faster rcnn performs better than paper values, what might be the reason?

[TADanton]

Hi Zhiqiang,

I tried to run your re-implementation of domain adaptive faster rcnn in adaptation from pascal voc to clipart, and I found it reached much better mAP (30 mAP) than the value reported in paper "stong weak distribution alignment" paper. (19.8 mAP)

May I ask what's your opinion on this? Why is your re-implementation much better? Or did I probably ignore some details in training script and misuse them? (I used your trainval_net_dfrcnn.py, and didn't use any rendered datasets).

Thanks a lot for any possible help in advance!

Best,
Anton

[harsh-99]

Hey,
It should not be the case. There must be some bug in the your code leading to this issue.