Question about consistency loss!!! about casd

[Cynthiacoding]

Hello! I'm also a learner about CSAD, but I have not successfully executed it limited the GPU source.
Could you please share your log? Thanks！
By the way, I sent you an email. Looking forward to your reply!

[suilin0432]

Hello! I'm also a learner about CSAD, but I have not successfully executed it limited the GPU source.
Could you please share your log? Thanks！
By the way, I sent you an email. Looking forward to your reply!

You should use GPU with large memory, such as V100, 3090 or M40.
I think I have replied you, maybe you should check your spam.

[Justinhzy]

Hi,
Thanks for your question.
Our method is built upon MELM paper. It contains some methods introduced by MELM that we may not mention in our paper. You can find those details in MELM repo (https://github.com/vasgaowei/pytorch_MELM).

About the consistency loss, did you change any hyper-parameters or random seed? The result should like this
VOC07 metric? Yes
AP for aeroplane = 0.7051
AP for bicycle = 0.7011
AP for bird = 0.5704
AP for boat = 0.4575
AP for bottle = 0.2945
AP for bus = 0.7453
AP for car = 0.7277
AP for cat = 0.7144
AP for chair = 0.2531
AP for cow = 0.6763
AP for diningtable = 0.4930
AP for dog = 0.6474
AP for horse = 0.6577
AP for motorbike = 0.7268
AP for person = 0.2372
AP for pottedplant = 0.2586
AP for sheep = 0.5631
AP for sofa = 0.6077
AP for train = 0.6536
AP for tvmonitor = 0.6650
Mean AP = 0.5678

Results:
0.705
0.701
0.570
0.457
0.294
0.745
0.728
0.714
0.253
0.676
0.493
0.647
0.658
0.727
0.237
0.259
0.563
0.608
0.654
0.665
0.568

[suilin0432]

Fine~.
But I didn't change any thing, maybe this codebase is not fully consistent with your own code which could achieve the result stated in your paper. By the way it's also strange that adding the reg branch could only boost the performace less than 1 AP.
But anyway, the result reported in your parper is really good, could you please give me the checkpoint of COCO which could reach 13.9.