Training process (distill)

[NikitaVasilevN]

Dear authors, thank you for your work.
I try to reproduce your results on dataset Scanner. I train my model on A40 (46 Gb GPU) with different number of scences (1000, 500, 10) and default parametrs in config ours_openseg.yaml, but can't do it due to I don't have enough memory. Can you say how many memory have you used to train your model on Scannet at least? Or what should I do to run distill process on the whole dataset?

Also how did you run test mode and preprocess scannet test dataset?

Thanks in advance

[oneHFR]

Hi, I encountered a similar issue.

Initially, I also encountered issues with distill on Scannet when using an RTX 3090 (24 GB), where the process would get killed. After switching to an L20 with 48 GB, I was able to run it smoothly. I monitored the GPU memory usage in real-time, and at certain points, it required close to 45 GB. So, it’s likely that memory is the main limitation here.

Hope this helps!

[ahme0307]

@oneHFR @pengsongyou I reproduced a 39.0 mIoU on ScanNet (20 classes) using the "ours_openseg.yaml" configuration file. The paper reports 46.0 mIoU for 3D Distill (Table 4). I'm unable to replicate their reported result. Did you face the same issue?