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Do you have any idea why oneformer3d is very sensitive to different backbones? #49
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One thing is you need to carefully follow the pre-processing of point clouds in our repo and your new backbones (e.g. PTv3). Like color normalization, voxel size or elastic transform. These things should also be changed. Please share your results if got something interesting with these backbones :) |
Hmmm, I am not very familiar with elastic transform. Is this transform different for different backbones? Also, isn't this transform only applied during training? So even if it is slightly different, it shouldn't have such a big impact on evaluation? |
Not sure, but it is rather strong augmentation. If the backbone is not trained with it, this can possible break the pre-training weights. I recommend to completely follow the preprocessing and augmentations of the new backbone. |
Hi, authors. Actually, I have tried this based on Pointcept from scratch. |
Hi @RayYoh , |
Hi, what does +4 mean? For AP50 result? |
I think smth like +4 mAP50 (may be less). If you use some backbone from pointcept e.g. ptv3, you can also start with their pre-trained weights. I think it should help much. |
Unfortunately not many tricks, just the one about the loss weight from our readme and multiple train runs... |
Dear authors, I'm wondering whether the pretrain of backbone plays an importance effect in the performance of instance segmentation? |
Yes, when running our code without pre-trained checkpoint, the mAP for instance segmentation is about 4% worse. |
I tried changing the backbone from SpConv to supposedly more powerful new backbones like PTv3 and Swin3D without changing any other parameters but they both give much poorer results on S3DIS. You seem to be using different backbones for different tasks too which suggests that this framework might be sensitive to different backbones used. Do you have any idea why this is the case?
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