Comments (8)
WMSC is the validation data for the experiments reported in the paper tables 3 and 6. To replicate what we have achieved, I would suggest starting with KpConv (since we have provided the simple instruction to run it) and making sure everything works fine there, then moving on to RandLA and SCF-net.
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Hi @meidachen
OK. Thanks. I will run KPConv at first, and then move to RandLA-Net and SCF-Net. In addition, I'm not clear that STPLS3D dataset is only splited into training and validation, right? Not training, validation, and testing. During training, the WMSC is used for evaluate to obtain the best model, and then WMSC used as to test the model?
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That is correct, in the paper we have tested the trained model in another dataset (FDc) which cannot be released. So on the released dataset, you can either validate and test on WMSC, or you could do cross-validation using all four real-world datasets.
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Hi @meidachen
So on the released dataset, you can either validate and test on WMSC. This operation is similar to the S3DIS with 5 area as testing. But I think it is not reasonable as using the 5 area as validation and testing at the same time. Actually, validation and testing should have no intersection. Or, if not using WMSC as validation, only as testing, 3 or 5 times average is also OK.
Thanks.
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You are right, it is better to have validation and testing sets without intersection, and yes, I was following S3DIS (testing on aera5) when releasing STPLS3D. One of the main reasons that we can't really do a train, validation, and test split is the lack of real-world data.
Two ways of doing it would be:
- use say OCCC or RA as the validation, then we lost one scene to train the model.
- use part of say USC or OCCC or RA as validation, then the model could be overfitted since the validation and training sets do have an intersection in the sense that they came from the same area and may share similar properties.
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Or, if not using WMSC as validation, only as testing, 3 or 5 times average is also OK.
In this case, I think cross-validation would be a better option.
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An error occurs when the two sets of data have inconsistent category labels during model fine-tuning using RandLA. Can you post the fine-tuning code? Thanks!
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Hi @volare1996 ,
Which two sets of data are you using?
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Related Issues (20)
- Evaluation script for the competition of instance segmentation HOT 3
- SoftGroup for STPLS3D HOT 7
- HAIS one point assigned to multiple instance proposals HOT 4
- about OA_from_confusions in kpconv HOT 1
- How to use the evaluation script for the competition of instance segmentation?
- How to inference in the test set of stpls3d dataset? HOT 2
- mAP (Intersection) calculation in the evaluation script HOT 3
- instance label 0 mean HOT 13
- I want to make my own dataset HOT 6
- Scores in the evaluation script HOT 4
- training with randla HOT 11
- Pre-trained models and dataset generation HOT 6
- Can I use the synthetic data generation pipeline to create my dataset? HOT 1
- Data format of STPLS3D txt point clouds for Instance Segmentation HOT 1
- Error while set up the environment for Instance Segmentation in Ubuntu 20.04 HOT 2
- Interested in the projection code HOT 2
- KPConv-STPLS3D- Data Problem HOT 2
- multi gpu HOT 2
- Download failed in Google Drive HOT 1
- A Problem about HAIS Model in Stpls3d Dataset
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