Comments (1)
I just uploaded the code in prepare_dataset/stanford/data_efficient_v2.py
.
For fair comparisons, I recommend using our generated point ids.
A quick note, we have tried generating different point ids w.r.t different label ratios and the results do not change too much.
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Related Issues (13)
- merge_sort: failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered terminate called after throwing an instance of 'c10::Error' HOT 11
- How to test? HOT 5
- How to visualise the results after testing?
- Cannot reproduce the results
- Need some help!
- Cannot reproduce the results. HOT 16
- Question about the requirement of GPU HOT 4
- Question about the training script for the 0.02% (S3DIS) HOT 7
- question about multi-GPUs training HOT 2
- Dose your code only can be train on MinkowskiEngine 0.4.3 Env now HOT 7
- What type of data is percentage0.001evenc? HOT 2
- can you provide 0.02% S3DIS experiment scripts? HOT 1
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