Comments (2)
However, I visualize the result. I find that it looks not bad. So I wonder if there are some mistakes of the metrics.
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Hi. The accuracy is computed on the reduced test here, for which there are no ground truth available, hence the strange results of accuracy. So this is normal!
What do you mean "I use epochs 100"? On sema3d you should use at least 400 epochs.
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Related Issues (20)
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation HOT 5
- cuda acceleration for partition HOT 4
- TypeError in 'NearestNeighbors(1, algorithm='kd_tree').fit(xyz)' HOT 2
- Inconsistent class_maps for s3dis HOT 2
- CUDA error when training for Semantic3D HOT 1
- the version of metrics HOT 2
- When making ply_c, fatal error: numpy/ndarrayobject.h: No such file or directory HOT 3
- How to control the number of superpoints in a room? HOT 6
- Segmentation fault (core dumped) HOT 2
- Running on Stanford3dDataset_v1.2_Aligned_Version, the error occurs. HOT 6
- CMake error HOT 1
- Which version of Pytorch is needed for this code? HOT 1
- ModuleNotFoundError: No module named 'torchnet' HOT 3
- RuntimeError: scan failed to synchronize: an illegal memory access was encountered HOT 2
- L0-cut pursuit partition algorithm HOT 3
- cupy_backends.cuda.api.driver.CUDADriverError: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered HOT 3
- About the number of superpoints HOT 2
- Overfitting soon after around 30 epochs HOT 13
- How to visualize SSP HOT 1
- ValueError: need at least one array to concatenate HOT 1
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