Comments (2)
You will need to take a subset of the training data as a validation set (I've seen numbers range from 5-10%) to verify your model. Ideally, the performance on the validation set is reflective of the performance of the test set, but from my experience, because of KITTI's dataset size, there is usually a large gap between validation performance and test performance.
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thank you
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Related Issues (20)
- MeanOrientationVFE HOT 3
- The results on the KITTI val. HOT 2
- About the accuracy of PDV on Kitti test sets HOT 2
- About full validation results on the uploaded PDV weights HOT 1
- RuntimeError: The expanded size of the tensor (8) must match the existing size (10) at non-singleton dimension 1. Target sizes: [128, 8]. Tensor sizes: [128, 10] HOT 1
- About training pdv on Waymo Open Dataset
- question about kde implementation.
- FPS
- About the cyclist on KITTI test set HOT 1
- The referenced results of PVRCNN(++) HOT 3
- ImportError: cannot import name 'VoxelGenerator' from 'spconv.utils' HOT 1
- undefined symbol: _ZNK3c104Type14isSubtypeOfExtESt10shared_ptrIS0_EPSo
- GLEW could not be initialized: Missing GL version HOT 1
- What should I do if I want to train this model on my own dataset? HOT 2
- codes running issues HOT 6
- what is the role of MAX_NUM_BOXES? HOT 1
- Train question HOT 2
- Pointcloud range and voxel size HOT 1
- ERRORS
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