Hi JUWON SEO,
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The NaN values in the mini-batch loss can occur for a variety of reasons. Getting rid of them may require some debugging in your SSD object detection. You may try these suggestions to troubleshoot and address the issue:
- Data Preprocessing: Ensure that your LiDAR point cloud data and ground truth labels are properly pre-processed before passing to the `trainSSDObjectDetector` function. Check for any missing or NaN values in the input data.
- Labeling and Ground Truth: Verify the correctness of your ground truth labels and ensure they align properly with the LiDAR point cloud data. The labeling process for LiDAR-based object detection may differ from traditional image-based labeling.
- Learning Rate and Training Options: Experiment with different learning rates and training options. NaN values in the loss could be related to inappropriate learning rates or other training parameters.
- NaN in Gradients: Monitor gradients during training. NaN values in gradients can sometimes lead to NaN in the loss. Check this by enabling gradient checking or printing gradient values during training.
You may refer to the following documentation for further reading:
I hope this helps!
