Epoch-The epoch number associated with the results in the row.This table provides the following fields regarding the results obtained for each class code and epoch: Once the training is finished, a CSV table with a name that is the same as the Output Model Name parameter value with a suffix of _stats.csv is created and added to the checkpoint folder. checkpoints and is created in the same location. The name of this folder is the same as the Output Model Location parameter value with a suffix of. If the training data uses different class codes to represent the same objects in the pretrained model, remap the training data's class codes accordingly.Ī folder is created to store the checkpoint models, which are models that are created at each epoch. For this reason, the training data must have the same attributes and class codes as the pretrained model. When a pretrained model is specified, the model that will be trained will adopt the weights from the pretrained model and fine-tune the weights to classify the same objects in the pretrained model. Learn more about training a point cloud classification model If the CPU is used for training, provide the smallest possible training sample to estimate the time it will take to process the data prior to performing the training operation. Use the CPU only if no available GPU is present. Using the GPU is typically faster than using the CPU. To maximize the GPU memory for training, use a graphics card that will not be used for training to drive the computer's display. If the selected GPU is also being used for the display, its available memory will be decreased by the operating system and any other applications using the display at the time. If multiple GPUs are present and the tool does not use the fastest card, you can specify the GPU using the GPU ID environment setting. The tool will attempt to use the fastest CUDA-capable graphics card on the computer. The point classification model can be trained using either a CUDA-capable NVIDIA graphics card or the CPU. To set up your machine to use deep learning frameworks in ArcGIS Pro, see Install deep learning frameworks for ArcGIS. This tool uses the PointCNN implementation using deep learning frameworks.

Point cloud classification