| .. _training: | |
| Training models | |
| =============== | |
| .. _training_pointcloud_classification: | |
| Traning point cloud classification | |
| ---------------------------------- | |
| Collect point cloud dataset as describe in :ref:`3d_dataset_collection`, and | |
| preprocess them as explained in :ref:`3d_dataset_preprocessing`. | |
| To train the models, you need to clone `this repo <https://github.com/mhwasil/pointcloud_classification>`_, and | |
| install the requirements to train the model. | |
| Training deep learning model | |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| The supported models are available under `config/config.yaml`. | |
| .. code-block:: bash | |
| #python trainer.py --model <model_name> --train | |
| #an example for training DGCNN with color (DGCNNC) | |
| python trainer.py --model DGCNNC --train | |
| Adding new model | |
| ^^^^^^^^^^^^^^^^ | |
| ToDo | |
| .. _training_2d_object_detection: | |
| Training 2D object detection | |
| ---------------------------- | |
| ToDo | |
| .. note:: | |
| You can train the model on `H-BRS Scientific Computing Cluster <https://wr0.wr.inf.h-brs.de/wr/index.html>`_, | |
| provided that you have access to it. The tutorial on how to submit job and train | |
| your model on the cluster can be found `here <https://github.com/mhwasil/pointcloud_classification/blob/master/hbrs_cluster_usage.md>`_. |