| library_name: tf-keras | |
| license: apache-2.0 | |
| tags: | |
| - image-classification | |
| - image-segmentation | |
| ## Model Description | |
| ### Keras Implementation of Point cloud classification with PointNet | |
| This repo contains the trained model of [Point cloud classification with PointNet](https://keras.io/examples/vision/pointnet/). | |
| The full credit goes to: [David Griffiths](https://dgriffiths3.github.io/) | |
| ## Intended uses & limitations | |
| - As stated in the paper, PointNet is 3D perception model, applying deep learning to point clouds for object classification and scene semantic segmentation. | |
| - PointNet takes raw point cloud data as input, which is typically collected from either a lidar or radar sensor. | |
| ## Training and evaluation data | |
| - The dataset used for training is ModelNet10, the smaller 10 class version of the ModelNet40 dataset. | |
| ## Training procedure | |
| ### Training hyperparameter | |
| The following hyperparameters were used during training: | |
| - optimizer: 'adam' | |
| - loss: 'sparse_categorical_crossentropy' | |
| - epochs: 20 | |
| - batch_size: 32 | |
| - learning_rate: 0.001 | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |