Model Description
Keras Implementation of Point cloud classification with PointNet
This repo contains the trained model of Point cloud classification with PointNet.
The full credit goes to: David Griffiths
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
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