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