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Object classification on ModelNet40

Data

We consider our experiment folder is located at XXXX/Experiments/KPConv-PyTorch. And we use a common Data folder loacated at XXXX/Data. Therefore the relative path to the Data folder is ../../Data.

Regularly sampled clouds from ModelNet40 dataset can be downloaded here (1.6 GB). Uncompress the data and move it inside the folder ../../Data/ModelNet40.

N.B. If you want to place your data anywhere else, you just have to change the variable self.path of ModelNet40Dataset class (here).

Training a model

Simply run the following script to start the training:

    python3 training_ModelNet40.py

This file contains a configuration subclass ModelNet40Config, inherited from the general configuration class Config defined in utils/config.py. The value of every parameter can be modified in the subclass. The first run of this script will precompute structures for the dataset which might take some time.

Plot a logged training

When you start a new training, it is saved in a results folder. A dated log folder will be created, containing many information including loss values, validation metrics, model checkpoints, etc.

In plot_convergence.py, you will find detailed comments explaining how to choose which training log you want to plot. Follow them and then run the script :

    python3 plot_convergence.py

Test the trained model

The test script is the same for all models (segmentation or classification). In test_any_model.py, you will find detailed comments explaining how to choose which logged trained model you want to test. Follow them and then run the script :

    python3 test_any_model.py