## Object classification on ModelNet40 ### Data Regularly sampled clouds from ModelNet40 dataset can be downloaded here (1.6 GB). Uncompress the folder and move it to `Data/ModelNet40/modelnet40_normal_resampled`. N.B. If you want to place your data anywhere else, you just have to change the variable `self.path` of `ModelNet40Dataset` class (in the file `datasets/ModelNet40.py`). ### 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 snapshots, 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