1.9 KiB
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