1.7 KiB
Scene Segmentation on S3DIS
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
.
S3DIS dataset can be downloaded here (4.8 GB).
Download the file named Stanford3dDataset_v1.2.zip
, uncompress the data and move it to ../../Data/S3DIS
.
N.B. If you want to place your data anywhere else, you just have to change the variable
self.path
of S3DISDataset
class (here).
Training
Simply run the following script to start the training:
python3 training_S3DIS.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called S3DISConfig
, and the first run of this script might take some time to precompute dataset structures.
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