2.1 KiB
Scene Segmentation on S3DIS
Data
S3DIS dataset can be downloaded here (4.8 GB). Download the file named Stanford3dDataset_v1.2.zip, uncompress the folder and move it to Data/S3DIS/Stanford3dDataset_v1.2.
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.
Scene Segmentation on Scannet
Incoming
Scene Segmentation on Semantic3D
Data
Semantic3D dataset can be found here. Download and unzip every point cloud as ascii files and place them in a folder called Data/Semantic3D/original_data. You also have to download and unzip the groundthruth labels as ascii files in the same folder
Training
Simply run the following script to start the training:
python3 training_Semantic3D.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called Semantic3DConfig, and the first run of this script might take some time to precompute dataset structures.
Scene Segmentation on NPM3D
Incoming
Plot and test trained models
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