39 lines
1.9 KiB
Markdown
39 lines
1.9 KiB
Markdown
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## Object classification on ModelNet40
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### Data
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We consider our experiment folder is located at `XXXX/Experiments/KPConv-PyTorch`. And we use a common Data folder
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loacated at `XXXX/Data`. Therefore the relative path to the Data folder is `./Data`.
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Regularly sampled clouds from ModelNet40 dataset can be downloaded
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<a href="https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip">here (1.6 GB)</a>.
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Uncompress the data and move it inside the folder `./Data/ModelNet40`.
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N.B. If you want to place your data anywhere else, you just have to change the variable
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`self.path` of `ModelNet40Dataset` class ([here](https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/e9d328135c0a3818ee0cf1bb5bb63434ce15c22e/datasets/ModelNet40.py#L113)).
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### Training a model
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Simply run the following script to start the training:
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python3 training_ModelNet40.py
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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.
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### Plot a logged training
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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.
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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 :
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python3 plot_convergence.py
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### Test the trained model
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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 :
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python3 test_any_model.py
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