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HuguesTHOMAS 2020-04-27 18:55:39 -04:00
parent e9d328135c
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13
doc/object_classification_guide.md
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@ -3,9 +3,16 @@
### Data ### Data
Regularly sampled clouds from ModelNet40 dataset can be downloaded <a href="https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip">here (1.6 GB)</a>. Uncompress the folder and move it to `Data/ModelNet40/modelnet40_normal_resampled`. 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
<a href="https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip">here (1.6 GB)</a>.
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](https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/e9d328135c0a3818ee0cf1bb5bb63434ce15c22e/datasets/ModelNet40.py#L113)).
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 ### Training a model
@ -17,7 +24,7 @@ This file contains a configuration subclass `ModelNet40Config`, inherited from t
### Plot a logged training ### 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. 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 : 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 :

2
doc/object_segmentation_guide.md
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@ -15,7 +15,7 @@ Similarly to ModelNet40 training, the parameters can be modified in a configurat
### Plot a logged training ### 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. 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 : 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 :

2
doc/scene_segmentation_guide.md
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@ -44,7 +44,7 @@ Incoming
### Plot a logged training ### 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. 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 : 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 :