a pytorch implimentation of Dynamic Graph CNN(EdgeConv)
I impliment the classfication network in the paper, and only the vanilla version. DGCNN(Dynamic Graph CNN) is based on the architecture of PointNet to do a point cloud classification task or a segmentation task.
To train the model, just set the path of you ModelNet40 dataset(you can download it from here) in dataset.py.
Run: python dataset.py, without any error printed. If does, please check the path of your dataset.
If you want to change the hyper-parameters of the model, you can modify params.py yourserf.
Then, just simply runing: python train.py, and it will start training. The training procedure would be saved in a directory called summary , and the model weights would be saved in a directory called weights.
The classification accuracy on test set is 91.2% on ModelNet40 dataset. And my training result is here:
| hyper-parameters | accuracy |
|---|---|
| Dynamic Graph CNN(base, K=10) | 89.47% |
| Dynamic Graph CNN(base, K=20) | 89.55% |
| Dynamic Graph CNN(base, K=30) | 91.00% |
| Dynamic Graph CNN(base, K=40) | 91.13% |
| Dynamic Graph CNN(base, K=50) | 89.99% |