wehub-resource-sync
1.1 KiB
1.1 KiB
Point Transformer
This model is implemented on August 27, 2021 when there is no official code released.
Thus we implemented this model based on the code from https://github.com/qq456cvb/Point-Transformers.
This is a reproduction of the paper: Point Transformer.
Performance
| Task | Dataset | Metric | Score - Paper | Score - DGL (Adam) | Score - DGL (SGD) | Time(s) - DGL |
|---|---|---|---|---|---|---|
| Classification | ModelNet40 | Accuracy | 93.7 | 92.0 | 91.5 | 117.0 |
| Part Segmentation | ShapeNet | mIoU | 86.6 | 84.3 | 85.1 | 260.0 |
- Time(s) are the average training time per epoch, measured on EC2 p3.8xlarge instance w/ Tesla V100 GPU.
How to Run
For point cloud classification, run with
python train_cls.py --opt [sgd/adam]
For point cloud part-segmentation, run with
python train_partseg.py --opt [sgd/adam]