3D object classification on ScanObjectNN

Dataset

There are three ways to download the data:

Download from the official website.
Or, one can download the data by this command (please submit the "ScanObjectNN Terms of Use" form on their official website before downloading):
```
mkdir -p data/ScanObjectNN
cd data/ScanObjectNN
wget http://hkust-vgd.ust.hk/scanobjectnn/h5_files.zip
```
Or, one can only download the hardest variant by the following link. Please cite their paper[1] if you use the link to download the data
```
mkdir data
cd data
gdown https://drive.google.com/uc?id=1iM3mhMJ_N0x5pytcP831l3ZFwbLmbwzi
tar -xvf ScanObjectNN.tar
```

Organize the dataset as follows:

data
 |--- ScanObjectNN
            |--- h5_files
                    |--- main_split
                            |--- training_objectdataset_augmentedrot_scale75.h5
                            |--- test_objectdataset_augmentedrot_scale75.h5

Train

For example, train PointNext-S

CUDA_VISIBLE_DEVICES=0 python examples/classification/main.py --cfg cfgs/scanobjectnn/pointnext-s.yaml

change the cfg file to use any other model, e.g. cfgs/scanobjectnn/pointnet++.yaml for training PointNet++

Test

CUDA_VISIBLE_DEVICES=0 python examples/classification/main.py --cfg cfgs/scanobjectnn/pointnext-s.yaml  mode=test --pretrained_path pretrained/scanobjectnn/pointnext-s/pointnext-s_best.pth

* change the cfg file to use any other model, e.g. cfgs/scanobjectnn/pointnet++.yaml for testing PointNet++

Profile parameters, FLOPs, and Throughput

CUDA_VISIBLE_DEVICES=0 python examples/profile.py --cfg cfgs/scanobjectnn/pointnext-s.yaml batch_size=128 num_points=1024 timing=True flops=True

note: 1. set --cfg to cfgs/scanobjectnn to profile all models under the folder. 2. you have to install the latest version of DeepSpeed from source to get a correct measurement of FLOPs

Reference

@inproceedings{uy-scanobjectnn-iccv19,
      title = {Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data},
      author = {Mikaela Angelina Uy and Quang-Hieu Pham and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
      booktitle = ICCV,
      year = {2019}
  }