DEMO.md

Here we provide a quick demo to test a pretrained model on the custom data and visualize the predicted results.

We suppose you already followed the INSTALL.md to install the KM3D repo successfully.

1. Download the provided pretrained models as shown in the README.md, and put set pretrained models in ./demo_kitti_format/exp/KM3D/
2. Run the KM3D with a pretrained model (e.g. ResNet-18train.pth) and kitti camera data as follows:

       cd km3d
       python ./src/faster.py --vis --demo ./demo_kitti_format/data/kitti/image --calib_dir ./demo_kitti_format/data/kitti/calib/ --load_model ./demo_kitti_format/exp/KM3D/pretrained.pth --gpus 0 --arch res_18
   
3. Run the RTM3D(GRM) with a pretrained model (e.g. ResNet-18train.pth) and kitti camera data as follows:

       cd km3d
       python ./src/demo.py --vis --demo ./demo_kitti_format/data/kitti/image --calib_dir ./demo_kitti_format/data/kitti/calib/ --load_model ./demo_kitti_format/exp/KM3D/pretrained.pth --gpus 0 --arch res_18
   
4. Run the KM3D with a pretrained model (e.g. ResNet-18train.pth) and custom camera data as follows:

       cd km3d
       python ./src/demo.py --vis --demo ~/your image folder --calib_dir ~/your calib folder/ --load_model ~/pretrained.pth --gpus 0 --arch res_18 or dla_34
   
5. Run the RTM3D(GRM) with a pretrained model (e.g. ResNet-18train.pth) and custom camera data as follows:

       cd km3d
       python ./src/demo.py --vis --demo ~/your image folder --calib_dir ~/your calib folder/ --load_model ~/pretrained.pth --gpus 0 --arch res_18 or dla_34