Skip to content

Latest commit

 

History

History
24 lines (23 loc) · 1.67 KB

GETTING_STARTED.md

File metadata and controls

24 lines (23 loc) · 1.67 KB
Raw

Currently we provide the dataloader of KITTI dataset, and the NuScenes dataset is on the way.

Training & Testing & Evaluation

Training by python with multiple GPUs in a machine

Run following command to train model with ResNet-18 backbone.

python ./src/main.py --data_dir ./kitti_format --exp_id RTS3D --batch_size 12 --master_batch_size 6 --lr 1.25e-4 --gpus 0,1 --num_epochs 200

Generate monocular 3D object detection results from KM3D or download them from KM3D-val-results or KM3D-val-multi-class-results

Results generation

Run following command for results generation.

python ./src/demo.py --demo ./kitti_format/data/kitti/val.txt --data_dir ./kitti_format --calib_dir ./kitti_format/data/kitti/calib/ --load_model ./kitti_format/exp/RTS3D/model_last.pth --gpus 0 --mono_path ./kitti_format/data/kitti/mono_results/

Visualization

Run following command for visualization.

python ./src/demo.py --vis --demo ./kitti_format/data/kitti/val.txt --data_dir ./kitti_format --calib_dir ./kitti_format/data/kitti/calib/ --load_model ./kitti_format/exp/RTS3D/model_last.pth --gpus 0 --mono_path ./kitti_format/data/kitti/mono_results/

Evaluation

Run following command for evaluation.

python ./src/tools/kitti-object-eval-python/evaluate.py evaluate --label_path=./kitti_format/data/kitti/label/ --label_split_file ./ImageSets/val.txt --current_class=0,1,2 --coco=False --result_path=./kitti_format/exp/results/data/