SOGDet
Main Results
| Method | mAP | NDS | Model |
|---|---|---|---|
| SOGDet-BO-r50 | 38.2 | 50.2 | |
| SOGDet-SE-r50* | 38.8 | 50.6 | |
| SOGDet-BO-r101 | 43.9 | 55.4 | |
| SOGDet-SE-r101* | 45.8 | 56.6 |
- Memory is tested in the training process with batch 1 and without using torch.checkpoint.
Environment Installation
Please see install.md.
Data Preparation
Please see install.md.
Train
For training process, we use config file in $SOGDet/configs/sogdet to define model, dataset and hyber parameters.
Run the following command to start a training process.
For example:
tools/dist_train.sh configs/sogdet-se-r50.py 8 Test
For testing bbox scores, run the following command:
tools/dist_test.sh configs/sogdet-se-r50.py sogdet-se-r50.pth 8 --eval bbox
For testing bbox & mIOU scores, run the following command:
tools/dist_test_plus.sh configs/sogdet-se-r50.py sogdet-se-r50.pth 8 --eval bbox
Visualization
For visualizing results, we should add parameter '--show-dir xxx' to save results during running test commands. For example:
tools/dist_test.sh configs/sogdet-se-r50.py sogdet-se-r50.pth 8 --eval bbox --show-dir work_dirs/sogdet-se-r50/ # for OD results
python tools/test_plus.py configs/sogdet-se-r50.py sogdet-se-r50.pth --eval bbox --show-dir work_dirs/sogdet-se-r50/ # for OCC results (no multi-process)Run the following command to visualize OD & OCC visualization results.
python tools/vis_pred_gt_od_hybrid.py --bbox-path work_dirs/sogdet-se-r50/ --voxel-path work_dirs/sogdet-se-r50/ --save-path work_dirs/sogdet-se-r50/visual_pred/
python tools/vis_pred_gt_occ.py --voxel-path work_dirs/sogdet-se-r50/ --save-path work_dirs/sogdet-se-r50/
Acknowledgement
This project is not possible without multiple great open-sourced code bases. We list some notable examples below.