An Efficient, Flexible, and General deep learning framework that retains minimal. Users can use EFG to explore any research topics following project templates.
- 2023.08.22 Code release of ICCV2023 paper: TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses.
- 2023.04.13 Support COCO Panoptic Segmentation with Mask2Former.
- 2023.03.30 Support Pytorch 2.0.
- 2023.03.21 Code release of CVPR2023 Highlight paper: ConQueR: Query Contrast Voxel-DETR for 3D Object Detection.
- 2023.03.21 Code release of EFG codebase, with support for 2D object detection (MS COCO dataset) and 3D object detection (Waymo and nuScenes dataset).
- gcc 5 (c++11 or newer)
- python >= 3.6
- cuda >= 10.1
- pytorch >= 1.6
# spconv spconv_cu11{X} (set X according to your cuda version) # waymo_open_dataset ## python 3.6 waymo-open-dataset-tf-2-1-0==1.2.0 ## python 3.7, 3.8 waymo-open-dataset-tf-2-4-0==1.3.1 git clone https://github.com/poodarchu/EFG.git cd EFG pip install -v -e . # set logging path to save model checkpoints, training logs, etc. echo "export EFG_CACHE_DIR=/path/to/your/logs/dir" >> ~/.bashrc# download waymo dataset v1.2.0 (or v1.3.2, etc) gsutil -m cp -r \ "gs://waymo_open_dataset_v_1_2_0_individual_files/testing" \ "gs://waymo_open_dataset_v_1_2_0_individual_files/training" \ "gs://waymo_open_dataset_v_1_2_0_individual_files/validation" \ . # extract frames from tfrecord to pkl CUDA_VISIBLE_DEVICES=-1 python cli/data_preparation/waymo/waymo_converter.py --record_path "/path/to/waymo/training/*.tfrecord" --root_path "/path/to/waymo/train/" CUDA_VISIBLE_DEVICES=-1 python cli/data_preparation/waymo/waymo_converter.py --record_path "/path/to/waymo/validation/*.tfrecord" --root_path "/path/to/waymo/val/" # create softlink to datasets cd /path/to/EFG/datasets; ln -s /path/to/waymo/dataset/root waymo; cd .. # create data summary and gt database from extracted frames python cli/data_preparation/waymo/create_data.py --root-path datasets/waymo --split train --nsweeps 1 python cli/data_preparation/waymo/create_data.py --root-path datasets/waymo --split val --nsweeps 1 # nuScenes dataset/nuscenes ├── can_bus ├── lidarseg ├── maps ├── occupancy │ ├── annotations.json │ └── gts ├── panoptic ├── samples ├── sweeps ├── v1.0-mini ├── v1.0-test └── v1.0-trainval # create softlink to datasets cd /path/to/EFG/datasets; ln -s /path/to/nuscenes/dataset/root nuscenes; cd .. # suppose that here we use nuScenes/samples images, put gts and annotations.json under nuScenes/occupancy python cli/data_preparation/nuscenes/create_data.py --root-path datasets/nuscenes --version v1.0-trainval --nsweeps 11 --occ --seg# cd playground/path/to/experiment/directory efg_run --num-gpus x # default 1 efg_run --num-gpus x task [train | val | test] efg_run --num-gpus x --resume efg_run --num-gpus x dataloader.num_workers 0 # dynamically change options in config.yamlModels will be evaluated automatically at the end of training. Or,
efg_run --num-gpus x task valAll models are trained and evaluated on 8 x NVIDIA A100 GPUs.
| Methods | Frames | Schedule | VEHICLE | PEDESTRIAN | CYCLIST |
|---|---|---|---|---|---|
| CenterPoint | 1 | 36 | 66.9/66.4 | 68.2/62.9 | 69.0/67.9 |
| CenterPoint | 4 | 36 | 70.0/69.5 | 72.8/69.7 | 72.6/71.8 |
| Voxel-DETR | 1 | 6 | 67.6/67.1 | 69.5/63.0 | 69.0/67.8 |
| ConQueR | 1 | 6 | 68.7/68.2 | 70.9/64.7 | 71.4/70.1 |
| Methods | Schedule | mAP | NDS | Logs |
|---|---|---|---|---|
| CenterPoint | 20 | 59.0 | 66.7 |
EFG is currently in a relatively preliminary stage, and we still have a lot of work to do, if you are interested in contributing, you can email me at poodarchu@gmail.com.
@article{chen2023trajectoryformer, title={TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses}, author={Chen, Xuesong and Shi, Shaoshuai and Zhang, Chao and Zhu, Benjin and Wang, Qiang and Cheung, Ka Chun and See, Simon and Li, Hongsheng}, journal={arXiv preprint arXiv:2306.05888}, year={2023} } @inproceedings{zhu2023conquer, title={Conquer: Query contrast voxel-detr for 3d object detection}, author={Zhu, Benjin and Wang, Zhe and Shi, Shaoshuai and Xu, Hang and Hong, Lanqing and Li, Hongsheng}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={9296--9305}, year={2023} } @misc{zhu2023efg, title={EFG: An Efficient, Flexible, and General deep learning framework that retains minimal}, author={EFG Contributors}, howpublished = {\url{https://github.com/poodarchu/efg}}, year={2023} }