├── demo_pic_and_video
├── ROAD_Waymo_Baseline
├── Road-waymo-dataset
├── runs (Save model's weight)
│ ├── action
│ ├── detect
│ └── location
├── Track1
├── Track2
├── utils
conda create --name ROADpp python>=3.10
conda activate ROADpp
pip install -r requirement.txt
├── runs (Save model's weight)
│ ├── action
│ ├── detect
│ └── location
You need to first understand how to configure the YAML file for YOLOv8, as well as the dataset format.
cd ROADpp_challenge_ICCV2023
python detect.py --video_path 'xxx' --yolo_path 'xxx' --two_branch True --major_path 'xxx' rare_path 'xxx' --pkl_name 'xxx' --save_res 'xxx'
-
mode: Detect mode, only accepts Track1 or Track2. -
video_path: Path to the video. -
yolo_path: Path to the YOLO model. -
two_branch: Indicates whether to use two-branch YOLO. -
major_path: Path to the major YOLO model. -
rare_path: Path to the rare YOLO model. -
devices: GPU number. -
imgsz: YOLO input size. -
video_shape: Original video resolution. -
submit_shape: Final submit shape. -
pkl_name: Submit file name (*.pkl). -
save_res: Save submit file. -
action_detector_path: Path to the action detector model (Track2 only). -
loc_detector_path: Path to the location detector model (Track2 only). -
t2_input_shape: Track 2 input shape. -
windows_size: Sliding windows shape.
- Convert Datasets to YOLO format()
- Train YOLOv8 on Track1(train_YOLOv8.py)
- implement Tracklet Function
- Track2 Pipeline
- Two branch Yolo Pipeline
- Implement linear interpolation bbox function
- Complete quick start guide(config using YMAL file)
- Fix T2 interpolation bug
- Two branch T2