Awesome

SCANet

Our manuscript is available at arxiv

Project Paths Setup

You can also modify paths by editing these two files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

Data Preparation

Put the tracking datasets in ./data. It should look like:

${PROJECT_ROOT}
  -- data
      -- LaSOT
          |-- ...
      -- TrackingNet
          |-- ...
      -- COCO
          |-- ...
      -- GOT10K
          |-- ...
      -- SARDet
          |-- ...
          ...

Training

Download SOT (OSTrack with SOT pretrained model) pretrained weights and put them under $PROJECT_ROOT$/pretrained_models.

python tracking/train.py --script scanet --config baseline --save_dir ./output --mode multiple --env_num 1 --nproc_per_node 2 --use_wandb 0

env_num doesn't need to be considered, it can be set to any number. if you want to train in different devices, you can consider it.

if you want to use env_num, go to lib/train/admin/local.py and lib/test/evaluation/local.py to set different device's num

Evaluation

Download checkpoint and raw results and put it under $PROJECT_ROOT$/output.

python tracking/test_multi.py 
python tracking/eval.py

Acknowledgments

Our project is developed upon TBSI and OSTrack. Thanks for their contributions which help us to quickly implement our ideas.