CSTNet for RGB-T Tracking

Our manuscript is available at arxiv

Environment Installation

prepare your environment as TBSI.

Notice: Our use pytorch version is 1.13.0

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
      -- lasher
          |-- trainingset
          |-- testingset
          |-- trainingsetList.txt
          |-- testingsetList.txt
          ...

Training

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

python tracking/train.py --script cstnet --config baseline --save_dir ./output --mode multiple --env_num 5 --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

Our tensorboard is released tensorboard/

Our training log is released at cstnet-baseline.log. Although iou name of training log is 'giou', we use wiou loss function. See lib/train/train_script.py and lib/train/actor/cstnet_actor.py

Evaluation

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

python tracking/test.py cstnet baseline --dataset_name lasher --threads 4 --num_gpus 1

Download lasher raw result and put it under $PROJECT_ROOT$/output.

python tracking/analysis_results.py

Acknowledgments

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

LiYunfengLYF / CSTNet