Transformer Tracking (CVPR2021)

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Deep LearningTransT
Overview

TransT - Transformer Tracking [CVPR2021]

Official implementation of the TransT (CVPR2021) , including training code and trained models.

We are revising the paper and will upload it in the next week

Results

Model LaSOT
AUC (%)
TrackingNet
AUC (%)
GOT-10k
AO (%)
OTB100
AUC (%)
NFS
AUC (%)
UAV123
AUC (%)
Speed
Params
TransT-N2 64.2 80.9 69.9 69.3 65.4 66.0 65.6fps 16.7M
TransT-N4 64.9 81.4 72.3 69.0 65.3 68.1 47.3fps 23.0M

Installation

This document contains detailed instructions for installing the necessary dependencied for TransT. The instructions have been tested on Ubuntu 18.04 system.

Install dependencies

  • Create and activate a conda environment

    conda create -n transt python=3.7
    conda activate transt
  • Install PyTorch

    conda install -c pytorch pytorch=1.5 torchvision=0.6.1 cudatoolkit=10.2
  • Install other packages

    conda install matplotlib pandas tqdm
    pip install opencv-python tb-nightly visdom scikit-image tikzplotlib gdown
    conda install cython scipy
    pip install pycocotools jpeg4py
    pip install wget yacs
    pip install shapely==1.6.4.post2
  • Setup the environment
    Create the default environment setting files.

    # Change directory to <PATH_of_TransT>
    cd TransT
    
    # Environment settings for pytracking. Saved at pytracking/evaluation/local.py
    python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"
    
    # Environment settings for ltr. Saved at ltr/admin/local.py
    python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

You can modify these files to set the paths to datasets, results paths etc.

  • Add the project path to environment variables
    Open ~/.bashrc, and add the following line to the end. Note to change <path_of_TransT> to your real path.
    export PYTHONPATH=<path_of_TransT>:$PYTHONPATH
    
  • Download the pre-trained networks
    Download the network for TransT and put it in the directory set by "network_path" in "pytracking/evaluation/local.py". By default, it is set to pytracking/networks.

Quick Start

Traning

  • Modify local.py to set the paths to datasets, results paths etc.
  • Runing the following commands to train the TransT. You can customize some parameters by modifying transt.py
    conda activate transt
    cd TransT/ltr
    python run_training.py transt transt

Evaluation

  • We integrated PySOT for evaluation.

    You need to specify the path of the model and dataset in the test.py.

    net_path = '/path_to_model' #Absolute path of the model
    dataset_root= '/path_to_datasets' #Absolute path of the datasets

    Then run the following commands.

    conda activate TransT
    cd TransT
    python -u pysot_toolkit/test.py --dataset <name of dataset> --name 'transt' #test tracker #test tracker
    python pysot_toolkit/eval.py --tracker_path results/ --dataset <name of dataset> --num 1 --tracker_prefix 'transt' #eval tracker

    The testing results will in the current directory(results/dataset/transt/)

  • You can also use pytracking to test and evaluate tracker. The results might be slightly different with PySOT due to the slight difference in implementation (pytracking saves results as integers, pysot toolkit saves the results as decimals).

Acknowledgement

This is a modified version of the python framework PyTracking based on Pytorch, also borrowing from PySOT and detr. We would like to thank their authors for providing great frameworks and toolkits.

Contact

  • Xin Chen (email:[email protected])

    Feel free to contact me if you have additional questions.

Owner
chenxin
Master Student of Dalian University of Technology
chenxin
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